Approximate incidence of adverse effect at different radiation exposures measured in Rads.
\r\n\tThis necessitated a need to understand control theoretical concepts and system analysis in a discrete time domain, which gave rise to the area of discrete time control systems. This has helped control engineers and designers to theoretically ascertain the possibilities and limitations of a control system design implemented in a digital framework, whereas continuous time designs suffer from the essential mismatch in the nature of the underlying independent time variable in theoretical studies and practical implementation. Also, many practical systems are inherently discrete time in nature, sensors and transducers sample data only at fixed time intervals, and computers calculate the control input only in some finite time.
\r\n\tTraditionally, fundamental concepts of discrete time control systems are derived from the continuous time counterpart upon time discretization of the latter and subsequent formal analysis. This gave rise to discrete time counterparts of system models and controllers in z-domain as well as in state space form. However, discrete time control system design and analysis matured as a discipline in itself with the advent of optimal and adaptive techniques solely based on discrete time approach. Robust nonlinear discrete time controllers were also developed utilizing the ideas of sliding modes, model predictive control, etc.
\r\n\tThe techniques for parameter estimation and system identification are largely dominated by discrete time methods. Well-established Kalman filter and extended Kalman filters are developed in discrete time. Many discrete time stochastic filters are utilized in control systems to reduce the impact of noise and disturbance during practical implementation.
\r\n\tDespite the developments in discrete time control designs and their usefulness in control system implementation, there are a few challenges like discretization effect on systems stability, communication loss, etc. which are also areas of serious research. With all its usefulness and limitations, discrete time control systems have found vast areas of application from process control and automation, robotics, network control systems and internet of things, control of networks and multi-agent systems, etc.
\r\n\tThis book intends to provide the reader with an overview of detailed control system design methodologies in discrete time which are well-established in literature. Emerging areas of interest in discrete time systems catering to new and existing challenges are also welcomed.
The present work originates from the authors’ earlier work in the field of digital games with emphasis on the impact of game play, in general, and on game-based learning, in particular [1, 2, 3, 4, 5]. The original approach has been generalized, and algorithms have been extended toward business applications far beyond the limits of gaming [6]. Essentials have been carried over to the study of scenarios of data analysis, visualization, and exploration [7, 8].
\nMotivated by questions for the impact of playing digital games, the authors analyzed game play represented as (sets of) sequences of actions. Seen from the application point of view, the task in focus is player modeling or, more generally, user modeling. Seen from the data point of view, it is string mining. Seen from the viewpoint of algorithms deployed, the task is pattern inference.
\nTo achieve a high expressiveness, the authors prefer logical terminology powerful enough to approximately represent human goals, intentions, preferences, desires, fears, and the like. Seen this way, the task is theory induction, and the method is hypotheses refutation [9, 10].
\nNo doubt, digitalization pervades nearly every sphere of life. Humans are facing more and more digital systems at their workplaces, in everyday education, in their spare time, and in health care. With the US Food and Drug Administration’s approval of aripiprazole tablets with sensors in November 2017 [11], the digitalization reaches the inside of the human body.
\nFrequently, the process of digitalization is placing on humans the burden of learning about new digital systems and how to use them appropriately. More digital systems do not necessarily ease the human life. To use them effectively, users need to become acquainted with software tools, have to understand the interfaces, and have to learn how to wield the tools. “A tool is something that does not do anything by itself unless a user is wielding it appropriately. Tools are valuable for numerous simple tasks and in cases in which a human knows precisely how to operate the tool. Those tools have their limitations as soon as dynamics come into play. There are various sources of dynamics, such as a changing world or human users with different wishes, desires, and needs” (see [12], p. xii). As the present authors put it earlier, the digitalization process “bears abundant evidence of the need for a paradigmatic shift from digital tools to intelligent assistant systems” (see [7], p. 28).
\nThinking about human assistance, the most helpful assistants are those who have own ideas, go their own ways, and—from time to time—surprise us with unexpected outcomes. This does apply to digital assistant systems as well.
\nApproaches to intelligent system assistance are manifold (e.g., see [13, 14] and the references therein including the authors’ contributions [15, 16]).
\nDigital assistants are programmed to behave differently in different conditions such as varying environmental or infrastructure contexts and varying human users with different prior knowledge, preferences, skills, needs, desires, fears, and the like. To adapt accordingly, assistant systems need to learn from the data available. In a sense, a digital assistant system has “to ask itself,” so to speak, how to learn what the user needs from sparse information such as mouse clicks or wisps over the screen.
\nSeen in its right perspective, digital assistant systems are facing problems of learning from incomplete information sometimes called inductive inference [17]. Digital assistant systems are necessarily learning systems.
\nThe purpose of the system’s learning is understanding the context of interaction to adapt to. In this chapter, the authors confine themselves to understanding the human user.
\nConventionally, this is called user modeling naming a rather comprehensive field of studies and applications (see, e.g., [18, 19, 20, 21, 22] or any of the earlier UMAP conference proceedings).
\nBy way of illustration, [23] provides a comprehensive digital game case study of mining large amounts of human-computer interaction data—in fact, data of game playing behavior—for the purpose of classification according to psychologically based personality traits [24].
\nThis exemplifies a particular way of user modeling by means of HCI data mining.
\nAlready for decades, the misconception of data mining as digging for golden nuggets is spooking through the topical literature [25, 26]. Some authors believe that data mining means somehow squeezing out insights from the given data and put this opinion in words such as “visualization exploration is the process of extracting insight from data via interaction with visual depictions of that data” (see [27], p. 357).
\nInstead, data mining is a creative process of model formation based on incomplete information (see [7], p. 108). In brevity, data mining is inductive modeling.
\nThe details of the inductive modeling process depend substantially on the data, on the goal of modeling, of the algorithmic technologies in use, and on the underlying model concept including the syntax of representing models [28, 29]. For going into the details of the model concept, [30] is of particular interest.
\nIn the thesis [30, 31] is taken as a basis for a systematic framework of data mining design in which the model concept resides in the center. An outer frame, so to speak, consists of the application domain, the methods of model construction, and the methods of model use. Every concrete model depends (a) on the context of modeling, (b) on communities of practice, (c) on the purpose of modeling, and, possibly, (d) on models generated earlier (see [31], p. 37). This covers Fayyad’s KDD process [32] and the CRISP data mining model [33].
\nWith respect to the difficulty of learning from incomplete information, process models of data mining allow for cyclic processing as shown in Figure 1. Consequently, data mining has to be seen as a process over time that does not result in an alone model, but in an unforeseeably long sequence of subsequently generated hypothetical models. This does perfectly resemble the learning systems perspective of [17].
\nFayyad’s KDD process according to [32] vs. the CRISP data mining model as in [33].
In the authors’ opinion, the thinking about emerging sequences of hypotheses is badly underestimated in contemporary data mining investigations. Pondering model concepts is not sufficient. We need to put emphasis on the investigation of suitable spaces of model hypotheses.
\nThroughout the rest of this chapter, spaces of hypotheses will be spaces of logical theories. The concept theory of mind is adopted and adapted from behavioral research in animals [34]. There is much evidence that certain animals reflect about intentions and behaviors of other animals [35, 36]. Birds of the species Aphelocoma californica—the western scrub jay, esp. the California scrub jay—are food-caching. They do not only cache food but also colorful and shiny objects such as plastic toys. In case such a bird, let us name it A, is caching food or other treasures, and if it is watched by another bird of its species, we name it B, then A returns shortly after to unearth the treasures cached before. The interpretation is, loosely speaking, that the bird A thinks about the possibly malicious thoughts of the bird B. It builds its own theory of mind. More generally speaking, thinking about another one’s thoughts means to build a theory of mind.
\nThe authors aim at digital assistant systems able “to understand their human users” by hypothesizing theories of mind. Anthropomorphically speaking, digital assistant systems shall be enabled “to think about their human user’s thoughts.” The cornerstone has been laid in [1, 2]. Case studies as in [4, 5, 37] demonstrate that this is possible.
\nFor this purpose, user models are seen as theories—just formalizations of theories of mind—such that human user modeling becomes theory induction. The conceptual approach is called theory of mind modeling and induction [1, 2] based on human-computer interaction data.
\nWhat the system “knows” about its human user comes from an analysis of interaction data. [3] describes a study based on a commercial digital game. When playing the game, players may learn about pieces of legerdemain. They play successfully when being able to script the necessary steps for doing conjuring tricks. Patterns of game playing behavior reveal the human players’ success or failure. Instances of those patterns are shown in recorded game play. It is the system’s task to learn patterns from their instances. This approach is generalized toward theory induction.
\nThe concept of a pattern in science dates back to work by Alexander in architecture [38, 39]. Angluin redefines the pattern concept for purposes of formal language investigations and, most importantly, provides algorithmic concepts for learning patterns from instances [40]. Exactly this is what an assistant needs to do when collecting sequences of interaction data.
\nIn game studies such as [3, 5], interaction is abstractly represented by finite sequences over an alphabet A that contains identifiers of all possible activities of all engaged agents such as human players, non-player characters (NPCs), and other computerized components. A+ denotes the set of all those strings, and given any particular game G, Π(G) is the subset of all strings that can occur according to the rules and mechanics of G. Angluin’s pattern concept describes string properties of a certain type. If instances ω1, …, ωn ∈ Π(G) occur, the learning task consists in finding a pattern p that holds in all the observed strings. In a sense, p is a theory with {ω1, …, ωn} ⊨ p, where ⊨ denotes the logical consequence operator.
\nThe authors generalize the before-mentioned approach toward human-computer interaction in general beyond the limits of game play as undertaken in [6, 7].
\nThe validity of the logical expression {ω1, …, ωn} ⊨ p means consistency of the hypothesis p with the set of observations Ωn = {ω1, …, ωn} it is built upon. In conditions more general than pattern inference according to [40], the choice of the logic is decisive to consistency. In the conventional case, the question for consistency Ωn ⊨ p is recursively decidable but NP-hard. Concerning the background of computability and complexity, the authors rely on [41, 42]. For the moment, let us assume any suitable logic. Details will be discussed as soon as they become interesting.
\nA closer look at conventional data mining process models as in Figure 1 reveals that original data appear somehow static. Both models on display show data represented by a drum icon. An emergence over time is beyond the limits of conventional perspectives. In contrast, human-computer interaction data emerge over time [3, 5, 7]. This leads to the learning task of processing sequences Ω1 ⊆ Ω2 ⊆ … ⊆ Ωn ⊆ … of growing finite data sets of observations. When learning patterns according to [39], the learner returns hypotheses p1, p2, …, pn, … such that every hypothesis pn is consistent with the underlying data set Ωn.
\nConsistence is a critical requirement and may be refined by approximations in different ways. In learning theory, it is known that algorithms that are allowed to temporarily return inconsistent hypotheses are of higher effectiveness [17, 43, 44]. The authors refrain from a detailed discussion of these effects, for reasons of space.
\nExtending the abovementioned approach, one arrives at an understanding of mining HCI data as the induction of theories over emerging sequences Ω1 ⊆ Ω2 ⊆ … ⊆ Ωn ⊆ … of data. The result is a corresponding sequence of logical theories T1, T2, …, Tn, … which, if possible, should converge to an ultimate explanation T of the observed human-computer interaction, i.e., Ωn ⊨ T for all sets of observations.
\nBy way of illustration, [5] is based on a case study in which the sequence of theories begins with some default T0 and consists of 35 subsequent hypothetical theories. In this sequence, subsequent theories remain unchanged frequently. There are only nine changes of hypotheses. The final theory reasonably explains the overall human user’s behavior.
\nNote that there are varying other approaches to deal with dynamics such as [45, 46]. The authors, however, stick to the logical approach for its declarativity and expressiveness. Different from other approaches, they dovetail logical reasoning and inductive inference [17, 43]. In this way, logics and recursion theory are underpinning data mining on HCI data.
\nThe logical background of the authors’ approach includes reasoning about changes in time. This leads directly to temporal logics that are around for already more than half a century [47, 48]. In these good old days, time was tense, but in conditions of digitalization, time became digital as well [49]. It was already known before that this makes a difference [50].
\nIn the simple digital game case study [4, 5], it is sufficient to choose the Hilbert-style logic K (see [49], Section 1.6).
\nWhich logic to choose depends on the particular domain of application. In particular, there is an indispensable need (i) to formalize background knowledge. The logic must allow for the representation of knowledge in such a way that it is easy (ii) to refute hypotheses [9, 10]. Below, we will come back to these two issues. Logics taken into account come from [49, 50, 51, 52, 53, 54, 55, 56]. For the generic approach discussed in this section, however, the choice is subordinate.
\nSpeaking about human-computer interaction with the intention of user modeling by theories of mind, the fundamental question is what to take into account. Interaction may be represented on largely varying levels of granularity [57] ranging from keystrokes and wisps over the screen through compound actions to activities on a task level (named quests in the world of digital games, where the approach originated). The authors are engaged in a joint project in which even the exact position of a document on the screen plays a role in mining HCI data and, thus, must be documented in interaction representations [58].
\nThe (finite) set of actions of interest is denoted by A. It is considered an alphabet. As usual in theoretical computer science, A* denotes the set of all finite strings over A including the empty string ε, A+ = A*\\{ε}. If it makes sense, one may restrict A+ to the set Π of only those sequences that can occur in practice, Π ⊆ A+. In conditions of strictly regulated interaction possibilities, Π is a formal language [59].
\nEvery string π ∈ Π abstractly represents some process of human-computer interaction such as a game play [3] or a session with a data analysis tool [7]. When trying “to understand the human user,” π is subject to investigation. Sometimes, there is a finite subset of Π available. By way of illustration, see Figure 2 adopted and adapted from ([3], p. 89, Figure 6.3).
\nExcerpts from recorded game play of 11 subjects striving to do a conjuring trick.
According to the game magazines worldwide, allegedly, the innovation of the commercial game studied in [3] consists in the unprecedented feature of players doing conjuring tricks. To investigate this in more detail, the alphabet A of actions contains player actions such as clicking to a magic head (denoted by mh in the strings on display in Figure 2), opening a grimoire, in the game called a magic book (mb), turning pages of the book when searching for an appropriate trick (tp), selecting a trick from the book (st), scripting the steps of the trick in preparation of performance (sc), and presenting the trick by means of a magic wand either successfully (mw) or not (mw-). The digital game system’s actions indicated by boxes ranging from yellowish to reddish in Figure 2 are the presentation of a magic head (mh) indicating that there is an opportunity of witchcraft, a comment (co) in response to a human user’s click to inform the player what to do, the opening of the magic book (mo) to allow for scripting tricks, and, in case the trick has been scripted correctly and the user has triggered its execution by clicking to the magic wand, a virtual execution (ex) of the trick by means of a cut scene and some response (re) to the player about the success of the performance.
\nThe (cutouts of) strings on display in Figure 2 have different properties that are indicators of the players’ mastery of game play, in general, and of scripting tricks, in particular [3]. For a precise and readable treatment, actions in A are written in brackets such as [mh] and [ex]. […] abbreviates an action not of interest. Using this convention, the cutout of π1 is [mh][cl][co][mb][tp][st][mo][sc][em][…][mh][cl][mo][sc][mw-][sc][mw-][sc][mw-]. Readers may easily recognize that the player has a problem. The substring [sc][mw-] indicates a failed effort of scripting and doing a conjuring trick. This will be discussed in some detail.
\nSuppose that ≼ denotes the substring relation. π1 ≼ π2 means that there are (possibly empty) strings π’ and π “satisfying π’π1π “= π2. In other words, π1 occurs somewhere in π2.
\nBy way of illustration, the following two sample formulas φ2 = [sc][mw-][sc][mw-] ≼ π and φ3 = [sc][mw-][sc][mw-][sc][mw-] ≼ π describe certain string properties. This justifies logical expressions such as π ⊨ φ2 and π ⊨ φ3 meaning that the string π satisfies the corresponding property. It is custom to say that π is a model of φ2 or φ3, respectively. The intuitive meaning is quite obvious. When φ3 occurs in a string π describing human game play, the player appears to stab around in the dark. According to Figure 2, it holds π1 ⊨ φ3, π5 ⊨ φ3, and π7 ⊨ φ3. Properties of this type are called patterns. Patterns according to Angluin [40] are properties of strings that are decidable. This does obviously apply to both φ2 and φ3 as well.
\nBecause the information about the other eight strings of game play in Figure 2 is incomplete, we are not sure whether or not one of the patterns φ2 and φ3 is satisfied. With respect to the information available, all we know is that we are not able to disprove one of these patterns. Needless to state that in computational logics, double negation cannot be removed [60, 61]. In other words, (¬¬p→p) is no valid axiom of (propositional) computational logics.
\nJantke [37, 62] has developed a family of games based on the Post correspondence problem (see [63], Section 2.6, pp. 88ff). Patterns that occur in game play are of higher complexity than those sketched above. Computational learning of these patterns—theory of mind induction—is possible but considerably more involved. As illustrated in [64], a computer program may even learn skills the human player is not aware of. The authors confine themselves to a sketch of the essentials of PCP games within the following four paragraphs.
\nA Post correspondence system (see [63], p. 89)—in PCP games, this is called a pool—is a finite set of pairs of strings that may be visualized as dominos. Some of these systems have solutions; others have not. Playing a PCP game means to incrementally modify a common pool according to some rules of play. The goal is to make a pool solvable and to prevent others from doing so. Who makes a pool solvable and declares victory accordingly wins the game by showing a solution. If the player’s demonstration fails, the game is lost.
\nInterestingly, the solvability of Post correspondence systems is algorithmically undecidable (for a comprehensive treatment of undecidability, [65] is recommended). As a consequence, a player might be unaware of being able to declare victory and to win the game accordingly. There is the phenomenon of missing a win. This may occur repeatedly.
\nUsing elementary formalizations (see [62, 64] for details), one may write down formulas φn of first-order predicate calculus saying that a player never misses more than n wins in a game. Whether or not ψn holds in recorded game play π is effectively undecidable. But the problem is effectively enumerable (some call it semi-decidable).
\nTherefore, a computer program can watch a human playing PCP games. It can analyze strings describing the human-computer interaction for the occurrence of missing wins. The program’s first hypothesis may be ψ0. In case a missing win is detected, the hypothesis is changed to ψ1. If ψn is hypothesized, but one more missing win is diagnosed, the hypothesis is changed to ψn + 1. The underlying process is identification by enumeration [66].
\nLet us have a look—quick and dirty—at the principle of identification by enumeration from a logical viewpoint. A space of hypotheses is an effective enumeration T0, T1, T2, T3, T4, T5, … of theories; in the paragraph before, these theories are the singleton sets {ψn}. When sets of observations Ω1 ⊆ Ω2 ⊆ Ω3 ⊆ Ω4 ⊆ Ω5 … come in subsequently, learning means to search the given enumeration of hypotheses for the first theory that does not contradict the current information. Formally, a learner L getting fed in Ωn searches for k = μm [¬(Ωn ⊭ Tk)] and hypothesizes L(Ωn) = Tk. The symbol μ represents the minimum operator [41].
\nAs explicated already much earlier [67], the key logical reasoning problem in learning from incomplete information is refutation. This is sound with related philosophical positions [11]. The crux is that ¬(Ωn ⊭ Tk) is usually undecidable as seen in the PCP game case study. This leads to the authors’ original pattern concept. Whereas in [3, 68]—adopted from [40]—the assumption is that the validity of a pattern in a stream of HCI data is decidable, the ultimate approach weakens the requirement (see [37], p. 12): Patterns are logical theories that are co-semi-decidable. In other words, under the assumption of an underlying logic with (i) its consequence operator ⊨, (ii) the operator’s implementation ⊢, (iii) background knowledge, and (iv) current observations, the implementation ⊢ may be used to find out in a uniform way whether any set of observations and any theory are inconsistent. Furthermore, according to scenarios of analyzing human experience of patterns in HCI data [69], patterns should have the property of locality. Informally, once a pattern instance occurred, it does not disappear throughout subsequent interaction. In formal terminology, for any pattern φ and for any π1, π2 ∈ A*, the validity of π1 ⊨ φ implies the validity of π1π2 ⊨ φ.
\nTo sum up, theory induction on HCI data is operationalized by construction of theories and sticking to them as long as they are not refuted. The underlying decisive knowledge forms an effectively enumerable space of hypotheses. In formal language learning, the appropriate technical term is called an indexed family of formal languages [70]. For the purpose of theory induction, this concept has been slightly generalized. The authors coined the term of an indexed family of logical formulas [5]. Because logic in general is more expressive than formal languages are, there is a need for requirements that are weaker but still sufficient to allow for inductive learning.
\nAssume any logic that does not exceed the expressive power of first-order predicate calculus to allow for a completeness theorem [71]. The logic brings with it its well-formed formulas, its consequence operator ⊨ and the operator’s implementation ⊢ (due to completeness). Practically, refutation completeness is sufficient [67]. By way of illustration, the authors’ recent application uses Horn logic and relies on the refutation completeness of Prolog [72].
\nGiven domain-specific background knowledge BK, an indexed family F of logical formulas is defined by the following conditions. F = {φn}n = 0,1,2,… such that the sequence of formulas φn is effectively enumerable. Furthermore, for any two indices m and n with m < n, the formula that occurs later in the enumeration does not imply the earlier one, i.e., BK ⊭ (φn→φm).
\nNote that the sequence of formulas {ψn}n = 0,1,2,… discussed in the context of PCP games above meets the conditions and, thus, is an example of an indexed family of logical formulas. The corresponding background knowledge comprises the rules of play including Peano arithmetic.
\nApparently, the authors’ approach is a two-stage process above the granularity of the more conventional processes depicted in Figure 1. First, one selects an effectively enumerable space of hypotheses. Second, one performs identification by enumeration as the key learning methodology. Other conventional steps such as data selection, data preparation, and data preprocessing occur as well [8]. However, the latter are not in focus of this chapter.
\nAs the choice of spaces of hypothetic models—an issue ignored in conventional approaches—is decisive, it is worth to take updates and revisions into account. The authors introduced a generalization for which they coined the term dynamic identification by enumeration [73].
\nIn contrast to earlier approaches that are widespread (see Figure 1, where in the CRISP-like model on the right, the “model” node is hatched, as it is missing in the original figure [33]), the authors stress the aspects illustrated by the (four groups of) darker boxes in Figure 3. First of all, data are not seen as a monolithic object within the process concept but as an emerging sequence. Second, whereas in the Fayyad process (see Figure [1] and the source [32]), the pattern concept appears from nowhere, the terminology of forming hypotheses is seen a central issue—the selection of a logic and the design of suitable spaces of hypotheses, both potentially subject to revision over time. Third, the inductive modeling procedure discussed in some more detail throughout this chapter is identification by enumeration.
\nHCI data mining approach with emphasis on aspects of inductive modeling.
Involved logical reasoning may easily become confusing—not so much to a computer or to a logic program [4], but to a human being. Within the digital game case study [4, 5], the generation of a single indexed family of logical formulas has been sufficient. Identification by enumeration works well for identifying even a bit perfidious human player intentions. Business applications as in [6] are more complex and may require unforeseeable revisions of the terminology in use, i.e., the dynamic generation of spaces of hypotheses on demand [73].
\nThe present section is aimed at a clarification of the core ideas and technicalities. For this purpose, the approach is stripped to the essentials. Recursion-theoretic inductive inference as in [17, 43, 44] is the most lucid area in which problems of inductive learning can be explicated without any need for dealing with syntactic sugar. The underlying apparatus of mathematics can be found in textbooks such as [41, 63].
\nIn Figure 4, the darker boxes with white inscriptions denote conventional concepts of recursion-theoretic inductive inference [44]. The other boxes reflect formalizations of this chapter’s core approaches to HCI data mining by means of identification by enumeration. The concepts derived from the present chapter’s practical investigations form a previously unknown infinite hierarchy between the previously known concepts NUM and TOTAL.
\nAbstractions of fundamental inductive learning concepts compared and related; ascending lines mean the proper set inclusion of the lower learning concept in the upper one.
Throughout the remaining part of this section, the authors confine themselves to only elementary concepts.
\nLearning logical theories is very much like learning recursive functions. Both have finite descriptions but determine a usually infinite amount of facts—the theorems of a theory and the values of a function, respectively. In both cases, the sets of facts are recursively enumerable but usually undecidable. The deep interplay of logic and recursion theory is well understood for almost a century and provides a firm basis of seminal results [74]. Inductively learning a recursive function means, in some sense, mining the function’s graph which is presented in growing chunks over time, a process very similar to mining HCI data.
\nA few notions and notations are inevitable. IN is the set of natural numbers. Pn denotes the class of n-ary partial recursive functions mapping from INn into IN. Rn ⊂ Pn is the subclass of all total recursive functions. Assume any ordering of IN written in the form X = {x0,x1,x2,…}. For any function f ∈ R1, the sequence of observations (x0,f(x0)), (x1,f(x1)), (x2,f(x2)), (x3,f(x3)), … provides growing but incomplete information about f. With respect to the ordering X, the amount of information up to the timepoint n is encoded in fX[n] = ((x0,f(x0)),…,(xn,f(xn))). If X0 is the standard ordering 0,1,2,3,4, …, the index is dropped such that the notation is f[n]. Throughout any learning process, hypotheses are natural numbers interpreted as programs according to some Gödel numbering φ. Because any two Gödel numberings are recursively isomorphic, the choice of a numbering does not matter. Learnability is transcendental.
\nAssume any class C ⊂ R1 of total recursive functions. The functions of C are uniformly learnable by an effectively computable learner L ∈ P1 on the ordering of information X0, if and only if the following conditions are satisfied. For all f ∈ C and for all n ∈ IN, the learner computes some hypothesis L(f[n]). For every f ∈ C, the sequence of hypotheses converges to some c that correctly describes f, i.e., φc = f. EX denotes the family of all function classes learnable as described. EX(L) ∈ EX is the class of all functions learnable by L. In the case arbitrary arrangements of information X are taken into account, the definition is changed by substituting fX[n] for f[n]. The class of functions learnable by L is named EXarb(L), and the family of all function classes learnable on arbitrary X is EXarb. The term EX is intended to resemble explanatory learning; this is exactly what theory of mind induction is aiming at.
\nThe equality of EX and EXarb is folklore in inductive inference. Therefore, arbitrary orderings are ignored whenever possible without loss of generality.
\nIntuitively, it seems desirable that a hypothesis reflects the information it is built upon. Formally, ∀m≤n (φh(xm) = f(xm)) where h abbreviates L(fX[n]). In the simpler case of X0, every xm equals m. The property is named consistency. The families of function classes uniformly learnable consistently are CONS and CONSarb, resp., and CONSarb ⊂ CONS ⊂ EX is folklore as well. Apparently, the message is that consistency is a nontrivial property.
\nConsistency may be easily guaranteed, (T) if all hypotheses are in R1 or (F) if it is decidable whether or not a hypothesis is finally correct. Adding (T) or (F) to the definition of EX and EXarb, one gets learning types denoted by TOTAL, TOTALarb, FIN, and FINarb, respectively. In inductive inference, FIN = FINarb ⊂ TOTAL = TOTALarb ⊂ CONSarb is folklore as well [44].
\nUnder the prior knowledge of FIN = FINarb, TOTAL = TOTALarb, and EX = EXarb (see [44]), all the abovementioned inclusions are on display in Figure 4.
\nNUM is the learning type defined by means of identification by enumeration as discussed in the previous section. A class C ⊂ R1 belongs to NUM, if and only if there exists a general recursive enumeration h with C ⊆ {φh(n)}n∈IN ⊂ R1. A partial recursive learning device L ∈ P1 learns via identification by enumeration on h, if and only if L(f[n]) = h(μm[φh(m)[n] = f[n]). Interestingly, this extremely simple concept reflects exactly the application in [4, 5].
\nThe potential of generalizing the learning principle of identification by enumeration is practically demonstrated in [6]. Accordingly, [73] introduces the novel concept of dynamic identification by enumeration. In terms of recursion theory, this looks as follows.
\nFor simplicity, the authors confine themselves to X0. Note that we adopt a few more notations. If h is an enumeration or, alternatively, if n is an index of the enumeration h, Ch and Cn denote the class of all functions enumerated by h. From Grieser [75], we adopt the notation [C] to denote all initial segments of functions in C, i.e., [C] = {f[n] | f ∈ C ∧ n ∈ IN}.
\nA class of functions C ⊆ R1 belongs to NUM*, if and only if there exists a computable generator function γ∈P1 such that for all f∈C, it holds (I) for all n∈IN that γ(f[n]) is defined, φγ(f[n]) ∈ R1, Cγ(f[n]) ⊆ R1, and f[n] ∈ [Cγ(f[n])] and (II) there is a critical point m∈IN such that for all n∈IN larger than m, it holds γ(f[m]) = γ(f[n]) and (III) f ∈ Cγ(f[m]).
\nThe criteria (I), (II), and (III) are practically motivated [6]. They are called operational appropriateness, conversational appropriateness, and semantic appropriateness, respectively. Usually, the change of γ(f[n]) to another γ(f[n + 1]) means an extension of terminology [6, 73]. The condition (II) of conversational appropriates prevents us from a Babylonian confusion.
\nAccording to [73], it holds NUM* = TOTAL. This proves the enormous gain of learning power by means of dynamic identification by enumeration. Whereas NUM is incomparable to FIN, NUM* is lying far above FIN; [44] provides much more information about the space between FIN and TOTAL.
\nIn this chapter, the authors are going much further by introducing a family {NUMk}k∈IN of infinitely many refinements of NUM*. A class C in NUM* belongs to NUM0, if and only if there exists some generator function γ that is constant and identical for all functions f of C. For a positive number k, a class C in NUM* belongs to NUMk, if and only if there exists a γ that, for every function f of C, does generate at most k different spaces of hypotheses γ(f[n]). Intuitively, γ suggest at most k times an extension of terminology for the purpose of more appropriately expressing hypotheses throughout the process of data analysis and learning.
\nJantke [76] provides a detailed discussion of benchmarks to prove that {NUMk}k∈IN forms an infinite hierarchy as on display in Figure 4. For brevity, just two benchmarks are presented. C1q-like = {f | f∈R1 ∧ ∀x∈IN (x > 0 → f(x) > 0) ∧ φf(0) = f}. Apparently, C1q-like ∈ NUM1 \\ NUM0. Ck+1q-like = {f | f∈R1 ∧ ∃g∈ Ckq-like ∧ ∃n∈IN (f(n) = 0 ∧ ∀x∈IN (x > n → f(x) > 0) ∧ ∀x∈IN (x < n → f(x) = g(x)) ∧ ∀x∈IN (x > n → f(x)=φf(n + 1)(x-n-1))}. This allows to separate NUMk+1 from NUMk.
\nBy the end of Section 3, the authors have summarized their HCI data mining approach and visualized essentials of inductive modeling in Figure 3. We take up the thread once again. The selection or the design of a terminology is essential. The terminology determines the space of hypothetical models that may be found. Throughout the process of data mining, model spaces may be subject to revision repeatedly (see preceding Section 4).
\nThe world of models is overwhelmingly rich. Models may be characterized by properties, by purpose, by function, by model viability, or by model fitness [30]. As Thalheim puts it, “models are developed within a theory” ([30], p. 117).
\nEvery concrete application domain provides such an underlying theory. It is a necessary precondition to data mining to specify all the aspects of the underlying theory that should be taken into account (see [30], p. 115, for mapping, truncation, distortion, and the like). Revisions may turn out to be necessary, when inductive modeling, i.e., learning proceeds. Therefore, the word “data understanding” in the CRISP model (see Figure 1) is considered inappropriate and, hence, substituted by “data analysis” in the approach shown in Figure 3. This figure is intended to visualize both the dynamics of the data and of the model spaces. Hypothetical data understanding is seen as the preliminary result of data mining.
\nWhen speaking about logics and its algorithmic use, it is strictly advisable to stay within the limits of first-order predicate calculus [71]. The selection or the design of a logic means to decide about the signature of the language and about axiom sets of background knowledge.
\nUnder the assumption of a given logic, business understanding and data analysis underpin an impression of what the current analysis process is about. To say it more practically, what might be typical statements arrived at by the end of the data mining process? In the authors’ digital game case study, by way of illustration, typical statements explain a human player’s action under conditions of a play state [4, 5]. In their business intelligence application [6, 7, 8], formulas relate business data and temporal information of largely varying granularity. As soon as the type of expected formulas becomes clear, the next design task is to specify an indexed family of logical formulas. This forms the first space of hypothetical models.
\nWithin the authors’ framework, a crucial step is the modification of a space of hypotheses. There are heuristics discussed in [8] that shall be briefly surveyed. An automation may require, to some extent, natural language processing.
\nThe human user’s activities are syntactically analyzed. In case there occur terms that have no corresponding sort, constant, function, or predicate names in the formulas of the current space of hypotheses, a limitation of the terminology is detected. The system is “unable to speak about what the user is doing.” A case discussed in [8], p. 234, is “retracement of business volume.” Retracement is interpreted as inequality with a (large) factor in it, and some sequence of such formulas of properly increasing strength is automatically generated.
\nMethodologies, guidelines, and process models aiming at (logical) model space construction are worth much more future research work and practical exploration.
\nThe authors’ present approach to mining human-computer interaction data works well in applications that provide larger amounts of data [3, 4, 5, 6]. The novel dynamic approach to the generation of model spaces exceeds the power of preceding approaches significantly [6, 73].
\nHowever successful in the cited prototypical applications, the approach may fail under conditions of small amounts of data. Consequently, it seems inappropriate to applications such as recommender systems. Perhaps, the authors’ approach would work when applied to accumulated data of larger numbers of users. If so, the particular outcome would be something like a theory of mind of a user stereotype. Related questions are still open.
\nAnother question derives from the authors’ generalization of identification by enumeration. The authors are convinced that it is possible to generalize their recent approach to dynamic identification by enumeration even further. This requires a careful easing of one or more of the requirements named operational appropriateness, conversational appropriateness, and semantic appropriateness. The related open questions need some more research effort.
\nFinally, the authors want to attract the audience’s attention to a larger and rather involved field of research problems beyond the limits of this chapter: reflective artificial intelligence.
\nThere are rarely any bug-free software systems. In the future, there will be rarely any bug-free assistant systems. However, even if a future assistant system were to be totally free of bugs, it would hardly be able to solve every imaginable problem. Digital assistant systems may fail. In response to this severe problem, it is necessary to work toward digital systems able to ponder their own abilities and limitations. Systems that do so are called reflective.
\nLimitations of learning systems are unavoidable [17]. In response, approaches to reflective inductive learning have been developed and investigated in much detail [75]. The results demonstrate the possibility to design and implement reflective artificial intelligence.
\nThe authors’ step from the conventional approach to dynamic identification by enumeration reveals a feature of reflection. A learning digital assistant system that gives up a certain space of hypotheses—in formal terms, γ(f[n]) ≠ γ(f[n + 1]) resp. γ(fX[n]) ≠ γ(fX[n + 1])—with the intention to change or to extend the terminology in use is, in a certain sense, reflective. It “worries” about the limits of its current expressive power and aims at fixing the problem. Vice versa, a system able to change spaces of hypotheses, but not doing so (formally, it holds γ(f[n]) = γ(f[n + 1]) or γ(fX[n]) = γ(fX[n + 1]), resp.), shows a certain confidence in its abilities to solve the current problem.
\nThis leads immediately to a variety of possibilities to implement reflective system behavior. First, a system changing its space of hypotheses may inform the human user about its recent doubts as to the limitations of terminology. Second, a bit further, it may inform the human user about details of the new terminology. Third, such a system may also report confidence.
\nAs a side effect, so to speak, the authors’ work leads to concepts and algorithmic approaches to reflective AI. This bears strong evidence of the need for further in-depth investigations.
\nAfter the second author—inspired by some fascinating results in behavioral sciences—has introduced the concept and coined the term of theory of mind modeling and induction in 2012, the two authors’ student Bernd Schmidt has undertaken the endeavor to provide the first theory of mind modeling and induction application. The authors are grateful to him for his engaged and excellent work and for his continuous willingness to meet whatsoever requirements.
\nWorking on an internship, Rosalie Schnappauf, then a student of the University of Rostock, took part in a series of experiments demonstrating that Bernd Schmidt’s implementation of identification by enumeration does really work and allows for the fully computerized induction of a human game player’s goals and intentions—a very first case of, so to speak, mining HCI data for theory of mind induction.
\nRosalie’s and Bernd’s success encouraged the authors to attack harder application problems and to develop the generalized approach to dynamic identification by enumeration.
\nPart of the work reported in this chapter has been supported by the German Federal Ministry for Education and Research (BMBF) within the joint research project ODIN aiming at Open Data INovation. The authors’ subprojects KVASIR (Erfurt University of Applied Sciences) and BALDUR (ADICOM Software) are currently administrated by the agency Projektträger Jülich (PtJ, see
Because of its low incidence, the risk of patient exposure to ionizing radiation is often underestimated—and underappreciated—as a patient safety (PS) threat across various healthcare settings. Consequently, the Joint Commission mandates that hospitals prepare for managing radiation-related risks in terms of protecting patients from unnecessary exposure, limiting any associated potential damage, monitoring the types and extent of radiation, and maintaining proficiency in decontamination procedures in cases of direct radioactive isotope contact [1, 2]. In terms of everyday healthcare facility functioning, there is a dual focus to ensure that radiation safety standards are met: (a) avoidance of unnecessary exposure including improper dosing and (b) assurance that radioactive material will be properly handled and disposed [2].
\nRegardless of the details or the mode of delivery, the intent of the treating team should always be the reduction in both short- and long-term radiation exposures [3]. It has been recommended by different organizations and authors that radiation exposure reduction (RER) efforts encompass both pre-procedural and procedural phases of treatment [4, 5]. The use of radiation for diagnostic or therapeutic indications (RDTI) has clear benefits when appropriately directed and supervised. However, serious errors, prolonged or repeated exposures, and lack of supervision can be associated with significant adverse consequences, including the risk of acute radiation sickness, malignancy, and death [6, 7, 8, 9, 10]. Table 1 [top section] lists the approximate incidence of adverse effects at various levels of radiation exposure (measured in Rads). In addition, comparative descriptions of alternative radiation units of measure are provided for the reader in the lower section of Table 1. The latter measure is intended to reduce the confusion often encountered due to multiple naming conventions in this area of science.
\nSide effect | \nFrequency | \nMinimum exposure amount (Rads) | \n
---|---|---|
Hyperpigmentation/erythema | \n>50% | \n50–200 | \n
Mild fatigue | \n>50% | \n50–200 | \n
Mild myelosuppression | \n>50% | \n50–200 | \n
Skin desquamation | \n<10% | \n100 | \n
Mild nausea/vomiting/diarrhea | \n<10% | \n100–400 | \n
Intractable vomiting/diarrhea | \n90% | \n>400 | \n
Comparison of alternative units of measure | \nConversion factor | \n
---|---|
1 Rad | \n0.01 Joule/kg; 0.01 Gray; 0.01 Sv | \n
1 Millirad | \n0.00001 Joule/kg; 0.00001 Gray; 0.00001 Sv | \n
1 Milligray; 1 Centigray; 1 Decigray; 1 Dekagray | \n0.1; 1; 10; 1000 Rads, etc. (respectively) | \n
1 Coulomb/kg | \n3876 Roentgen; 3875 Parker; 3875 Rep | \n
1 Millicoulomb/kg | \n3.876 Roentgen* | \n
1 Microcoulomb/kg | \n0.003876 Roentgen* | \n
1 Tissue Roentgen | \n1 Roentgen | \n
Approximate incidence of adverse effect at different radiation exposures measured in Rads.
kg = kilogram; Sv = Sievert; * = same applies for Parker and Rep units.
An important distinction must be made between radiation exposure and radioactive contamination. Radiation exposure refers to a person receiving energy in the form of waves or particles from an external source or from internal contamination [9, 10]. To prevent harm to the patient, the duration of exposure is carefully controlled. To prevent harm to the radiology technician, distance and shielding from source are employed [11, 12]. In contrast, a contaminated person has radioactive material on (or inside) the body secondary to ingestion, inhalation or deposition on the body surface. Thus, contamination can be classified as internal or external. Most patients exposed to radiation are not contaminated [13]. Radiation can be measured in SI unit Gray (Gy), which represents the absorption of one joule of radiation energy per kilogram of matter. In order to reflect the degree of radioactive contamination in human tissue, the unit of Sievert (Sv) us usually employed. The following clinical vignettes will illustrate both radiation exposure (#1) and contamination (#2 and #3). For the purposes of our chapter, the reader should be familiar with the three general types of radiation, including the associated energetic characteristics and shielding capacity (Table 2). In addition, various levels of radiation exposure (measured in millisieverts) including the typical associated contextual settings are shown in Figure 1.
\nType of radiation | \nPenetrating energy | \nPenetrating capacity in human body | \nShielding capacity | \n
---|---|---|---|
Alpha (α) | \nLow | \nEpidermis | \nDissipates in air | \n
Beta (β) | \nIntermediate | \nSoft tissue | \nSheet of paper | \n
Gamma (γ) | \nHigh | \nBones and organs | \nLead | \n
Types of ionizing radiation, with corresponding levels of penetration and preferred shielding characteristics.
Different levels of radiation exposure, measured in millisieverts (mSv) and the associated biological manifestations.
Over a period of months, numerous patients who underwent computed tomography (CT) perfusion scans of the brain at different hospitals across a wide geographic area reported vague complaints of oddly shaped patterns of unexpected hair loss. Reportedly, the mostly band-like areas of alopecia appeared within 1–2 weeks following each patient’s CT study. Some patients began complaining of new onset memory loss and/or difficulty keeping balance while walking. Given the unusual pattern of clinical signs and symptoms, as well as the isolated nature of occurrences, it took months before the connection was made between CT perfusion scans and what turned out to be significant radiation overdoses. When the true scope of the problem became evident, hundreds of patients were identified as having received approximately eight times the expected levels of radiation. It appeared that the root cause for the above occurrences may be faulty programming of CT scanner devices. A nationwide statement of caution was issued by the FDA, urging hospitals across the US to review institutional CT scan logs to check radiation dosage levels and data regarding applicable adherence to established dosing protocols [14, 15]. In response to the above events, the first state law in the US aimed at protecting patients from excessive radiation exposure during CT scans was signed into law by Gov. Arnold Schwarzenegger of California [16]. In addition to providing an accreditation mandate for CT scanners, the bill also requires that radiation dose be recorded on the scanned image in a patient’s medical record, and that radiation overdoses be reported to patients, treating physicians, and the state Department of Public Health [16].
\nIn 1987, improperly abandoned hospital radiation equipment in Goiania, Brazil, led to the contamination of a large number of people. During the post-incident review, it was discovered that an unused irradiation machine was left behind when a privately owned healthcare facility moved. The device was subsequently stolen by a group of young men who sold it to a scrap metal dealer. During the disassembly of the medical equipment, a broken capsule of the highly radioactive cesium-137 was accidentally smashed, along with its lead enclosure, liberating “shiny bluish dust which glowed in the dark” [17]. Unaware of the danger, numerous individuals associated with the scrap metal yard owner came into contact with the radioactive powder. The most seriously affected victims developed alopecia, cutaneous burns, vomiting and diarrhea. The governmental response was slow at first, due mainly to the lack of recognition of the magnitude and the urgency of the situation. Experts from the Soviet Union and the US were involved in the subsequent management and containment of the radioactive risk. The incident was thought to be the most serious of its kind at the time, with 240 documented cases of contamination, 20 hospitalizations, and 4 fatalities [17, 18].
\nIn 1992, an unexpected discovery of radioactive waste was made by a regional disposal company in Indiana, Pennsylvania [9, 19]. Subsequent investigation by the US National Regulatory Commission (NRC) found that in November of 1992, a local clinic in Indiana, Pennsylvania treated a patient with high-dose brachytherapy using an iridium-192 radioactive source [20]. It was determined that the treatment was not completed due to equipment-related issues. Unknown to the operators, the source wire became fractured and remained in the patient. Investigators discovered that the required radiation survey at the end of the treatment was not performed. The patient was discharged to a nursing home and died 5 days later. Unaware of the danger, nursing home staff removed the source-containing catheter and disposed of it as biohazardous waste [9]. The source was identified during routine radiation surveillance by the waste disposal company. In addition to being a contributor to the index patient’s death, more than 90 individuals may have been exposed to the radioactive material, with doses ranging from <0.05 to >2.55 rem [20].
\nDifficult to identify at the time of the initial exposure, radiation injury tends to present in a delayed fashion. Radiation injury also tends to be low on a typical differential diagnosis list as most cases tend to involve unintentional (and unrecognized) exposure. As demonstrated by our three vignettes, the uncommon occurrence of harmful medical radiation exposure (HMRE) can originate as a result of various types of PS error; both of omission and of commission [21]. In addition, radiation-related PS issues can result from lack of adequate oversight at both institutional level (e.g., absent safety procedures) and governmental level (e.g., lack of applicable laws, regulations, or enforcement) [9, 22, 23].
\nComplexities associated with HMRE prompted an important discussion regarding the nature and the content of the informed consent process, specifically as it relates to medical radiation exposure [24]. The true gravity of such considerations is exemplified by the known association between cumulative radiation exposure and the incremental risk of malignancy following repeated CT imaging episodes [25]. Moreover, compared to the adult population, the overall risk is significantly greater for pediatric patients [26].
\nTwo broad categories of clinical (e.g., biologic) effects of radiation, specific to the contexts of radiation therapy or accidental isotope exposure, include deterministic injuries and stochastic injuries. Deterministic injuries manifest as radiation-induced escalation of normal physiologic apoptosis resulting in increased death of essential cells with resultant tissue and organ dysfunction [27]. These types of injuries occur when large numbers of cells become damaged and, as a result, die immediately or shortly after irradiation [28]. Dermatoligic post-exposure injury can range from “local erythema” to “skin necrosis” [28]. Estimation of dosage is measured in the units of Gy, with 0–2 Gy associated with no biological effects; 2–5 Gy causing transient erythema (<2 weeks), followed by epilation (2–8 weeks) and recovery (6–52 weeks); 5–10 Gy associated with prolonged erythema (up to 8 weeks), epilation (2–8 weeks), and recovery (6–52 weeks); 10–15 Gy exposure causes transient erythema (<2 weeks), dry/moist desquamation (2–8 weeks), followed by permanent epilation (6–52 weeks) and finally atrophy (>40 weeks); and >15 Gy being associated with acute ulceration (<2 weeks), moist desquamation (2–8 weeks), dermal necrosis (6–52 weeks), and eventual surgery (>40 weeks) [28]. Table 3 outlines the above exposure levels in a systematized fashion.
\nRadiation dose (Gy) | \nPossible adverse reaction | \nTimeline | \n
---|---|---|
0–2 | \nNo effect | \n\n |
2–5 | \nTransient erythema | \n<2 weeks | \n
5–10 | \nProlonged erythema | \n<8 weeks | \n
10–15 | \nDry/moist desquamation leading to permanent epilation | \n2–8 weeks → 6–52 weeks | \n
>15 | \nAcute ulceration leading to desquamation and dermal necrosis | \n<2 weeks → 6–52 weeks | \n
Post-exposure deterministic injury shown with radiation dose in Gray units and the typical timeline associated with the appearance of adverse effects.
Stochastic effects manifest as cellular carcinogenesis and result from radiation induced mutations in genetic material of cells including germ cells [27]. For stochastic injuries, post-radiation damage becomes the key determinant of clinically apparent, usually long-term manifestation [28]. Such effects also depend on the type/activity of the isotope involved. More specifically, these kinds of injuries have a linear nonthreshold dose that may lead to radiation-induced malignancy and/or heritable genetic defects [28]. Estimation of dosage from radiologic studies utilizes the units of Sieverts (Sv), with procedures such as dual-isotope SPECT (24 mSv) and CT angiography (19 mSv), carrying the highest effective radiation doses [28]. Of note, victims of the Chernobyl disaster were exposed to a maximum radioactivity of 300–450 mSv/h within a 15 km radius. The individuals that had suffered from radiation are suspected to have received a minimum of 0.8–2 Gy (80–200 Rad) dose [28].
\nThe first line of ensuring safety is the presence of organizational policies and procedures pertaining to HMRE as well as the handling of radioisotope-containing medical materials, both at the departmental and institutional levels [29, 30, 31]. In addition to applicable policies and procedures that are harmonized to prevailing laws and regulations, organizations also employ radiation safety experts in the role of Radiation Safety Officer (or functional equivalent thereof) to ensure the maintenance of appropriate legal and procedural compliance [31, 32, 33]. Any HMRE events that are deemed reportable to appropriate local, regional, or national authorities are handled by the Radiation Safety Officer. In addition, employees who work around radiation equipment and/or interact with medical radioisotopes must wear radiation monitoring badges that help quantify levels of healthcare worker exposure [34, 35]. Some general considerations of how appropriate policies and procedures can help protect the well-being of both patients and healthcare workers include [7, 32, 36, 37, 38]:
In diagnostic radiography, the use of hardwired “safety prompts” helps facilitate double-checking of the expected radiation dosage; also, it is important to ensure the presence of appropriate warning lights, such as “X-ray in progress” and sufficiently labeled facilities with caution signs
Ensuring that the delivery process of therapeutic radiation is appropriately structured, including thorough planning, simulated application, and the presence of built-in cross-checks (e.g., two or more experts sign-off on the final therapeutic plan, including the physician, the physicist, and a dosimetrist)
Monitoring of cumulative monthly radiation exposure and limiting further exposure for those employees who exceeded established thresholds
Protocolized monitoring of medical waste for the presence of radioactivity, both at the site of origin (e.g., the hospital) and at the destination (e.g., landfill)
In the European Union and associated countries, the Euratom Treaty recommends that a patient examination and clinical justification are provided before a referral is made to a radiologist or a nuclear medicine expert. Moreover, nonionizing radiation is preferred whenever it will provide comparable information to that obtained by means of ionizing radiation [39]. For example, an ultrasound or magnetic resonance imaging (MRI) may provide the same desired information as a CT, without the need for ionizing radiation [40]. Additional safety enforcement strategies include: safety checklists to verify the patient and study being performed; radiation dose customization utilizing the patient’s weight, age, medical history, and intended body segment to be scanned/imaged; and decision support systems which provide ordering physicians an opportunity to answer questions regarding their patients and consider alternatives to ionizing diagnostics [40].
\nThe US Food and Drug Administration (FDA) has partnered with other organizations to promote education and communication regarding radiation safety to patients and medical professionals [41]. Among their resources, the FDA collaborated with the National Council on Radiation Protection and Measurement to communicate the risk of radiation exposure with patients, particularly imaging involving young children [41, 42]. The FDA advocates for patient and healthcare provider awareness via the Image Wisely and Image Gently radiation risk campaigns, as well as with the International Atomic Energy Agency’s “Radiation Protection of Patients” website [41, 43, 44]. The FDA has also advocated for patient and healthcare provider tools to reduce radiation exposure. One particular innovative safety tool is the “Patient Medical Imaging Record Card”, which was developed by the FDA in collaboration with Image Wisely [41, 43]. The card can be used to track patient imaging studies by date, type, and location to prevent unnecessary repeat ionizing radiation exposures [41]. Looking toward the future, this card would ideally be integrated into the patient’s electronic health record and stored in a nationally accessible database for healthcare providers, such as the Federal Data service Hub, which is established by the Affordable Care Act and backed by the Health and Human Services department [45].
\nThe US Nuclear Regulatory Commission was established with The Energy Reorganization Act of 1974 to license and regulate the civilian use of radioactive materials to protect public health and safety and the environment. It is in charge of overseeing nuclear reactors, security, and materials as well as radioactive waste. The commission sets rules and licensing, enforces those rules, evaluates facilities, and provides support and logistics for incident response. Some aspects of management and regulation of certain radioactive materials have been granted to Agreement States [46].
\nAlthough most individuals exposed to radiation contamination are not symptomatic, the consequences of such exposures tend to result in long-term sequelae [47, 48, 49, 50]. Providers should be aware of signs and symptoms of radiation injury so that such occurrences can be readily recognized, contained, and victims treated promptly [51, 52]. As demonstrated in our Clinical Vignette #1, acute HMRE tends to have organ-specific, regional anatomic manifestations (e.g., pneumonitis, lung fibrosis, gastric ulceration, and radiation proctitis) [52, 53, 54]. Systemic manifestations (e.g., acute radiation syndrome) are extremely rare in the healthcare setting and usually involve direct exposures of patients, workers, or otherwise unsuspecting individuals, to the radioactive isotope material, as outlined in our clinical vignette #2 [18, 55] and clinical vignette #3 [9, 19, 20].
\nAcute radiation syndrome (ARaS), unlike radiation injury, is a systemic entity that occurs very rarely in the healthcare setting. It usually involves some form of equipment failure, radioactive isotope release, criminal activity/theft, or inappropriate disposal of equipment or isotope(s) [9, 18, 19, 20, 55]. Because ARaS may be the only overt “manifestation” of a major radioactive breach, it is critical that it is promptly recognized, and that it leads to a thorough investigation into associated events. Symptoms of ARaS evolve over time in distinct phases. The duration of each phase and the time of its onset will be approximately inversely proportional to the dose [56]. An initial prodromal phase, with symptoms such as nausea, vomiting, weakness, and fatigue, typically develops within hours to days after exposure of the whole body to radiation exceeding 0.7 Gray (Gy). ARaS manifests most acutely and severely in the hematopoietic, gastrointestinal, and cardiovascular/neurovascular systems [27, 57]. Radiation-induced gastrointestinal manifestations of ARaS manifest as nausea, vomiting, and bloody diarrhea. Severe dermatological injury with burns, desquamation, epilation, and ulceration can occur after significant radiation exposure even in the absence of ARaS [58], as exemplified by our clinical vignette #1. The above manifestations are summarized in Table 4.
\nSyndrome | \nHematopoietic | \nGastrointestinal | \nCardiovascular/neurovascular | \n
---|---|---|---|
Dose | \n>0.3–0.7 Gy | \n>6–10 Gy | \n>20–50 Gy | \n
Prodromal stage (minutes—2 days) | \nAnorexia, nausea/vomiting | \nAnorexia, severe nausea, vomiting, cramps, and diarrhea | \nExtreme nervousness and confusion, severe nausea, vomiting, watery diarrhea, loss of consciousness and burning sensation of the skin | \n
Latent stage | \nPatient appears well for 1–6 weeks | \nPatient appears and feels well for less than a week | \nPatient may return to partial functionality (often lasts less than several hours) | \n
Manifest illness stage | \nAnorexia, fever, and malaise Drop in all blood cell counts Primary cause of death is infection and hemorrhage Most deaths within a few months Survival rate is inversely proportional to dose | \nMalaise, anorexia, severe diarrhea, fever, dehydration, and electrolyte imbalance Death occurs within 2 weeks after exposure | \nWatery diarrhea, convulsions, and coma Onset occurs 5–6 hours after exposure Death occurs within 3 days of exposure | \n
Recovery | \nFull recovery for large percentage of patients from a few weeks to 2 years after exposure Death may occur in some individuals at 1.2 Gy The LD50/60 is 2.5 to 5 Gy | \nThe LD100 is about 10 Gy | \nNo recovery expected | \n
Acute radiation syndrome: most common manifestations [13].
The general principles of protection from radiation injury depend upon four factors: distance, time, shielding, and removal or containment of contamination [27]. When caring for potential radiation contaminated patients, healthcare personnel must minimize the duration of exposure to a source, maximize the distance from source, and establish effective shielding from the source. Identification of the presence of radioactive contamination on or within a patient mandates early removal/containment in order to forestall further damage and contamination [27]. In cases similar to the Goiania incident, hand-held Geiger counters must be utilized in order to focus on accurately identifying anatomic areas of contamination unique to each individual [1]. Substantial exposure of emergency responders and clinicians caring for potentially heavily contaminated patients may occur. Emergency medical services and clinicians must use caution and adhere to strict precautions for managing hazardous materials to prevent inadvertent contamination of themselves and others [27]. Personnel should wear radiation dosimeters, sealed in clear, airtight plastic bags, and worn outside the clothing to allow rapid assessment and early detection of contamination. Workers and work areas should undergo repeated surveillance with radiation detectors at appropriate intervals [1, 27].
\nIn cases of more significant exposure, ARaS manifests initially through the hematopoietic system as blood marrow tissues are highly radiosensitive [27]. Of all the components of hematopoiesis, circulating lymphocytes have the most radiosensitive cell lines and provides a useful laboratory tool to screen for the severity of the radiation sickness early in observation (Figure 2) [56]. After whole body exposure above 0.5 Gy, the rapid fall in lymphocyte number starts within hours, and the lymphocyte depletion is proportional to the dose between 1 and 10 Gy [56]. GM-CSF may be helpful for the recovery of the bone marrow function after clinically significant radiation exposure [57]. Lymphocyte depletion kinetics serves as the single best estimator of radiation exposure and clinical outcome [27]. A decrease in absolute lymphocyte levels may be observed at whole-body doses as low as 100 mSv, but clinically significant response may not be seen below 1–2 Sv. Depending on the absorbed dose, such changes can begin within hours of exposure, so it is recommended that an immediate complete blood count with differential is performed as a baseline and then every 6–12 hours thereafter for 2–3 days [27]. An elevated serum amylase provides a supplementary piece of information that may also be an early sign of serious radiation exposure involving the head and neck. The results of this test are nonspecific; however, and they may also reflect alcohol intake, a stress response, trauma to the face or abdomen, or other factors [27]. In addition, the presence of nausea and vomiting within several (usually around 4) hours of exposure may also be diagnostically helpful.
\nTime-dependent lymphocyte depletion kinetics following either severe or moderate radiation exposures. As early as 6–12 hours following exposure, there may be some indication of the severity of the exposure [35].
Similar to other toxicological phenomena, determining the potential harm of radiation exposure mandates consideration of three factors: dose of radiation exposure, tissue or surface area exposed, and duration of exposure. Whole body radiation exposure to 4 or 5 Sv (or Gy) imparts potentially lethal effects, while an extremity can tolerate several times that exposure [27]. General measures of radiation exposure (e.g., fluoroscopy time) have low utility and accuracy [28]. At this juncture, it is important to introduce the concept of KERMA, or “Kinetic Energy Released in Matter”, which is a measure of energy delivered (or dose) [28]. Air-KERMA is the KERMA measured in air (e.g., low scatter environment) [28]. More useful methods of determining radiation administered include: (a) total air-KERMA (exposure) at pre-specified reference point, (b) air-KERMA area product, and (c) peak skin dose or the maximum dose received by any local area of patient skin [28, 59]. See Figures 3–5 for further information.
\nTimeline for post exposure injury for dosage of 2–5 Gy.
Timeline for post exposure injury for dosage of 10–15 Gy.
Timeline for post exposure injury for dosage >15 Gy.
A point of concern among care providers and parents is the risk of radiation exposure from medical imaging, especially in the pediatric population. Epidemiologic studies have shown that in utero exposure to radiation is associated with higher incidence of pediatric cancers, but data related to rates of pediatric and adult cancers are relatively scarce [60]. In recent years, CT scanning has become the favored imaging modality in many clinical scenarios and is likely to see even further increases in use going forward [61, 62, 63]. As such, CT utilization in pediatrics has increased markedly over the last 20 years. Over 85 million CT scans are performed annually in the United States, with 5–11% of these performed on children [64]. Before we embark on further discussion, important dose-related information in the context of diagnostic testing is provided in Table 5.
\nRelative radiation level | \nAdult effective dose estimate range (mSv) | \nPediatric effective dose estimate range (mSv) | \nExample examinations | \n
---|---|---|---|
O | \n0 | \n0 | \nUltrasound; MRI | \n
☢ | \n<0.1 | \n<0.03 | \nChest X-ray; hand X-rays | \n
☢☢ | \n0.1–1 | \n0.03–0.3 | \nPelvis X-ray; mammography | \n
☢☢☢ | \n1–10 | \n0.3–3 | \nAbdomen CT; nuclear medicine bone scan | \n
☢☢☢☢ | \n10–30 | \n3–10 | \nAbdomen CT with and without contrast; whole body PET | \n
☢☢☢☢☢ | \n30–100 | \n10–30 | \nCTA chest abdomen pelvis with contrast; transjugular intrahepatic portosystemic shunt placement | \n
Relative radiation level designations along with associated effective adult and pediatric doses, as well as imaging examinations that correspond to said levels [65].
A typical CT scan of the head of a child carries an average dose of 2–2.5 millisieverts (mSv) of radiation. CT imaging of the chest and abdomen carries doses averaging 3–4 and 5–6 mSv, respectively. The actual dose administered differs from the more nebulous effective dose, as other factors make the amount of radiation exposure more meaningful in children than adults. The effective radiation doses received by children are about 50% higher than those received by adults for similar imaging studies due to smaller body sizes and radiation attenuation [66, 67]. Up to an age of 10, children are approximately three times more sensitive to radiation than adults, which is why longer life expectancy coupled with organ systems that are still developing disproportionately increases the relative burden of pediatric radiation exposure [67, 68, 69].
\nSeveral studies have attempted to answer questions regarding specific childhood cancer risks associated with radiation exposure. Two studies showed increased incidence of pediatric leukemia in children with medical radiation exposure; however, these studies used retrospective questionnaire data and their result as inconsistent with older data [70, 71]. Certain genetic phenotypes might make some children more sensitive to the effects of radiation and risk of acute lymphocytic leukemia [72]. Very limited data exist on CT-attributable risk of solid tumors in children. There is weak evidence regarding the association between radiation exposure and such occurrences (e.g., pediatric astrocytoma and Ewing’s sarcoma), but this connection is in no way definitive [60].
\nData regarding the lifetime risk of cancers appear to be more robust. A large retrospective cohort study reviewed >175,000 patients from the NHS registry in England [26]. The authors noted a positive association between dose of radiation from CT imaging and leukemia and brain tumors. They found relative risk of leukemia to be 3.18 in patients who received more than 30 mSv of cumulative radiation. Similarly, they found an increased relative risk of brain cancer to be 2.82 in pediatric patients who received cumulative dosing of 50 mSv or more [26]. The caveat to these data, however, is that these are rare cancers to begin with, thus the absolute relative risk increase is very small. Although the relative risk of brain cancer may nearly triple with significant cumulative radiation exposure, the absolute risk is still exceedingly small. Based on robust statistical models, for every 100,000 skull/brain CT scans in 5-year-old children, eight brain/central nervous system cancers and four cases of leukemia would result [73]. The same study estimates that 100,000 chest CT scans would lead to an excess of 31 thyroid cancers, 55 breast malignancies, and 1 leukemia case [73]. Consequently, the lifetime risk of cancers, although small, should be discussed with parents of children undergoing CT scanning. Although these studies are largely safe in children, unnecessary exposure to radiation should still be avoided, and diagnostic tests not utilizing ionizing radiation should be used whenever possible. The medical necessity of imaging should be weighed against the relatively small risk of harm when determining the appropriateness of these studies. Again, the greatest risk of cancer appears to exist when children are exposed to cumulative doses of radiation greater than 30–50 mSv.
\nAccording to the American College of Radiology, no single diagnostic X-ray study or procedure results in radiation exposure sufficient to threaten the well-being of the pregnant patient, the developing embryo, or the fetus [74]. In fact, diagnostic radiation exposures during pregnancy may be safer than the frequent concerns over in utero radiation exposure suggest [75]. Moreover, the utilization of diagnostic radiological imaging may entail more benefit than risk in the evaluation of certain maternal injuries or illnesses [76]. As much attention should focus on limiting diagnostic radiation exposure of the gravid woman’s breast tissue, to prevent carcinogenesis, as on limiting radiation exposure of the fetus [77, 78]. In the setting of pregnancy, radiation exposure should be limited to 1 mGy during the first trimester, with teratogenicity risk being elevated at 5 mGy [79]. In addition, iodine-containing contrast media may lead to hypothyroidism in the fetus, an additional consideration when performing radiographic studies utilizing contrast material [79]. Counseling of the patient by the referring clinician and by the radiologist is essential in providing informed consent as the benefits and risks of procedures can be opaque and the decision may impart lasting consequences [80]. Impacting 5–7% of all pregnancies, trauma represents an important cause of nonobstetric maternal morbidity and mortality [81]. Consequently, the risk-benefit equation regarding diagnostic imaging in this particular setting is somewhat different, with the mantra that the best way to ensure fetal wellbeing is to aggressively treat the mother [82].
\nLiterature suggesting that accrual of cumulative radiation exposures from diagnostic radiological studies, such as CT scans or fluoroscopy, over the course of patients’ lifetimes puts them at risk for the potential carcinogenic risks of radiation [83, 84]. One example here comes from the area of endovascular interventional procedures. Since the introduction of endovascular therapy in the late 1980s, there has been incredible growth in this group of procedural modalities. In fact, endovascular procedures have increased approximately 400% over the past decade [85]. The applicability and medical advancements of this form of therapy have revolutionized treatment of our patients. However, there has been an associated cost, including substantial risk of ionizing radiation exposure [86]. Some of the pioneers of endovascular therapy have succumbed to the deleterious consequence of ionizing radiation [87]. Radiation safety practices have made tremendous advances since the discovery of Roentgen’s X-rays over 120 years ago. Early practitioners were focused on patient outcomes and providing minimally invasive methods to treat complex disease processes. These sacrifices of early practitioners led to our awareness and knowledge that now allows us to perform truly remarkable treatments to benefit our patients. A number of very practical steps can be taken to reduce radiation exposure to patients, operators, and staff [88, 89]. Awareness itself can be an effective first step in reducing exposure. Once awareness of the problem exists, we can then work to educate and enact training and methodology to achieve maximal safety to our patients and ourselves. However, despite the available data, there remains a significant safety deficit. In 2014, a survey of US vascular surgery trainees found 45% had no formal radiation safety training, 74% were unaware of the radiation safety policy for pregnant females, 48% did not know their radiation safety officer’s contact information, and 43% were unaware of the acceptable yearly levels of radiation exposure [90]. However, an important observation was that the trainees who felt their attendings were applying ALARA techniques were much more likely to do so themselves. Therefore, it is incumbent on those of us providing training to the next generation of caregivers to set an example of excellence and expect the same from our trainees. Only by expecting excellence can we hope to achieve superior safety for our patients and ourselves.
\nAdvocates for radiation safety recommend exposing patients, especially children, to as little radiation as possible. This is embodied within the concept of “as low as reasonably achievable” (ALARA) in the context of radiation exposure [84]. As such, ALARA addresses the role for healthcare providers, particularly those caring for children, in reducing exposure to radiation while maintaining the reliability of the diagnostic radiology modality [91]. Multiple methods can be used to achieve ALARA including: adjusting the amount of radiation in the diagnostic study based on patient weight, considering alternative modalities such as sonography or magnetic resonance imaging, enhancing shielding with thyroid or breast shields, focusing on the suspicious area with focused or limited view diagnostic imaging, and discouraging repeat CT scan studies [91]. In one example, although noninvasive multi-slice cardiac-computed tomography angiography (CCTA) can accurately screen for coronary ischemia, its widespread utilization has generated concern because of potential diagnostic radiation exposure. Utilization of a radiation dose reduction program in concert with limiting the image acquisition window for CCTA has demonstrated marked reduction, more than 50%, in estimated radiation doses in a statewide registry without impairment of image quality [83]. In another example, appendicitis represents the most common disease process resulting in increased CT scan utilization in children over the last two decades. Clinical practice guidelines advocating for “abdominal sonography first” for the evaluation of appendicitis have demonstrated comparable diagnostic accuracy to CT scan imaging, while reducing CT scan utilization and thus radiation exposure [91]. The Pediatric Emergency Care Applied Research Network collaborative development of a clinical decision guideline for pediatric head trauma is another example of research helping to reduce the medical radiation footprint by reliably identifying patients at low risk for clinically important traumatic brain injuries, for whom CT can routinely be obviated [92].
\nCareful adherence to existing PS protocols, including active surveillance for any signs and/or symptoms of HMRE, is among the most important considerations for facilities/departments providing diagnostic and/or therapeutic radiation services [28]. In addition to direct radiation, the formation of X-ray image is inherently associated with some degree of “scattered radiation” that is the principal source of exposure to the patient and medical staff [28]. This “scatter” increases with both intensity of the X-ray beam and the size of the exposed field [28]. Any hospital employing medical radiation needs to have an infrastructure to support protocols for every step of the way throughout the application of said radiation including patient and healthcare worker safety, proper identification and dosing, and waste management of materials in order to prevent contamination.
\nThe power to harness ionizing radiation for medical uses has a history spanning more than a century. Although its positive impact on the modern-day prowess of the diagnostician is unquestionable, great care must be taken in order to not abuse this technology. Diagnostic imaging with ionizing radiation seems poised to be part of the medical armamentarium for the foreseeable future. Further research is required in all aspects of this field, including more efficient protocols for delivery, custom-tailoring therapy which takes into account the patients’ makeup, potential short-term and long-term harmful effects, the prediction and prevention of harm and better safeguards for dosimetry not only for patients but also for healthcare workers. Greater strides must be achieved in the realm of oversight and standardization of practice, as well as a comprehensive, nonpunitive reporting system for adverse events. A multidisciplinary approach from health physicists, radiation safety personnel, and clinicians is paramount for the management of contamination events and for the safe and accurate use of both diagnostic and therapeutic medical radiation. The key for this technology going forward is for education to be widespread among all levels of healthcare, from patients and their families to healthcare providers and policy makers. Research and public health information dissemination will go hand-in-hand throughout the next century of medical radiation use.
\nIntechOpen celebrates Open Access academic research of women scientists: Call Opens on February 11, 2018 and closes on March 8th, 2018.
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\\n\\nAPPLYING FOR THE “INTECHOPEN WOMEN IN SCIENCE 2018” OPEN ACCESS BOOK COLLECTION
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\n\nThe submissions are now closed. All applicants will be notified on the results in due time. Thank you for participating!
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