Bulbar dysfunction scale.
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Barely three months into the new year and we are happy to announce a monumental milestone reached - 150 million downloads.
\n\nThis achievement solidifies IntechOpen’s place as a pioneer in Open Access publishing and the home to some of the most relevant scientific research available through Open Access.
\n\nWe are so proud to have worked with so many bright minds throughout the years who have helped us spread knowledge through the power of Open Access and we look forward to continuing to support some of the greatest thinkers of our day.
\n\nThank you for making IntechOpen your place of learning, sharing, and discovery, and here’s to 150 million more!
\n\n\n\n\n'}],latestNews:[{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"2996",leadTitle:null,fullTitle:"Computational Intelligence in Electromyography Analysis - A Perspective on Current Applications and Future Challenges",title:"Computational Intelligence in Electromyography Analysis",subtitle:"A Perspective on Current Applications and Future Challenges",reviewType:"peer-reviewed",abstract:"Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles. EMG may be used clinically for the diagnosis of neuromuscular problems and for assessing biomechanical and motor control deficits and other functional disorders. Furthermore, it can be used as a control signal for interfacing with orthotic and/or prosthetic devices or other rehabilitation assists. \nThis book presents an updated overview of signal processing applications and recent developments in EMG from a number of diverse aspects and various applications in clinical and experimental research. It will provide readers with a detailed introduction to EMG signal processing techniques and applications, while presenting several new results and explanation of existing algorithms. This book is organized into 18 chapters, covering the current theoretical and practical approaches of EMG research.",isbn:null,printIsbn:"978-953-51-0805-4",pdfIsbn:"978-953-51-7033-4",doi:"10.5772/3315",price:139,priceEur:155,priceUsd:179,slug:"computational-intelligence-in-electromyography-analysis-a-perspective-on-current-applications-and-future-challenges",numberOfPages:462,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"eec43ca5106bfbac5321a0945acf723d",bookSignature:"Ganesh R. 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Naik received his B.E. degree in electronics and communication engineering from the University of Mysore, Mysore, India, in 1997, M.E. degree in communication and information engineering from the Griffith University, Brisbane, Australia, in 2002, and PhD degree in the area of digital signal processing from RMIT University, Melbourne, Australia, in 2009.\nHe is currently an academician and researcher at RMIT University. As an early career researcher, he has authored more than 60 papers in peer reviewed journals, conferences and book chapters over the last five years. His research interests include pattern recognition, blind source separation techniques, audio signal processing, biosignal processing, and human–computer interface. Dr. Naik was the Chair for the IEEE Computer Society CIT08 Conference, Sydney and a member of the organising committee for IEEE BRC2011 conference, Vitoria, Brazil. He was a recipient of the Baden–Württemberg Scholarship from the University of Berufsakademie, Stuttgart, Germany (2006–2007). 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Dysphagia is common in patients with neurological disorders. It can result from damage to the central or peripheral nervous system, as well as muscle and neuromuscular junction disorders. Neurogenic dysphagia often leads to serious complications including pulmonary aspiration, dehydration, and malnutrition. Dysphagia negatively affects the course of the disease and its outcome. Ethinenias can usually be prevented if dysphagia is detected early and treated correctly. The most common cause of neurogenic dysphagia, however, is stroke [1].
Swallowing is a sequence of coordinated voluntary and involuntary (reflex) movements that push contents of the oral cavity into the esophagus and the stomach. It is a complex process consisting of coordinated movements of the jaw, the soft palate, and the esophagus muscles. The process involves the olivary nuclei and the cerebral cortex [2]. A person swallows approximately 600 times per day. Nearly 200 times while eating, 50 times while sleeping, and 350 times in all other cases. In most cases, swallowing is performed unconsciously [3].
The process of swallowing can be divided into four phases: oral, oropharyngeal, pharyngeal, and esophagopharyngeal. During the first (oral) phase, food is delivered into the oral cavity. Food is chewed, it is moistened with saliva, and a food bolus is formed [3, 4].
During the second (oropharyngeal) phase, the food is accumulated at the back of the tongue, chewing is stopped, and the tongue lifts up and pushes the food bolus through the pharynx into the middle portion of the pharynx (oropharynx). At the same time, the longitudinal muscles of the tongue and the mylohyoid muscles contract and press down the tip, back, and root of the tongue sequentially to the hard palate. The tongue is pushed backwards. The soft palate lifts up as to close down the nasopharynx. The pharynx and the hyoid bone move forward and up. The epiglottis moves back and down as to close down the entrance to the trachea. Breathing stops. The pharynx contracts [5].
During the third (pharyngeal) phase, the food bolus moves down into the middle portion of the pharynx, where the middle pharyngeal constrictor and the inferior pharyngeal constrictor contract, ensuring the bolus is squeezed and pushed downwards. At the same time, the larynx and the hyoid bone lifts up, enabling faster food bolus passing through the middle portion of the pharynx to the inferior portion. At the moment of swallowing, the reflex response enables expansion of the esophageal entrance, and the pharyngeal constrictors push the food bolus through the pyriform sinuses into the esophagus [5, 6].
During the fourth (esophagopharyngeal) phase, the upper esophageal sphincter relaxes. The food bolus enters the esophagus. The esophagus contracts sequentially. The inferior esophageal sphincter opens. The food bolus enters the stomach [6].
The first phase is voluntary, while the others are involuntary. Cranial nerves IX, X, XI are involved at all stages of swallowing [7].
Dysphagia (from dys- + Greek “phagein” meaning to eat, to swallow) is a clinical symptom of swallowing dysfunction: difficulty or painful passage of a food bolus from the oral cavity to the stomach [8]. Dysphagia can occur both as an independent condition or as a part of complex syndrome. Thus, dysphagia is the most important element of a bulbar or pseudobulbar palsy. Dysphagia is a subjective perception of difficulty swallowing.
Swallowing disorders are signs of various diseases. It is one of the key problems in diffuse and focal brain injuries [9]. Dysphagia occurs in 27.2% of elderly, who can take care of themselves; in 47.4% of elderly patients in intensive care units; in 51% of persons needing assistance. Dysphagia develops in 13–57% of patients suffering from dementia, in 19–81% of patients with Parkinson’s disease, in 44–60% of patients with neurodegenerative diseases. Neurogenic dysphagia occurs in 25–65% of patients after stroke, provided that mortality rates among tube-fed patients with post-stroke dysphagia vary from 20 to 24% [2, 10]. In 15–17% of cases, difficulty swallowing develops after posterior fossa brain tumor surgeries and represents one of dangerous postoperative complications. Nearly 60% of patients experience difficulty swallowing after extubation [11, 12].
Dysphagia has a negative impact on the quality of life. It leads to severe respiratory complications; it becomes the cause of dehydration, metabolic disorders, and cachexia. Dysphagia increases disability, significantly worsens prognosis, and complicates patient rehabilitation. Dysphagia is a significant risk factor for the development of aspiration pneumonia [13].
Dysphagia is commonly divided into esophageal and oropharyngeal.
Based on the functional mechanisms of disease development, dysphagia is classified into:
neurogenic (motor, high)
organic or mechanical
psychogenic
In addition, all types of dysphagia can be divided into:
acute
chronic
Based on the disease course, dysphagia can be:
intermittent
persistent
progressive, with increasing clinical symptoms
In most cases, neurogenic dysphagia is oropharyngeal [14]. Neurogenic dysphagia is characterized by problems with neural control of swallowing caused by various neurological diseases.
The most common cause of neurogenic dysphagia development is a stroke [9, 10, 15, 16]. Dysphagia can also occur in case of traumatic brain injury, brain tumors, encephalitis, botulism, and rabies. This type of pathology is observed at late stages of Parkinson’s disease, Alzheimer’s disease, amyotrophic lateral sclerosis, other neurodegenerative diseases, severe myasthenia gravis, multiple sclerosis, Guillain–Barré syndrome, and other disorders.
On rare occasions, neurogenic dysphagia can be the sole manifestation. However, in most cases it is a component of bulbar or pseudobulbar palsy (Tables 1 and 2).
Bulbar dysfunctions | Sensitivity of pharyngeal mucosa | Swallowing function | Epiglottis position (evaluation of the epiglottic paresis grade) |
---|---|---|---|
Grade 1 | Partially preserved | Preserved | Upper (normal) |
Grade 2 | Partially preserved | Partially impaired | Upper |
Grade 3 | Not observed | Impaired | Upper |
Grade 4 | Not observed | Grossly impaired | Medium |
Grade 5 | Not observed | Not observed | Lower (epiglottic paralysis) |
Bulbar dysfunction scale.
Variant | Clinical profile |
---|---|
1st | Partially preserved sensitivity of the laryngeal mucosa and the epiglottic mobility; the swallowing function is preserved. On rare occasions, choking starts when liquid food is swallowed; patients do not require tube feeding. |
2nd | Mild impairment of swallowing function with partially preserved sensitivity of the laryngeal mucosa; patients experience significant limitations. These patients can be fed by small portions (using a teaspoon) with food of cream, puree, and jelly-like consistency. When a patient has impaired critical judgment and weak ability to concentrate on performed actions, a need for tube feeding arises. |
3rd | Severe impairment of laryngeal mucosa sensitivity and severe impairment of swallowing function, which require tube feeding. This variant is the most dangerous in terms of underestimation of case severity, because the epiglottis is in its upper position and it does not block breathing. Aspiration of the oropharynx contents into the trachea is common. Due to impairment of laryngeal and tracheal mucosa sensitivity and cough reflex suppression, aspiration is manifested at late stages in the form of respiratory failure and rapidly progressive pneumonia. |
4th | Severe impairment of laryngeal mucosa sensitivity, severe impairment of swallowing function; the epiglottis is in its middle position and usually does not block breathing. In this variant, aspiration is manifested quite soon after failure of respiratory tract protection in the form of respiratory failure and grunting breathing. |
5th | Severe impairment of laryngeal mucosa sensitivity, no swallowing function. The epiglottis rests at the inferior wall of the larynx, i.e., in its lower position. The glottis can be examined only using a fiberscope or via direct laryngoscopy (during intubation). Respiratory failure in such patients is usually manifested immediately after failure of respiratory tract protection (extubation) in the form of difficulty breathing. |
Variants of bulbar dysfunctions (swallowing function).
Bulbar palsy occurs in case of bilateral or unilateral injury of cranial nerve nuclei IX, X, and XII and their roots. Isolated nuclear palsy is not common due to close proximity of anatomical structures of the caudal part of the medulla. Bulbar palsy elements are also included in some alternating syndromes. By its nature, bulbar palsy is peripheral paralysis of the pharynx. In addition to dysphagia, bulbar palsy is characterized by dysarthria (speech disorder resulting from an injury of the nerves responsible for articulation), atrophy of the pharynx and tongue muscles, and aphonia. As a rule, breathing pattern disorders (aspiration syndrome, Lambert–Eaton myasthenic syndrome, respiratory failure) develop in critically ill patients suffering from bulbar palsy, which often leads to death [17]. This is caused by close proximity of the caudal group of cranial nerve nuclei to the respiratory and vasomotor centers [7].
Pseudobulbar palsy is a neurological syndrome caused by bilateral disturbance of the corticonuclear tracts. In contrast to bulbar palsy, pseudobulbar palsy is central paralysis of the pharynx. Pseudobulbar palsy is characterized by the same triad of symptoms as bulbar palsy: dysarthria, dysphonia, and dysphagia. However, in pseudobulbar palsy, swallowing disorders are less severe: there is no muscles atrophy, primitive oral reflexes are present, and pharyngeal reflex does not disappear. Pseudobulbar palsy is characterized by compulsive crying or laughing. Pseudobulbar palsy can be often combined with signs of central hemiparesis or tetraparesis, due to the pyramidal tracts involvement [6, 7, 18].
A special form of oropharyngeal dysphagia called “postextubation dysphagia” (PED) was first observed in 1991 during myography of oral cavities of extubated patients. PED is one of the signs of post-intensive care syndrome (PICS) [2, 19, 20, 21]. The pathogenesis of PED is characterized by the “learned non-used” phenomenon characterized by three-phase swallowing impairment associated with long-term intubation (>48 h) or due to the absence of subglottal pressure in case of a cannula bearing [17, 22].
Dysphagia assessment procedures are selected depending on patient characteristics, severity of swallowing disorder, and procedure availability. Patients with stroke shall be screened for dysphagia during the first 24 hours after the disease onset and before oral eating [9, 23].
Swallowing assessment protocol was developed by the American Speech–Language–Hearing Association (ASHA). The main tests for oropharyngeal dysphagia assessment are the following ones:
Water swallow test (three-swallow test): inexpensive and potentially useful basic screening test together with the data obtained from medical history and physical examination. The test has prognostic sensitivity of >95% as applied to detection of dysphagia occurrence [24, 25].
Videofluoroscopy (“modified barium swallow”), which is a gold standard for oropharyngeal dysphagia assessment. The swallowing process is video recorded during fluoroscopy. Monitoring of patient’s swallowing motions is provided. This procedure can be beneficial in predicting the risk of aspiration pneumonia [26].
Upper gastrointestinal tract endoscopy. Endoscopy is not a sensitive method for detection of impaired swallowing function and is not effective for aspiration detection. At the same time, this method is effective for dysphagia monitoring [27].
Fiberoptic endoscopic evaluation of swallowing (FEES), which is a modified method that includes visual examination of the larynx and pharynx structures using flexible transnasal fiberoptic endoscope when a patient is eating or drinking. This method is the most commonly used in real clinical practice [28].
Overall estimate of dysphagia severity is based on summing up PAS and FEDS grades (Tables 3–5) [15, 27].
Automated impedance-manometry (AIM), which is combination of impedance-manometry and high-resolution manometry. This method provides valuable diagnostic information. These measurements represent reliable prognostic method of aspiration [10, 29].
High-resolution pharyngoesophageal manometry, which is quantitative assessment of pressure and time of the pharynx contraction and time of upper esophagus relaxation. It can be used in combination with videofluoroscopy in order to gain better understanding of involved movements and pressure [30].
Grading | Description of respiratory tract, larynx, and trachea state |
---|---|
1 | Food does not pass into the respiratory tract. |
2 | Food passes into the respiratory tract, staying above the vocal cords and it can be coughed out of the respiratory tract. |
3 | Food passes into the respiratory tract, staying above the vocal cords but it cannot leave the respiratory tract. |
4 | Food passes into the respiratory tract, touches the vocal cords, and is pushed out of the respiratory tract. |
5 | Food passes into the respiratory tract, touches the vocal cords but cannot be pushed out of the respiratory tract. |
6 | Food passes into the respiratory tract, it passes beneath the vocal cords and cannot be pushed out of the larynx or respiratory tract. |
7 | Food passes into the respiratory tract, it passes beneath the vocal cords and cannot be pushed out of the trachea despite the efforts. |
8 | Food passes into the respiratory tract, it passes beneath the vocal cords, but a patient is too weak to cough it up. |
Penetration-aspiration scale (PAS) developed according to Rosenbek criteria.
Main findings | Grade | Potential clinical consequences | |
---|---|---|---|
Saliva | Penetration/aspiration | Grade 6 | No oral eating, only tube feeding |
Pudding | Penetration or aspiration without or insufficient protective reflex | Grade 5 | Tube feeding |
Pudding | Penetration/aspiration with sufficient protective reflex | Grade 4 | Tube feeding with small portions of pudding for oral eating during rehabilitation procedures |
Liquids | Penetration or aspiration without or insufficient protective reflex | Grade 4 | Tube feeding with small portions of pudding for oral eating during rehabilitation procedures |
Liquids | Penetration/aspiration with sufficient protective reflex | Grade 3 | Oral eating of pureed food |
Solid food | Penetration/aspiration with food residues in the pyriform sinuses | Grade 2 | Oral eating of pudding or liquids |
Solid food | No penetration or aspiration, mild residues of food in the sinuses | Grade 1 | Oral eating of semi-solid food or liquids |
Fiberoptic endoscopic dysphagia severity scale (FEDSS).
Overall estimate | 0 = no dysphagia | 1 = mild dysphagia | 2 = moderate dysphagia | 3 = severe dysphagia | 4 = very severe dysphagia | |||
---|---|---|---|---|---|---|---|---|
Penetration-Aspiration Scale (PAS) | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
Endoscopic Dysphagia Severity (FEDS) | 1 | 2 | 3 | 4 | 5 | 6 |
Overall estimate of dysphagia severity.
Dysphagia and protein-energy malnutrition, being predictors of bad functional recovery, are always associated with a high risk of medical complications. Weaning from mechanical ventilation of such patients is difficult. They have a high risk of purulent-septic complications. At the same time, a risk of sudden death increases [31].
Dysphagia complications are malnutrition, dehydration, weight loss, and respiratory tract obstruction. Aspiration pneumonia is one of the most important complications of dysphagia [32].
When a stroke is complicated with dysphagia, malnutrition occurs 3 times more often than without it. The cases of protein-energy malnutrition in patients suffering a stroke vary from 7% to 15% at acute stage and from 22% to 35% after 2 weeks from the disease onset. Starvation or malnutrition associated with dysphagia activates catabolic processes. Among stroke patients requiring long rehabilitation period, malnutrition can amount to 50%. Malnutrition syndrome is risk factor of pneumonia. It increases sensitivity to oropharyngeal flora, leads to immunity suppression, reduces coughing strength, and affects wakefulness. All these factors complicate rehabilitation procedures [33].
Aspiration is also one of the most dangerous complications of dysphagia. It leads to the respiratory tract obstruction, hypoxia, and aspiration pneumonia [34].
Neurogenic dysphagia treatment is provided in complex with primary disease treatment. Stroke patients need follow-up and treatment and rehabilitation procedures by multidisciplinary team, including nutritional support, exercises and physiotherapy, logopedic correction, pain control treatment, and psychological correction [9, 15, 35].
Screening test shall always be conducted prior to patient feeding. Patient shall take oral medicines only in the presence or with assistance of medical staff. After taking medicines, a patient shall drink small portions of water. For this purpose, a patient shall be in a semi-sitting position or shall lie on a side with elevated chin; a risk of aspiration is lower in these positions. If even a mild swallowing disorder at pharyngeal phase is observed, it is required to act vice versa: to lower the chin. In this position, the tongue root is pushed backwards, and the epiglottis protective position is improved. This movement compensates for delayed initiation of pharyngeal swallow, as it narrows laryngeal inlet and prevents the bolus passing into the respiratory tract [36].
With nasogastric tube feeding, it is necessary to remember that long-period nasogastric feeding may cause such complications as nasopharyngitis, esophagitis, esophageal stricture, and nasopharynx edema. When dysphagia progression occurs or no swallowing function dynamics have been observed for a long period, a gastrostomy tube is used [37].
Dysphagia patient management, depending on the level of consciousness, reasoned contact, use of a tracheostomy tube, and other factors, shall include the following actions related to examination: [38].
Medical history taking;
Swallowing screening test within 3 hours from the moment of admission;
Examination of the oral cavity, teeth and gums, palpation of the regional lymph nodes;
Examination of the mouth and pharyngeal mucosa sensitivity;
Examination of patient’s speech apparatus;
Pharyngeal reflex assessment;
Testing of the reaction to the tracheostomy tube (when required);
Testing of the reaction to sanitation of the area above the tracheal cuff (when required);
Examination of saliva and sputum accumulated in the area above the cuff (when required).
Dysphagia rehabilitation procedures shall include: [39].
Correct selection of patient feeding pattern;
Calculation of the caloric value of products with modified consistency and selection of nutritional formulas ensuring nutritional support;
Selection of food consistency, methods of oral cavity mucosa sensitivity stimulation, swallowing process stimulation and disinhibition, recovery of breath, speech;
Logopedic exercises therapy aimed at correct positioning, stimulation of active swallowing, normal breathing, recovery and enhancement of functional activity of the muscles involved in the process of swallowing and eating: the muscles of mastication, expression, the tongue muscles;
Physiotherapeutic treatment methods; in such case, the procedure of choice for neurogenic dysphagia is the larynx electrical stimulation of swallowing reflex;
Acupuncture;
Surgical correction (if medically required) aimed at creation of alternative ways for patient feeding;
Psychological correction;
Hygienic care of the oral cavity;
Lifestyle correction aimed at correction or creation of the conditions facilitating independent eating process for patients when the function is diminished or impaired;
Teaching relatives on feeding skills and complication prevention.
In order to facilitate the impaired process, various methods of swallowing training and retraining are developed. These methods include strengthening exercises, biofeedback stimulation, temperature and taste stimulation [29, 40].
The following rehabilitation methods are used for neurogenic dysphagia patient rehabilitation:
Preparatory. Preparation of the oral cavity for swallowing (cleaning and moisturizing with a sponge), removal and installation of removable dentures.
“Swallowing enhancement” method. After patient examination, food consistency, which is optimal as for the current moment, is selected. Subsequently, food of various consistency is given, and the volume of food for single administration is increased gradually. At the same time, the required swallow volume, amount of food for single administration, and feeding temperature range are determined. Taste sensation is recovered throughout the rehabilitation period.
Replacement therapy method. Nasogastric tube is installed (percutaneous endoscopic gastrostomy is further applied when required). In several cases, parenteral feeding is used.
Postural method, which involves the selection of a proper posture required for eating.
Training method, which consists of training exercises for strengthening the muscles involved in swallowing process, recovery of swallowing control function.
Complex approach and consistency of dysphagia patients rehabilitation of stroke patients is well-established. Rehabilitation shall be conducted by the members of the multidisciplinary team (MDT) trained on the methods of dysphagia-specific assistance rendering. It is quite important that speech-language pathologists carry out the main works on rehabilitation of neurogenic dysphagia patients. But other members of MDT shall assist speech-language pathologists: physician involved in therapeutic exercises, physiotherapy doctor, as well as patient’s relatives.
Physiotherapy tasks: [41, 42].
Development of adequate afferentation flow by low-frequency electrophonopedic articulation simulation;
Speech and swallowing management program support;
Support of non-functioning muscles tonus to prevent their atrophy;
Prevention of degradation process in the cricoarytenoid joint capsule;
Recovery and support of CNS regulatory function.
Physiotherapy aimed at electrical stimulation of the larynx and tongue root shall be performed during most of the acute stoke period, when it is medically substantiated.
Approximate current parameters for electrical stimulation:
Pulse shape: triangular, with a very slow rise;
Pulse time: 200 ms;
Pause time: 1,000–7,000 ms (depending on patient’s readiness);
Current intensity: 2.5 mA;
Time of procedure execution: 30 min;
Frequency of execution: every day No. 10–15.
The effectiveness of rehabilitation techniques in the treatment of patients after stroke has been shown in a number of studies [4, 10, 18, 29, 37, 38, 39]. Various methods of swallowing as part of complex therapy for dysphagia in stroke have shown high efficiency. The best results of swallowing recovery are shown when using integrated approach, include specialized nutritional mixtures with different densities. Recovery was better in patients with pseudobulbar disorders [16, 35]. Involvement of patient’s relatives and patient motivation plays a significant role in recovery [4, 9].
To perform oral feeding, it is necessary to awaken a patient and seat him/her up before feeding. Make sure a patient stays in a sitting position for 20–30 minutes after the end of oral feeding. First of all, it is necessary to ask patient if he/she wants to take breakfast, lunch and dinner among his/her family members or prefers to eat alone. Anyway, it is necessary to arrange the meal in a comfortable, quiet, and friendly atmosphere, and to put away all unwanted noise sources so that the patient can focus on eating. A patient with difficulty swallowing needs sufficient time for eating. Do not hurry a patient. It is important that a patient feels safe and enjoys the meal while eating.
It is necessary to provide proper positioning of a patient. Correct posture is important to prevent food aspiration while swallowing. When possible, a patient shall sit in an armchair while eating.
When a patient is fed sitting in the armchair, prepare pillows to keep the patient’s position, comfortable table and non-slipping carpet before feeding.
Patient shall sit in so that the feet rest on flat surface or on the floor, the body is in vertical positions, and hands are free. If a patient is able to sit on a chair while eating, he/she can also incline forward and lean upon the table. Body inclined forward will prevent the head throwing back. Patient’s head shall be on the middle line, not inclined rather than thrown back. The neck shall be slightly (not too much) bent forward to prevent aspiration.
When a patient is not able to keep his/her head position by own efforts, it is necessary to support his/her neck and shoulders from the back to prevent the head throwing back and to help a patient to control his/her tongue position. If, however, the patient’s head is inclined too far forward, it is necessary to support his/her chin with the assistant’s hand from below or to use special locking collar for the head support.
To prevent aspiration in the process of swallowing, the “chin-to-chest” posture will help; and for patients with unilateral weakness of the tongue muscles, slight turning of the head towards the impaired side while swallowing will be helpful.
When a patient is fed in the bed (if it is impossible to sit him/her in a bedside chair), the patient shall be kept in a comfortable semi-vertical position. For this purpose, raise a patient slightly to the bedhead, supporting him/her with pillows so that the body rests along the middle line. The head and neck shall be inclined forward slightly. The patient’s knees shall be bent slightly with a cushion/pillow put underneath.
It is necessary to teach a patient to take food and to lift it to his/her mouth with a hand or both hands. When a patient is not able to suck in liquids, teach him/her to drink from a spoon.
Patient shall be advised to take just a small amount of food or liquid at a time. Teach a patient to lift food or liquid to the middle of his/her mouth, not to a side, and to take food in the mouth using lips, not teeth.
It is quite important to attract patient’s attention to the fact that his/her oral cavity shall be absolutely empty after each spoon or piece of food, in order to prevent food accumulation at the side with weak tongue or cheek muscles. Patient shall remove food residuals with a finger after each swallow.
When required, help a patient to clean his/her oral cavity: mucus and saliva accumulated in the mouth shall be removed with wet towel on a regular basis. Remember that the patient’s teeth and dentures shall be cleaned minimum twice per day to maintain the oral cavity hygiene.
Do not give a patient drinks together with solid food. In order to reduce the risk of aspiration, drinks shall be given prior to or immediately after eating.
When a patient meets problems with food swallowing, ask him/her to cough up.
Examine the patient’s oral cavity after eating. As far as a risk of aspiration still exists for a certain period of time after eating, a patient shall stay in vertical position for 30–40 minutes after the meal.
Do not feed a patient if there are any doubts concerning his/her ability to swallow. In such cases, tube feeding is required.
We now know much more about neurogenic dysphagia than we did before. The questions of pathogenesis, clinical picture, diagnostics are well studied. The described approaches in the diagnosis and treatment of neurogenic dysphagia play an important role in clinical practice and are necessary for quality medical care for these patients. Although to date, the level of their evidence remains in the category of cohort studies and expert opinion. This means that we have a lot of interesting work ahead of us and, I hope, important discoveries.
No conflict of interest.
Organizations use insights derived from data to stay competitive. The big data phenomenon has made deriving insights more challenging due to the changing characteristics of the data landscape. The increased volume, variety, and velocity of big data challenge traditional information technology (IT) processes to scale and support big data analytics and data science. Big data is used by organizations as a resource in data science projects to develop new business value and insights. Big data examples include sensor data, images, text, audio, and video data. These data sources can provide new insight opportunities alone and when paired with existing data sources such as organizational data warehouses.
\nData science is a competency that leverages data processing, algorithms, and math to develop insights from data. Data science is a core competency that organizations want to develop to stay competitive. While data science as a competency is growing, the practices to ensure these projects are successful have not kept up with the pace. One primary challenge is using existing software development methodologies to deliver data science projects. Applying traditional software methodologies, such as the waterfall approach, is problematic and has been identified as the one contributing factor for data science project failure; organizations are treating data science projects like other IT projects [1].
\nAccording to Saltz, current research in data science and big data has primarily focused on the use and application of algorithms and generating insights, but little to no research has occurred about tools, methodologies, or frameworks used to deliver data science projects [2]. Saltz outlined that existing tools, methodologies, and frameworks are not mature enough to effectively use in data science and big data projects [2]. This paper focuses on the changing data landscape, the data science process, and identifying best practices to accelerate the data science processing using Python. Python is an interpreter, object-oriented programming language and one of the most popular tools used in big data and data science projects.
\nThe increased use of internet-connected smart devices has changed how organizations use information [3, 4, 5]. The Internet of Things (IoT) creates large amounts of data quickly from sensors embedded in devices, one of the contributing factors in creating the category of big data [5]. The rise of data science is primarily a result of big data, due to the need to analyze data, other than traditional structured data, such as text, machine-generated, and geospatial data [5]. Big data and data science go hand in hand; thus software development approaches used need to consider both [1, 4, 6]. Results of a data science project not only include insight, but also working software that needs to be deployed and supported. Analyzing the characteristics of big data highlights the challenges with traditional software development approaches.
\nThe growth of data impacts the scope of data used in data science and software development. Scope increases project complexity where new technology is used to accommodate more data [3]. Large amounts of unstructured data cannot be easily ingested and processed using a traditional relational database for example.
\nData variety becomes a concern for software development as the types of data sources to be used for development and analysis increase. The variety of data means increasingly complex forms of data such as structured and unstructured data [3, 4, 5]. Traditionally, structured data is created in rows and columns and easily understood; however, unstructured data comes in different forms, levels of details, and without clear metadata complicating the ability to understand and use [3, 4, 5].
\nExamples of data variety include images, IoT sensor data, clickstream, images, and event data. These data sources may be analyzed independently, but often analysis requires data to be integrated. Integrating multiple data sources with different structures increases the complexity of projects.
\nThe speed at which data is created is referred to as velocity. In 2014, Twitter averaged 1 billion tweets every few days [7]. Fresher data results in the ability to analyze new patterns and trends that were not possible before big data. With IoT applications, data that is 15 minutes may be too old for analysis [5]. Data acquisition becomes a challenge as traditional data acquisition focused on extract, transformation, and load (ETL) of data. Increased velocity changes the order where data is loaded first, then analyzed, otherwise known as extract, load, and then transformation (ELT) [3, 4, 5].
\nVeracity refers to how accurate data is and how well the data is understood. Big data is not clearly analyzed prior to ingestion due to the volume and speed of creation, which results in data that may have credibility and reliability problems. Often metadata for big data sources do not exist. These challenges increase the complexity of deriving insights from big data sources [3, 4, 5].
\nSoftware development lifecycles have traditionally focused on requirements which drove the design, leveraging the design to develop software, testing, and then deploying the software. Projects using big data change the order in which these phases occur. Big data sources are ingested, stored, and explored first, and then requirements are determined which changes the traditional order of activities for project delivery. Using the traditional software development lifecycle for projects that include big data has failed further supporting that projects using big data need to adjust project approaches [1].
\nAccording to Mayer-Schönberger and Cukier, big data changes how the world interacts and means a disruption to what was considered normal. This disruption also means disruption to the software development processes that create the software that derives knowledge and value from data. Big data results in changes to IT processes, technologies, and people. One greater reliance observed is that data scientists need to address the complexity introduced with big data and help derive the knowledge from data [3, 4].
\nAccording to Saltz, most data science processes focus on the tasks that need to be completed in data science such as the techniques to acquire and analyze data [2]. Saltz analyzed different data science approaches and found that most outlined the steps as data acquisition, cleansing, transformation, integration, modeling, analysis, and deployment [2]. The data science approaches are task-oriented, and no real evolution of the process had occurred since the cross-industry standard process for data mining (CRISP-DM) was introduced in the 1990s [2].
\nThe most commonly used data mining process is CRISP-DM which is a process that conceptually described the stages used in data mining. Originally created to support data mining projects, it has been adapted by data scientists. There are six stages in the CRISP-DM process which are presented sequentially, but iteration is expected:
Business understanding
Data understanding
Data preparation
Modeling
Evaluation
Deployment
The start of the process, business understanding, focuses on the business value of the project. Once requirements and objectives are understood, the problem definition is created. Data science projects start with a problem to be addressed or a question to be explored. Tools to support identifying the problem include diagrams such as a decision model such as a fishbone diagram [8].
\nOnce the problem to be addressed is identified, the next focus is on data source identification and collection. Data source identification includes identifying the sources, such as a transactional processing system, and the attributes needed to address the problem. Problems may involve several different data sources, which means data integration work is likely. After integration, data is profiled and statistically analyzed to determine quality, demographics, relationships between variables, and distribution. The outcome of data understanding is a definition of how the data can be used. The data understanding stage is often referred to as exploratory data analysis (EDA) [8].
\nThe goal of data preparation is to create the data that is to be used in the modeling stage. Input from data understanding is used to determine the final set of attributes, often referred to as features that will be used in the model. Preparation includes integration, cleansing, and deriving of new attributes. Data preparation is iterative as the training and testing of the model may require new or changed data. The result of data preparation is the data set to be used as input into the modeling stage [8].
\nAs part of the modeling stage, different techniques and algorithms are used to determine the best model. Modeling goes through cycles of testing and training, where the data scientist adjusts parameters to produce the best outcomes. It may be necessary to return to data understanding and preparation stages if the model performance is not acceptable [8].
\nModel evaluation is determined based on the overall fit to the problem statement and business objectives. Evaluation is conducted by analyzing error rates, variance, and bias of the model. Often models using different techniques are compared to determine the best performing one. Once the best performing model is identified, a formal review is conducted to move to model deployment [8].
\nThe deployment stage is often overlooked and unplanned for but important as this is where business value is realized. Deployment focuses on a working software model that can be supported and executed on a regular frequency. Once deployed, models are monitored for performance as model accuracy will degrade because of organizational change and time. Models are often retrained once this occurs [8].
\nThe description provided of CRISP-DM is a summary and does not highlight the granularity of effort needed for successful data science outcomes. Many organizations use CRISP-DM as a framework and add steps to each stage for software development teams to follow. There was an effort to create CRISP-DM 2.0 in 2007, but there is no new research or activity in this area [2].
\nLandset et al. outlined that big data has caused IT departments to rethink how data is processed and the use of data science software [9]. Choosing the right processing framework and data science software can be challenging due to data science project requirements and that data complexity might require more than a single solution [9]. Additionally, tools available to conduct data science are many with partial functionality, limited ability to handle big data, and integrate into big data processing platforms, which contributes to the fragmentation and complexity of the data science and big data technology solutions.
\nLandset et al. called out that no single tool or framework covers all of the requirements of a data science project [9]. Data scientists and computer engineers need tools that support computing performance, usability, machine learning algorithm breadth, and portability. To support these needs, data scientists and computer engineers have turned to open-source tools to address the variety of requirements of data science projects. Open-source technology categories include data processing platforms and engines and machine learning tools. The Hadoop ecosystem is commonly used to address the data processing aspect of big data and data science [9]. The Hadoop ecosystem includes workflow, data collection, storage, and processing, for example. While there are many open-source machine learning tools, Python has become one of the leading programming languages used in data science as well as R and Mahout [9].
\nTo best understand what the best practices are in big data and data science projects, it is beneficial to highlight some of the challenges. Some of the challenges highlighted so far include lack of a detailed software development methodology and the characteristics of big data. Other challenges include that many data science projects are managed as a single project and the focus on reproducibility, collaboration, and communication is overlooked [10].
\nAndrejevic argued that big data changes how businesses and customers interact which disrupts normal business processes [11]. This disruption also means disruption to the software development processes used to create the data science models that derive data insights [11]. When organizations leverage big data, this results in changes to IT processes, technologies, and people [11]. One change observed is that data scientists are challenged to handle all activities related to the data science process including all the data wrangling activities which can consume most of the project timeline [3, 11]. Traditional IT projects normally have defined roles and activities involving several team members [3, 11].
\nMayer-Schönberger and Cukier highlighted the impact of big data on organizations citing that the volume, variety, and velocity characteristics make data science and big data projects difficult to deliver using traditional IT project approaches [12]. Volume challenges how much data is ingested and consumed, variety highlights the need to support different data structures, and velocity outlines the need to ingest data as soon as it is created [12]. The need for nontraditional (non-relational databases and storage systems) data processing platforms and software that can handle big data is the reason why traditional IT processes are unsuited for data science and big data projects [12].
\nSome of the challenges in delivering data science and big data projects include having clear business objectives, dealing with volume, identifying what data to use, understanding opportunities to store and process data, and having clear privacy and security requirements [13]. Mousannif et al. proposed a framework for a big data project workflow that included planning, implementation, and post-implementation phases [13]. Mousannif et al. highlighted a need to focus on reproducibility of data ingestion, processing, and storage to expedite future big data projects [13].
\nLowndes et al. proposed that data science and big data projects can be accelerated by focusing on reproducibility, transparency, and collaboration by implementing new processes leveraging open-source tools [10]. Lowndes et al. published the results of implementing new processes to accelerate data science for the Ocean Health Index (OHI) project which is repeated yearly to address the change in global ocean health [10]. New processes were implemented in the categories of reproducibility, communication, and collaboration and broken down further into specific tasks [10].
\nLowndes et al. outlined that the following areas need to be addressed for reproducibility: data preparation, modeling, version control, and organization [10]. Data preparation includes creating and leveraging common coding routines to sort, cleanse, transform, and format data [10]. Modeling focused on standardizing to a common programming language to ensure all were using the same algorithm methods which resulted in reduced iterations on validating results [10]. Version control ensured that the team was treating the data science process as a software development process where code was tracked and change management was put in place to improve software reusability [10]. Organization was addressed through leveraging in-code documentation standards, file naming standards, and treating each data science project as a single set of common code by implementing the project function in the programming language [10].
\nLowndes et al. proposed that collaboration be improved by having centralized coding repositories, common workflows for promoting code, and a centralized repository for communication [10]. Git, a widely used cloud-based version control system, was used as the common coding repository, and a Wiki was used for documenting projects [10]. Having a common project management approach was also highlighted as a benefit [10].
\nLowndes et al. focused on improved team communication and effectiveness through sharing data and methods [10]. The focus was on not redoing work if the work was already completed [10]. Data sharing covered centralizing cleansed data sets for reuse and creating common data pipelines to be used for data science projects (like the OHI project) [10]. Since the OHI project is completed yearly, much of the software development work could be leveraged from the prior year in place of starting the project from scratch each time [10].
\nThere are several gaps and lack of maturity in the data science process as outlined by the research of Landset et al., Mayer-Schönberger and Cukier, Mousannif et al., and Saltz. Based on these gaps, best practices will be proposed to accelerate the data science process leveraging Python. While other open-source programming languages could be used in the same manner as suggested in the next section, the choice of Python is not meant to recommend that Python is the only choice or best choice to use in data science projects. Python was chosen due to its popularity, performance, and portability capabilities in the data science and big data space.
\nPeople, process, and technology are contributing factors to data science complexity. Data scientists are expected to multiskilled resources knowing statistics, big data platforms, pipeline development, and deep learning neural networks. The primary process leveraged for data science is CRISP-DM which has not really changed since it was introduced in the 1990s. Lastly, there is so much available technology to use in data science it boggles the mind. While these problems will take time to solve, there are some best practices that can be leveraged to make data science less complex and more scalable in organizations. Best practices will be addressed in general as well as with Python.
\nData science initiatives tend to be done as independent efforts or one-off projects. Data scientists often work as the project manager, data wrangler, software developer, data engineer, tester, and do the data science as well. Data scientists cannot be effective assuming all these roles. With data wrangling (cleansing, transformation, formatting, etc.) taking up more than half the project, the data engineer has emerged as a key role in supporting the data science process. Assign a data engineer to handle the data wrangling, and let the data scientist focus on data science. Additionally, data science initiatives are projects. Ensure the data science initiative has a lead who can remove barriers, deal with stakeholders and get the required subject matter experts to support the data science project. Collaboration is key in making progress and valuable data science results [10, 14].
\nPython is an interpreter, object-oriented programming language introduced in 1991 and has emerged as one of the leading open-source data science tools used by data scientists [15]. Python has become a leading data science tools for several reasons. As an open-source tool, Python is freely available and modifiable, keeping costs low and promoting rich features through the open-source community [6].
\nPython is easy to learn. Although it is a programming language, the syntax is easy to adopt, especially by programmers, and through studies, the learning cycle tends to be shorter than a comparable data science tool—R. R is another open-source programming environment specializing in statistical computing and graphics. Both tools are comparable in many areas; however, Python has emerged as being more scalable and portable [15].
\nScalability and portability are important in the data science community. With big data characteristics such as volume, velocity, and variety, data science tools need to be robust. Scalability refers to the ability to handle growing computing requirements, and portability is the ability to easily run on multiple computing platforms. Python has libraries and packages that support fast computing, and it runs on the common operation systems such as Linux, Windows, Unix, and macOS [15]. While Python is discussed as a data science tool, it is also a programming language used in the development of applications.
\nTeam collaboration was mentioned as a best practice in data science; however, collaboration is inherent within open-source communities. Python has a deep reach in the data science community with data scientists contributing to creating new libraries and code routines. In addition, the tech companies often choose Python to release new functionality first. Google, for example, released its deep learning package TensorFlow first in Python, setting a trend [14, 15]. The Python community provides an avenue for new data scientist or even seasoned ones to find solutions to data science problems. Communities often exchange questions and answers on websites such as Stack Overflow or share code on public repositories such as Git [14, 15].
\nThrough the data science process, data scientists must visualize data relationships, and Python also supports robust visualization options [14]. Libraries exist to support multiple graphing options such as charts, graphs, and interactive plots [15]. Libraries are collections of methods and functions that data scientists can leverage without having to write new code.
\nThe most significant factor supporting Python as the leading open-source data science tool is the number of libraries available for data scientists. These libraries, as mentioned, provide prepackaged methods and functions, and several libraries available in Python have enabled data scientists to leverage the latest machine learning algorithms (such as TensorFlow), manipulate data easily, and create data science models that perform and scale [15].
\nWhile there are several reasons why Python is popular with data scientists, other tools such as R, Scala, and Ruby are available that provide similar functionality as Python. The intent of the following section is to show how functionality in tools can accelerate the data science process. The scope of this research is not to compare Python to other languages, but to illustrate how to accelerate the process of data science.
\nPython has a plethora of libraries available for use, but there are a few that can accelerate the data science process and have become the set of tools that data scientists leverage. Libraries that are heavily used by the data science community include NumPy, SciPy, pandas, Matplotlib, and Scikit-learn. The “Py” at the end of Python libraries is pronounced “Pie” [15].
\nNumPy is a library created by Travis Oliphant that is leveraged by most of the other data science libraries. NumPy provides a large set of mathematical functions that operate on multidimensional array data structures. Arrays allow data to be stored in blocks across multiple dimensions which enable mathematical operations to be applied to matrices and vectors. Arrays support vectorization which allows fast math operations supporting scalability and performance which is valuable in solving data science problems [15].
\nSciPy is another library created by Oliphant, Peterson, and Jones. SciPy leverages NumPy functionality and includes additional algorithms, matrix processing, and image processing; SciPy and NumPy are often paired together in data science where NumPy provides enhanced performance and SciPy an extended library of mathematical functions and advanced processing on data types [15].
\nPandas focuses on object data structures. Most working in data management default to thinking of data in rows and columns. Pandas handles processing of data tables with different data types (very similar to a relational database table) as well as time series. Pandas enables easy data manipulation without the constraints of a relational database. Slicing, dicing, dropping, and adding elements, reshaping, joining, and aggregating data is much easier, leveraging the object data structures called dataframes. Dataframes are commonly used in R as well [15].
\nMatplotlib is another library that leverages NumPy. Matplotlib is highly used for charting, graphing, and time series analysis, especially in the data understanding and preparation phases of the data science process [15].
\nScikit-learn (also referred to as Sklearn) contains the core data science method and functions for Python [15]. Scikit-learn contains methods and functions that cover supervised and unsupervised algorithms, model selection, model validation, and final evaluation of performance. Several modules exist that data scientists should be aware of such as preprocessing and feature extraction that contribute to accelerating the data science project. Specific examples will be addressed as part of the data science process. The stages of CRISP-DM will be used to address when best practices get leveraged. Although CRISP-DM starts with business understanding, the application of Python typically starts in data understanding.
\nData understanding focuses on the data collection and analysis of the data sources to be used in the data science project. In data understanding, the three areas that can help accelerate the data science process are data profiling, data visualization, and data preprocessing. Data profiling is the process of gathering statistics and demographics about data sets. Profiling provides the ability to assess data quality and understand how attributes are populated. Library pandas_profile can be leveraged to complete data profiling. Data profiling includes data set info, variable types, descriptive statistics, and correlations between numeric variables [15]. An example of the output is listed in Figures 1 and 2.
\nThe data set information and variables types can be created using the pandas_profile library.
Data visualization using the Matplotlib and Seaborn libraries is highly effective in the data understanding stage. Both libraries support multiple charts and graphs to visualize data relationships. Using the Matplotlib library, a correlation heat map can be created to demonstrate correlations within a data set.
Data preprocessing focuses on scaling, normalization, binarization, and one hot encoding. Scaling is applied when the values of potential features have a large variance between random variables. Data normalization is used to adjust the values in a feature vector so that they can be measured on a common scale. Binarization is used to convert a numerical feature vector into a binary vector such as true or false. One hot encoding is a process by which categorical variables are converted into a form that could be provided enables better prediction by algorithms. One hot encoding determines feature frequency, identifies the total number of distinct values, and then uses a one-of-k scheme to encode the values [15, 16]. Preprocessing data accelerates the data science process by minimizing the number of iterations in the modeling stage. The Scikit-learn library has methods for scaling, normalization, binarization, and one hot encoding such as sklearn.preprocessing.
\nAutomating feature selection is the process of reducing the number of input variables used by predictive models. Feature selection can be a time-consuming process for data scientist. Automatically selecting those features that are most useful or most relevant for use in the analytical problem accelerates the data science process. Scikit-learn has multiple methods that support overfitting, improve accuracy, and reduce the training time of models such as sklearn.feature_extraction [15, 16].
\nWhen data science is done as an independent effort, much of the work in the data understanding and preparation phase of a data science project is done without the end-state vision. Data understanding and preparation mimics many of the development activities that are used to develop data pipelines. The activities to develop the data pipeline include acquiring the data, exploring the data for deep understanding and value determining, integration, transformation and formatting to get the data ready to be consumed by the model. Why not develop the pipeline as part of the project? At the end of the data science project, the expectation is a working data science model. The model will have no value if the data pipeline to produce the input is not available and ready for production deployment.
\nWith data understanding and preparation taking more than 50% of the project timeline, data scientists need to have access to the correct data in the correct format. Not only will developing the pipeline as part of the project prepare the data for production consumption, but it may also be leveraged for other data science projects.
\nAnother area to review is existing ETL or pipelines in the data warehouse environment. Data science leverages all kinds of data, and often models use existing transactional data enriched with big data sources. Reusing existing ETL or pipelines promotes consistency and reduces the development work in the data science project. Pipelines in Python are an easy way to automate common and repeatable steps such as the preprocessing steps. One pipeline library that can be leveraged in Python is Luigi.
\nOne of the challenges with data pipelines is pipelines contain components that need to be linked together for processing. Many of the components in a pipeline support long-running job, streaming of data, and running machine algorithms that may fail. Luigi, the pipeline library, can help link many of these pipeline components together or provide the workflow management so that the components can be run and managed as a single pipeline. Workflow management handles the dependencies between the components. As part of the Luigi library, templates are provided that provide support for long-running jobs in Python. Luigi also contains file system abstractions for the Hadoop File System (HDFS) which ensures the pipeline will not fail holding incomplete data. Luigi also includes a Visualizer page that provides a visual status of the pipeline and provides a visual graph of the pipeline [15].
\nScikit-learn is the library that is leveraged for modeling and evaluation. The data scientist will choose the modeling approach or algorithm to be used for the data science problem; however, tuning the model and choosing the best performing model can be a challenge. Scikit-learn has several functions and methods that can be leveraged to expedite this stage of the data science process. One example is train_test_split which supports the random creation of training and test files [15].
\nTraining and test files are used to “fit” the model, and through this process different settings or hyperparameters are tuned to improve the accuracy of the model. This causes overfitting where performance is no longer generalized in the model. The approach to avoid overfitting is called cross validation. A function in Scikit-learn can be leveraged called cross_validate where another data is held out, referred to as the validation data set. The cross_validate function can be used where the training is completed, but evaluation is completed on the validation set. Once appropriate accuracy is achieved, the test set is used for final evaluation [15].
\nModel and feature selection can also be time-consuming activities in the data science process. Scikit-learn can also be leveraged to evaluate an algorithm’s performance as well as to select the best fit parameters using cross validation. Another method that can be used is GridSearchCV. GridSearchCV is used to wrap an estimator, where it selects parameters from training file to maximize the score; GridSearchCV can be used as part of a pipeline to combine several transform components and estimators to create a new estimator as well. Scikit-learn offers many options for data processing, model selection, and model validation. It also includes a complete set of methods to support many different modeling algorithms [15].
\nSoftware engineering principles should be applied to both the data pipeline and the data science model. The likelihood of using big data where volume, velocity, and variety need to be addressed means that the data science project scope is not only to produce working software, but also software that has quality and can scale.
\nThe characteristics of data science software quality specifically focus on supportability, reusability, reliability, portability, and scalability. Supportability focuses on the ability of IT operations to address maintenance and failure issues. Reusability is the ease with which software can be reused other data science projects. Reliability is the frequency and criticality of software failure, where failure is an unacceptable behavior occurring under permissible operating conditions. Portability is the ease with which software can be used on computer configurations other than its current one. Last, scalability is the ability to handle performance demand without having to re-architect the software. All these characteristics should be applied to data science pipelines and models [10].
\nScalability tends to be the largest challenge with machine learning algorithms. The expectation with machine learning algorithms is that as data increases, the algorithm addresses the volumes in an efficient way where the run time increases linearly. Machine learning algorithms that do not scale increase running time exponentially or simply stop running. One way to address the scaling problem is to use out-of-core learning [15].
\nOut-of-core learning uses a set of algorithms where data that does not fit into memory can be stored in another location such as a repository or disk. Scikit-learn includes functionality that supports the streaming of data from storage and iterative learning [15].
\nAs mentioned prior, there are many tools available to data scientists, and the landscape is evolving daily [9]. For this research, Python was chosen due to popularity, and other open-source languages may have similar capabilities. Several different Python libraries were recommended that could accelerate the data science process. A summary of these libraries follows.
\nThere are five core libraries available in Python that accelerate performance in the data science process. NumPy provides a large set of mathematical functions that operate on multidimensional array data structures. SciPy, which is often paired with NumPy, includes additional algorithms, matrix processing, and image processing functionality. Pandas enables the use of object data structures in rows and columns. Matplotlib is a visualization library which pairs well with NumPy. Scikit-learn contains many different machine learning algorithms.
\nWhile most of these core libraries will be used in the phases of data science, there are several packages in each library that help accelerate some of the challenges with the data science process. In the data understanding stage, both Matplotlib and Seaborn support many different types of visualizations. Pandas_profile, part of the Pandas library, quickly completed the profiling process exposing data set demographics.
\nData preparation tends to make time which could be better spent on data science modeling. Leveraging the packages of preprocessing and feature_extraction as part of Scikit-learn helps reduce some of the work. Preprocessing quickly handles scaling, normalization, and binarization of variables. Feature_extraction evaluates different features for value to reduce training time. Focusing on building a reusable pipeline in the data preparation stage can accelerate the deployment stage. Luigi is a pipeline package that can automate code modules, create workflow, and help with support of data pipelines once in production.
\nAs part of modeling and evaluation, packages from Scikit-learn are used to automate training and testing data set creation using train_test_split. Both the cross-validate and GridSearchCV can be used to evaluate models and compare models to get the final recommendation. Once the model is ready for deployment, the use of out-of-core learning can be used to maximize the use of computing resources in production as data science models tend to heavily use computing power.
\nCurrent research in data science and big data has primarily focused on the use and application of algorithms and generating insights, but little to no research has occurred about tools, methodologies, or frameworks used to deliver data science projects. Analyzing the characteristics (volume, variety, and velocity) of big data highlights the challenges with traditional software development approaches. Results of a data science project not only include insight, but also working software that needs to be deployed and supported; thus software engineering practices should not be avoided. Leveraging tools such as Python can help accelerate the data science process. Python has become a leading data science tool for several reasons, primarily due to the functionality that is created quickly to address the data science community needs.
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All published Book Chapters are licensed under a Creative Commons Attribution 3.0 Unported License. Monographs are licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license granted to all others. Our Copyright Policy aims to guarantee that original material is published while at the same time giving significant freedom to our Authors. IntechOpen upholds a flexible Copyright Policy meaning that there is no copyright transfer to the publisher and Authors hold exclusive copyright to their work.
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\n\n\n\nIntechOpen is committed to disseminating high-quality scientific research in a manner that exemplifies the best practice in scholarly publishing. IntechOpen is an official member of the Committee on Publication Ethics (COPE), which advocates the maintenance of the highest ethical standards for all parties involved in the act of publishing, including Authors, Academic Editors of the book, Peer Reviewers, the publisher and Societies, where applicable.
\n\nIn line with publication ethics practices recommended by COPE, ICMJE, and other similar organizations, IntechOpen's contributing Authors, Academic Editors, and Peer Reviewers are required to declare fully all possible conflicts of interest.
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\n\n\n\nThe Internet has changed the dynamics of scholarly communication and publishing which is why we find it necessary to clearly indicate our stance on what we consider to be a published scientific work. A significant number of working papers, early drafts, and similar works in progress are shared openly online between members of the scientific community. It has become common practice for researchers to announce their work on a personal website or a blog in order to gather comments and suggestions from other researchers. Such works and online postings are ‘published’ in the sense that they are made publicly available, but this does not mean that if submitted for publication by IntechOpen they are not original works. We differentiate between reviewed and non-reviewed works when determining whether a work is original and has been published in a scholarly sense or not.
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