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",isbn:"978-1-83768-400-7",printIsbn:"978-1-83768-399-4",pdfIsbn:"978-1-83768-401-4",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,isNomenclature:!1,hash:"3e168136bc7435be0c6bbe1d7adec1f4",bookSignature:"Prof. Marwa Zakaria, Prof. Tamer Hassan and Prof. Laila Sherief",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/12194.jpg",keywords:"Beta Thalassemia Major, Transfusion Dependent Beta-Thalassemia, Microcytic Hypochromic Anemia, Mutations, Beta Thalassemia Intermedia, Non-transfusion Dependent Thalassemia, Hb E Disease, Alpha Thalassemia, Genetic Counseling, Newborn Screening, Prenatal Diagnosis, Gene Therapy",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"June 14th 2022",dateEndSecondStepPublish:"July 12th 2022",dateEndThirdStepPublish:"September 10th 2022",dateEndFourthStepPublish:"November 29th 2022",dateEndFifthStepPublish:"January 28th 2023",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"a month",secondStepPassed:!0,areRegistrationsClosed:!1,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"Dr. Marwa Zakaria completed her post-graduate training in Pediatric Nutrition at Boston University School of Medicine, USA. She is an Associate Professor and senior consultant of Pediatrics in the Faculty of Medicine at Zagazig University and a member of the International Society of Pediatric Oncology (SIOP), the European Hematology Association (EHA), and the Egyptian Society of Hematology.",coeditorOneBiosketch:"Professor at Zagazig University and an active member at EHA, SIOP, HAA, and ESPHO. Dr. Hassan is a guest speaker at numerous pediatric oncology and hematology meetings and he had over 50 international research publications in Pediatrics and Pediatric Hematology and Oncology.",coeditorTwoBiosketch:"Professor at Zagazig University, president of Sharkia Thalassemia Association, and member of the Egyptian national guidelines committee (NEGC) for evidence-based clinical practice. Prof. Sherief has over 50 international publications and many national publications and is an editorial board member in 17 international journals and Peer Reviewer for more than 38 international journals.",coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"187545",title:"Prof.",name:"Marwa",middleName:null,surname:"Zakaria",slug:"marwa-zakaria",fullName:"Marwa Zakaria",profilePictureURL:"https://mts.intechopen.com/storage/users/187545/images/system/187545.png",biography:"Prof. Marwa Zakaria is an Associate Professor of Pediatrics and Pediatric Hematology and Oncology, Pediatric Department, Zagazig University, Egypt. She is an active member of the International Society of Pediatric Oncology (SIOP), European Hematology Association (EHA), and Egyptian Society of Pediatric Hematology and Oncology (ESPHO). She has participated in several professional trainings and workshops, including ICH GCP online training, EHA Master Class and Bite-size Master Class, and training from the Society of Neuro-Oncology (SNO). She completed a postgraduate training program in Pediatric Nutrition at the School of Medicine, Boston University, USA, in 2017. She completed several international preceptorships, including a thalassemia preceptorship and a hemophilia preceptorship. Dr. Zakaria is the recipient of a 2018 award from SIOP, and scholarships from EHA-HOPE in 2017 and 2018. She has participated in many international and national pediatric and hematology conferences, where she has also been a guest speaker. She has more than forty international research publications in pediatrics and pediatric hematology and oncology to her credit. She has edited three books and five book chapters. 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He is an active member of the European Hematology Association (EHA), International Society of Pediatric Oncology (SIOP), and Egyptian Society of Pediatric Hematology and Oncology (ESPHO), and has attended numerous national and international pediatric and hematology conferences held by these organizations and others. He has been a guest speaker at numerous pediatric oncology and hematology meetings and has published more than fifty international research publications in pediatrics and pediatric hematology and oncology. Dr. Hassan has edited two books and authored four book chapters. He has participated in many professional trainings and workshops. He received international scholarships from EHA-HOPE Cairo in 2017 and 2018, and an award from SIOP in 2016. He has completed several international preceptorships, including a hemophilia preceptorship at Saint Luc Hospital, Brussels, Belgium, and an immune-thrombocytopenia (ITP) preceptorship at Dmitry Rogachev National Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, Russia. Dr. Hassan is an editor and reviewer for many journals, including Hemophilia, Medicine, Oncology Letters, Child Neurology, and more. He was a primary investigator in four international clinical trials and a sub-investigator for ten others.",institutionString:"Zagazig University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"5",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Zagazig University",institutionURL:null,country:{name:"Egypt"}}},coeditorTwo:{id:"110940",title:"Prof.",name:"Laila",middleName:null,surname:"Sherief",slug:"laila-sherief",fullName:"Laila Sherief",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bS1HqQAK/Profile_Picture_2022-05-19T09:40:38.jpg",biography:"Professor Laila Sherief has been a long-serving member of the Zagazig University community in Egypt. She first graduated with honours from the Zagazig University and then went on to do her internship and residency there before becoming a lecturer, an Associate Professor then a Professor in Paediatric in the Faculty of Medicine. Prof. Sherief has published extensively in national/international medical journals and at medical conferences. She has over 50 international publications and many national publications and acts as a Peer Reviewer for more than 38 international journals, including Pediatric Hematology and Oncology, Pediatrics International, Journal of Coagulation & fibrinolysis, Medicine, BMC Endocrinal Disorders, Transfusion Medicine and Cancer Chemotherapy & Pharmacology. She is editorial board member in 17 international journals as BMC Pediatric, Frontiers in Genetics, Hematology case reports, Archives of hematology case reports and reviews, and Annals of Medical case reports. She supervised 83 master and MD thesis in Pediatric, Pediatric Hematology & Oncology and Clinical pathology\r\nProf. Sherief frequently attends national and international conferences and maintains memberships in many professional societies as International Society of Paediatric Oncology (SIOP), International Society of Haemostatis and Thrombosis (ISTH)., Egyptian Society of Pediatric Haematology & Oncology (ESPHO) and Egyptian Societies of thalassemia. She is the president of Sharkia thalassemia Association, Egypt, and member of the Egyptian national guidelines committee (NEGC) for evidence- based clinical practice. 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They are an important cause of neonatal mortality, children morbidity, and long-term disability and so constitute a serious public health and epidemiological problem [1]. A significant proportion of CAs is detected before birth by routine ultrasound examination and other screening techniques [2]. Prenatal identification of CAs can have significant emotional and psychological consequences for parents and affected babies [2]. Therefore, it is imperative to make the right diagnosis as soon and as accurate as possible. Genetic testing in prenatal period is an area with very sensitive and ethically challenging situations [3]. Determining a genetic diagnosis prenatally permits parents to make informative reproductive decisions and to be counseled about possible fetal outcomes [4]. It adds important information for current pregnancy in the terms of full phenotype beyond ultrasonographically detected abnormalities on the one hand and postnatal prognosis on the other [3]. Consequently, it is important to use appropriate genetic testing approach to obtain a specific diagnosis.
In recent years new diagnostic algorithms for prenatal genetic testing are being developed with the advent of new genomic technologies, like molecular karyotyping (comparative genomic hybridization) and next-generation sequencing (NGS). These technologies offer testing options that exceed conventional karyotyping of the fetus and provide better diagnostic yield. Despite the evidence of important additional diagnostic yield of new technologies in the etiology of CAs, they have not been systematically implemented in clinical prenatal diagnostic algorithms in several national healthcare systems.
A CA is defined as any structural anomaly present at birth. Major CAs are anomalies that have medical, surgical, or cosmetic significance and occur in about 2–3% of liveborn and 20% of stillborn infants [1]. Thus, CAs are more prevalent that many chronic childhood diseases, such as autism, pediatric cancers, and type 1 diabetes, are an important cause of neonatal mortality, children morbidity, and long-term disability [1, 5]. Therefore, they represent an important public health and epidemiological problem.
CAs can be isolated or present in a characteristic pattern affecting one or more organ systems.
The overall prevalence of most major CAs does not vary much across ethnic groups [6, 7]. However, the risk for different types of anomalies is variable and may be related to genetic susceptibilities and also to cultural and social differences that can influence exposures (e.g., neural tube defects due to a dietary deficiency of folic acid) [6, 7]. The prevalence of most major birth defects over time has remained constant, but some have shown a significant increase such as gastroschisis [6, 7].
The etiology of CAs is complex. CAs can be the result of genetic factors, environmental factors, or a combination of both [8, 9], although the underlying etiology often remains unknown. It is estimated that genetic factors represent an important cause of CAs and may result from different genetic mechanisms: the most common are aneuploidies, deletions, and duplications of DNA segments (collectively known as CNV), and single gene disorders [8, 9]. Some disorders have an epigenetic basis; genes can be silenced or activated by modifications that may depend on the parent of origin or other influences [4].
With the traditional diagnostic approach using conventional karyotyping and direct molecular genetic testing, the etiology of CAs remains undiagnosed in 65–70%, including cases with multifactorial or polygenic etiology (e.g., isolated neural tube defects or cleft palate), 15–25% is thought to be genetic or genomic—chromosomal in 10–15% and monogenic in 10%—and 10% is thought to be due to environmental factors [4]. Although most CAs are isolated and sporadic; the genetic contribution has long been recognized, and specific genes involved are increasingly being identified. However, the majority of isolated CAs are thought to be caused by a complex interplay of genetic and environmental factors and follow the so-called multifactorial or polygenic inheritance [4, 8, 9]. On the other hand, multiple CAs are often part of a syndrome, of chromosomal or monogenic etiology.
In the prenatal settings, the frequency of chromosomal abnormalities depends on many factors: the gestational age, type of the anomaly, the number of anomalies, and the combination of anomalies identified [10]. In retrospective series, chromosomal abnormalities were found in 2–18% of cases when isolated and in 18–35% when multiple CAs were prenatally detected on ultrasound [10, 11]. Chromosomal abnormalities are more common in spontaneous abortions (50%) than in stillbirths (6–13%) [12].
Due to the frequency, morbidity and lethality CAs pose an important public healthcare problem. For the planning of preventive healthcare measures, it is very important to determine the epidemiology and etiology of CAs.
The following chapters give an overview of the conventional genetic diagnostic approach and the use of new genomic technologies in the prenatal genetic diagnosis of CAs.
Currently fetal karyotyping and targeted genetic testing are still most commonly used in the genetic diagnostic evaluation of high-risk pregnancies including morphologically abnormal fetus detected by ultrasound examination and positive result of the screening test or due to parental chromosome rearrangement or genetic disorder with a known pathogenic variant. Despite the recent shift of genetic diagnostics toward genomic approach, the conventional diagnostic approach encompassing the karyotyping and targeted molecular genetic testing is worth noting.
To identify possible genetic causes underlying ultrasonographically detected CAs or positive result of the screening test (nuchal translucency, combined screening test, triple/quadruple hormone test), a full chromosome analysis has been widely used and regarded as the gold standard from the late 1990s to about 2010. A diagnostic yield of classical karyotype is more than 18% in fetuses presenting with isolated or multiple CAs [10].
As chromosome analysis is subjective and experience-dependent [13], its insufficiencies have been complemented by fluorescent in situ hybridization (FISH) analysis, where a DNA-specific fluorescent probe is hybridized to the complementary sequence in a cell preparation. In contrast to conventional chromosome analysis, FISH can be used to study cultured or direct cell preparations (metaphase/interphase FISH). FISH allows for the detection of repetitive regions like satellites in the acrocentric chromosome or variable length of pericentromeric heterochromatin not covered by genomic methods [13]. Currently, FISH analysis is a valuable tool for the identification of the origin of the marker chromosome composed of heterochromatin [14] and complex chromosome rearrangements and mechanism of the chromosome rearrangement [15, 16]. Locus-specific fluorescent probes detect subtelomere and interstitial submicroscopic chromosomal rearrangements associated with clinically recognizable phenotypes with diagnostic yield of 3–6% for chromosome abnormalities [17]. However, screening with FISH for tandem duplications seems to be of limited value.
FISH is a more targeted approach, because it requires prior knowledge of chromosome region of interest but has limited utility as a first-tier investigation [18].
Chromosome analysis may also be considered as a quantitative method, which can accurately detect the proportion of the mosaicism. As many as 0.16% of cases with low-level yet clinically significant chromosome mosaicism would be undetected by array CGH method [18]. In addition, in mosaic cases with two abnormal cell lines resulting in a no-net gain or loss (i.e., 45,X/47,XXX) array, CGH would return to normal result [18].
Different types of mosaicism can be found in prenatal diagnostics, like confined placental mosaicism (CPM), true fetal mosaicism, and clonal expansion because of in vitro cultivation [20]. CPM is found in about 1–2% of chorionic villi samples, and certain chromosome trisomies are typically found, like trisomy of chromosome 2, 7, or 16 [19].
Over the last decades, chromosome analysis has been the cornerstone in prenatal genetic diagnosis. In fetuses with multiple CA, there is a chance of more than 18% to detect a chromosomal abnormality [11], while a chance for a chromosomal aberration in cases with an isolated CA is not well determined [20, 21].
There are some pros to why fetal karyotyping remains in the everyday genetic practice. The chromosome analysis assesses the number (aneuploidies) and the structure of chromosomes (chromosome rearrangements) in a single assay (i.e., free trisomy 21 versus unbalanced Robertsonian translocation involving chromosome 21 or balanced reciprocal translocations) [18].
However, because of its low resolution, the need for cell cultivation, which is time-consuming and artifact prone, and the inability to detect complex abnormalities, the chromosome analysis is placed behind other high-throughput genomic investigations [19].
Currently, karyotyping remains the investigation of choice for low-risk pregnancies with normal fetal morphology, like advanced maternal age with increased risk for trisomy 21 [22].
As mentioned above, about 10% of CAs is thought to be associated with monogenic disease [4]. Ultrasound examination can detect many fetal structural abnormalities, from the early anatomic survey in the first trimester to morphology and biometry in the second trimester and monitoring of the fetal growth in the third trimester. In addition to ultrasound, fetal magnetic resonance is now widely used to improve imaging of the central nervous system structures [23].
The conventional genetic approach using targeted molecular methods, like Sanger or PCR, is useful (enables) in the diagnostics of cases with ultrasonographically well-defined phenotypes associated with specific diagnostic hypothesis in genetically homogenous CA (e.g., TAR, achondroplasia) and is the method of choice for prenatal testing in cases of familial monogenic condition with known pathogenic variant [24]. In cases with poorly defined phenotype and genetically heterogeneous CAs (many genes responsible for the same phenotype), this approach rarely warrants the diagnosis.
While the conventional genetic diagnostic approach is time-consuming, labor-intensive, and with limited diagnostic yield, the new genomic approaches and technologies, like molecular karyotyping and next-generation sequencing, offer new possibilities to establish specific prenatal genetic diagnosis in high-risk pregnancies.
Growing knowledge and important technical evolution in the last two decades enabled us, in the clinical context, to detect and interpret smaller and smaller genomic imbalances. The classical karyotyping has been replaced first by comparative genomic hybridization and soon thereafter with array-based CGH (aCGH). It is becoming widely applied in the prenatal setting, where it is recommended by many professional societies for routine prenatal diagnostic testing in fetuses with ultrasound anomalies [25].
The comparative genomic hybridization using microarrays (aCGH) is based on competitive hybridization of short segments of whole genome DNA to preprepared probes (short sequences of DNA), spotted on a glass slide in a precise grid (microarray). The DNA of a patient and reference sample DNA are both digested with restriction enzymes to generate short fragments and after that labeled with two different fluorescent dyes. Both patient and reference DNAs are combined and hybridized to the same microarray, thereby competing for the same probes. A specialized scanner measures signal intensities and dedicated software links signals to specific genomic regions (Figure 1). When there is a deletion in the patient, we see it as a predominance of reference DNA in that genomic region. As a result, a relative log ratio of patient’s signal compared to reference signal gives a curve with negative values. Despite enabling the detection of progressively smaller genomic imbalances, one needs to be aware of the limitations—the technique will not detect low-level mosaicism, triploidy, balanced translocations, and point mutations.
Array CGH results in a prenatal case showing typical 22q11.2 deletion. The left side of the figure shows the whole chromosome 22; the right side shows magnified region 22q11.
After performing several large prospective and retrospective studies, it is estimated that this technique offers a 5–10% increase in detection of clinically relevant copy number variation in fetuses with ultrasound anomalies (compared to conventional karyotyping) [3, 26, 27, 28].
In the first years, the technology has been used only in fetuses with multiple congenital anomalies where the yield of pathogenic CNVs was reported to be up to 20%. With the broader use, gained experience, and numerous data on normal variation, molecular karyotyping has been introduced in different prenatal situations. It is also used in the cases of isolated congenital anomalies, increased nuchal translucency only, or positive maternal serum screening. In some countries, all prenatal genetic testing is performed with molecular karyotyping, if the invasive approach has been employed [29]. Namely, a systematic review and meta-analysis of 17 evaluated studies demonstrated additional 5% of clinically relevant CNVs over conventional karyotype in the group of fetuses with isolated increased nuchal translucency NT > 3.5 mm. Even more, a copy number abnormality was identified in 1.7% of fetuses with a normal ultrasonographic examination result with an indication for invasive testing being advanced maternal age or positive aneuploidy screening test [28, 30].
Last but not least, molecular karyotyping has two additional benefits over classical karyotype. As it does not need dividing cells, it can be performed directly from the prenatal sample, speeding up the whole process and giving results in a few days. Even more, only a small amount of DNA is needed, and therefore, it can be easily performed even on those samples with an insufficient amount of material.
The abovementioned added diagnostic yield of microarrays over conventional karyotyping provides evidence that molecular karyotyping should be used as a method of choice for the analysis of potential genetic causes of fetal congenital anomalies. Consistent with this is the ACOG committee opinion which states that molecular karyotyping is recommended instead of a conventional karyotype when there is one or more ultrasonographically identified CAs in the fetus [31].
However, there are still some limitations in prenatal settings, such as the possibility of detecting variants of unknown significance (VOUS), CNV in susceptibility loci, and secondary findings.
The identification of a variant of unknown significance (VOUS) still occurs in 0.3–1% of prenatal cases, despite the wide use of the technology and diverse population data from pre- and postnatal testing. The percentage depends on the resolution and type of the platform used [28, 32]. Currently, there are no guidelines on how to deal with VOUS findings in the prenatal settings. Different practices exist between laboratories. Parental samples can be obtained at the same time as the prenatal sample, so that they are accessible if there is a need to test the origin of certain VOUS identified in the fetus. On the other hand, they can be collected later in those cases that need additional testing. The management of pregnancy is significantly influenced by knowing if specific CNV is de novo or inherited. Some laboratories report all VOUS findings; others only report such CNV when it occurs de novo and taking into account the size and location of the identified CNV.
Yet again, different approaches can be employed when discussing detection and reporting of secondary findings and CNV in susceptibility loci—some laboratories report all such findings, whereas others have specific national or internal guidelines and lists of specific variants that are reported and those that are not reported in prenatal settings [29, 33]. It is important to emphasize the need for informative pretest genetic counseling, where these situations are discussed with future parents.
Generally, it is well accepted that CNVs in susceptibility loci with higher penetrance are reported as such in the context of prenatal genetic testing. A clear difference between such findings and other known fully penetrant microdeletion/microduplication syndromes must be presented to the pregnant couple.
Although microarray analysis has increased the diagnostic yield in comparison to conventional karyotyping, a considerable proportion of fetuses with multiple CAs have a normal karyotype and also a normal microarray result and thus remain without a definitive diagnosis. Determining the cause of CAs in those cases is, during the prenatal period, usually very challenging and frustrating. Genetic testing can be a long process, and the quick turnaround required for prenatal testing limits this process. Additionally, there is often an incomplete presentation of characteristic phenotypes. So targeted gene sequencing is limited by poorly defined phenotype, genetic heterogeneity, and a limited time period during pregnancy.
When CAs are associated with genetic changes in multiple genes, then sequencing the panel of genes using next-generation sequencing (NGS) should be considered a method of choice. The next-generation sequencing approach is based on parallel sequencing of multiple DNA fragments in a single reaction. This enables high-throughput sequencing of large segments of human genome in a cost-efficient manner (Figure 2).
Two causative variants are shown identified by next-generation sequencing (clinical exome sequencing) in a fetus with Joubert syndrome. Part A depicts variant Cys615Arg and part B depicts variant Arg441Cys, both in TMEM67 gene. Both variants have previously been reported as pathogenic, and segregation analysis has shown them to be present in the compound heterozygous form, clarifying the cause in this case.
While this approach is widely used in postnatal settings, its use is more limited prenatally, for reasons similar to that of Sanger sequencing. The limitation of gene panel (gene targets)-based approaches is in their dependence on correct diagnostic hypothesis and the long time to reach a diagnosis in case appropriate panel is not selected or cannot be selected due to a nonspecific clinical presentation. These issues can be addressed by using either mendeliome sequencing or whole exome sequencing, which use NGS to sequence the coding exons of genes associated with Mendelian diseases or all genes in the human genome, respectively. Mendeliome and exome sequencing are achieved by capturing exonic sequences using exon-specific probes. In this way, exonic sequences of the human genome are enriched in the sample, making it possible to focus sequencing on those regions. The principle benefit of this approach is that rather than performing multiple separate gene panel tests for identification of monogenic causes of CAs, a single genetic test is performed, and then results for any gene panel can be inspected depending on the observed clinical signs and symptoms [34]. This makes such an approach significantly faster and without the need for cascading numerous laboratory tests in the case of negative results. Such an approach is also robust in cases with nonspecific clinical presentation without a clear diagnostic hypothesis and in cases that were misdiagnosed.
Current reports have consistently shown the benefit of using exome sequencing in the diagnosis of fetal CA. The current evidence suggests that a genomic abnormality may be identified in up to 20–30% of fetuses with multiple CA and with normal standard genetic results. While initial reports have shown a modest added benefit of exome sequencing in multiple CAs [35], later studies showed considerably higher yields. In a study by Drury et al., exome sequencing could resolve additional 21% cases of pregnancies with multiple congenital anomalies, abnormal ultrasound findings, and a normal microarray result [36]. Even higher diagnostic yields were reported in more selected series of cases—a recent report has shown a positive yield of 47% in fetuses with high suspicion of an underlying genetic disorder and a negative microarray and/or targeted tests [37]. Similarly, Alamillo and colleagues reported a relatively high positive yield in patients with prenatal ultrasound anomalies [38]. These cases illustrate the potentially important role of this new technology in the routine prenatal diagnostics of CAs.
Several studies have now shown that exome sequencing can also be used to detect structural variants and a variety of other pathologic variants apart from simple single-nucleotide variants [39]. This property makes exome sequencing an efficient test for structural and point variation. Despite this, molecular karyotyping is still considered the method of choice in prenatal diagnostics, predominantly because of lower costs and well-established evidence of sensitivity and specificity in the prenatal setting. With the reduction of the price of exome sequencing and with increasing evidence supporting its sensitivity, we expect that next-generation sequencing will ultimately be used for detection of structural and point mutations in a single test. Exome sequencing is, however, a method of choice in multiple CAs cases with normal results of molecular karyotyping and strong clinical suspicion of a monogenic etiology. Accordingly, American College of Medical Genetics has released a policy statement suggesting that WES can be used in the clinical assessment of “a fetus with a likely genetic disorder in which specific genetic tests, including targeted sequencing tests, available for that phenotype have failed to arrive at a diagnosis.” However, the statement stresses the limitations of the use of this technology in the prenatal setting, including long turnaround times and high rates of false positives, false negatives, and VOUS [40].
Despite several benefits outlined above, there are additional challenges associated with NGS-based approaches, particularly in the prenatal diagnostic practice. Exome sequencing is a demanding diagnostic test, requiring a complex set of laboratory, bioinformatic, and interpretative steps before a clinical report may be issued. For this reason, its turnaround time usually ranges from several weeks to several months. To address this issue, there is an incentive to offer urgent exome sequencing service and thus offer provision of clinical reports within less than a month’s time. This significantly increases the utility of this test in prenatal diagnosis. Sequencing of parental samples along with the fetal sample may also be used to facilitate the timely interpretation of the sequencing results. Furthermore, we believe that limiting the set of reported variants to known and clear pathogenicity is also an option in order to limit the complexity of the analysis only to clinically actionable and conclusive results. Furthermore, as in other genome-wide analysis approaches, NGS-based diagnostics inherently raise the issues of incidental findings and variants of uncertain clinical significance. Due to limited time and other specificities in prenatal diagnostics, several approaches should be employed to address these issues. These include (1) limiting the gene target to genes with overlapping clinical symptoms and signs and (2) limiting the reported variants to definitely pathogenic and likely pathogenic variant classes. Furthermore, efficient collaboration within a multidisciplinary team is often crucial in clarifying the clinical relevance of identified variants. Opting for this approach, it is possible to utilize diagnostic advantages of NGS-based approaches while reducing the chance of encountering uncertain and unsolicited findings. Nevertheless, extensive genetic counseling should be offered to patients while stressing the possibility of identification of VUS findings and incidental findings.
The goal of prenatal genetic testing in fetuses with CAs is to determine if there is a genetic etiology and consequently enabling well-informed genetic counseling to the parents about the prognosis, reproductive options, obstetric and pediatric management, and recurrence risks.
Different approaches of prenatal genetic diagnostics are used. Despite known evidence of important additional diagnostic yield of new genomic technologies to the etiology of CAs, most countries still use the traditional genetic diagnostic approach. Hereinafter we present a diagnostic workflow that is currently in use at our institution (Figure 3). It incorporates the use of new genomic technologies and is focused on the expected diagnostic yield and limited diagnostic time frame.
Diagnostic algorithm for the prenatal genetic diagnostics of CAs. CAs, congenital anomalies; QF-PCR, quantitative fluorescence-polymerase chain reaction; NGS, next-generation sequencing.
When fetal CAs are detected on an ultrasound examination, we offer an invasive procedure for diagnostic genetic testing.
In cases when specific chromosomal disorder (e.g., double bubble and trisomy 21) or monogenic syndrome is strongly suspected on the initial evaluation and single gene testing is straightforward (e.g., achondroplasia), we exclude common aneuploidies (trisomy 13, 18, and 21, and aneuploidies of sex chromosomes) first, using quantitative fluorescence-polymerase chain reaction (QF-PCR), and proceed with single gene testing, respectively.
When the specific clinical diagnosis is not apparent, we use a genomic approach for the detection of genetic etiology of CAs. Because aneuploidies represent the commonest genetic etiology of CAs, we first opt for QF-PCR to exclude the aneuploidies mentioned above.
If the results are normal we proceed with molecular karyotyping instead of a conventional karyotype as studies showed that this approach allows for the highest diagnostic yield. We use this approach whether the anomaly appears to be isolated or multiple anomalies are detected.
In fetuses with normal results of molecular karyotyping, with multiple CAs, and a strong clinical indication for monogenic etiology, we proceed with WES; our approach involves sequencing the fetus as well as the biological parents (so-called trio sequencing), which increases the diagnostic yield by filtering out thousands of uninformative genomic variants as well as shortens the analysis turnaround time (less than 3 weeks at our institution).
In cases when CAs are lethal or have unfavorable prognosis, the parents often decide to terminate the pregnancy, but for the purpose of genetic counseling, it is still important to obtain the diagnosis. Accordingly, we shift the diagnostic process after the termination of the pregnancy. Thorough dysmorphological and pathohistological evaluation may give additional information on the specific phenotype and thus enables a more direct diagnostic approach in the aborted fetus. Otherwise, we use the diagnostic approach similar to the approach during the pregnancy.
Different medical specialists are involved in the process of prenatal diagnosis of CAs. The role of a clinical geneticist in the whole pathway of genetic diagnostics of a pregnancy with CAs in a close collaboration with other medical specialists (obstetricians, surgeons, radiologists, pathologists, etc.) and a multidisciplinary approach is undisputed due to all the complexities of prenatal diagnostics of CAs, their clinical presentation and phenotype evaluation, choice of the right genetic testing strategies, interpretation of genetic testing results, and their communication to patients and families.
With the advent of new genetic genomic technologies in the prenatal settings, the diagnostic yield in the etiology of CAs can be significantly improved. This has important consequences for the patients, as it enables the identification of the cause of CAs and, consequently, their prevention, as well as understanding the genetic epidemiology of CAs and designing optimal professional and cost-effective diagnostic algorithms for the diagnostics of CAs.
With the implementation of new genomic technologies in the diagnostic algorithm, approximately 50% of the genetic etiology of prenatally detected CAs can be explained. Therefore, we suggest a timely implementation of these technologies in prenatal diagnostics of CAs.
In the recent years, machine learning techniques have been utilized to solve problems at hand across multitudes of industries and topics. In the healthcare industry, these techniques are often applied to a variety of healthcare claims and electronic health records data to garner valuable insights into diagnostic and treatment pathways in order to help optimize patient healthcare access and treatment process [1]. Unfortunately, many of these applications resulted in inaccurate or irrelevant research results, as proper research protocols were not fully followed [2]. On the other hand, statistics has been the basis of analysis in healthcare research for decades, especially, in the areas of clinical trials and health economics and outcomes research (HEOR), where the precision and accuracy of analyses have been the primary objectives [3]. Furthermore, the classical statistics methodologies are often preferred in those research areas to ensure the ability to replicate and defend the results and ultimately, the ability to publish the research content in peer-reviewed medical journals [3]. The increased availability of data, including data from wearables, provided the opportunity to apply a variety of analytical techniques and methodologies to identify patterns, often hidden, that could help with optimization of healthcare access as well as diagnostic and treatment process [4].
With the rapid increase in data from the healthcare and many other industries, it is important to consider how to select well - suited statistical and machine learning methodologies that would be best for the problem at hand, the available data type, and the overall research objectives [5]. Machine learning alone or complemented by statistical modeling is becoming, not just a more common, but a desired convergence to take advantage of the best of both approaches for advancing healthcare outcomes [1]. Please note that this book chapter was originally posted on the Cornell University’s research working article website: https://arxiv.org. The book chapter content is mostly the same between the two versions [6].
Machine learning (ML) is considered a branch of artificial intelligence and computer science that focuses on mimicking human behaviors through a set of algorithms and methods that use historical values to predict new values [7], without specifically being coded to do so and thereby learning over time [8, 9]. ML is grounded in statistical learning theory (SLT), which provides the constructs used to create prediction functions from data. One of the first examples of SLT was the creation of the support vector machine (SVM), the supervised learning method that can be used as for both classification and regression and has become a standard in modeling how to recognize visual objects [7]. SLT formalizes the model that makes a prediction based on observations (i.e., data) and ML automates the modeling [7].
SLT sets the mathematical and theoretical framework for ML as well as the properties of learning algorithms [7] with the goals of providing mechanisms for studying inference and creating algorithms that become more precise and improved over time [8]. SLT is based multivariate statistics and functional analysis [8]. Functional analysis is the branch of statistics that measures shapes, curves, and surfaces, extending multivariate vector statistics to continuous functions and finding functions that describe data patterns [8]. Inductive inference is the process of generalizing and modeling past observations to make predictions for the future; SLT formalizes the modeling concepts of inductive inference, while ML automates them [8].
For example, pattern recognition is considered a problem of inductive inference and SLT, as it is a curving-fitting problem, and one of the most common applications of ML [7, 8, 9]. Pattern recognition is not suited for traditional computer programming as the inferences needed are not free of assumptions and the patterns are not easily described or labeled programmatically with deterministic functions. The standard mathematics behind SLT makes no assumptions on distributions, uses stochastic functions that can include humans labeling the “right” classification, i.e., training data, and can assume that the probability of the occurrence of one observation is independent of another thereby including the concept of randomness [7, 8, 9]. These tenets are therefore those of ML as well.
SLT also provides the definition of terms often using in ML such as overfitting, underfitting and generalization. Overfitting is when the presence of noise in the data negatively affects training and the ultimate model performance because the noise is being incorporated into the learning process, thereby giving error when the model sees new data [8, 9]. Underfitting is when the noise impacts both performance on training data as well as new and unseen data [9]. In ML, discussion about underfitting and overfitting are often used to describe models that do not generalize the data effectively and might not present the right set of data elements to explain the data patterns and posited hypotheses [9]. Underfitting is often defined when model which is missing features that would be present in the most optimized model, akin to a regression model not fully explaining all of the variance of the dependent variable [9]. In a similar vein, overfitting is when the model contains more features or different features than is optimal, like a regression model with autocorrelation or multicollinearity [9].
The general goal of learning algorithms and therefore ML model optimization is to reduce the dimensions, features, or data variables to the fewest number needed as that reduces noise or the impact of trivial variables that can overfit or unfit [8, 9]. A regularization model can then become generalized to perform not just on the past or the training data, but also on future and yet unseen data [8, 9]. Although true generalization needs both the right modeling criteria as well as strong subject matter knowledge [8].
Often dimension reduction approaches like Principal Component Analysis (PCA) or boot strapping techniques used along with subject matter expertise can help resolve how to refine models, combat fit challenges, as well as improve generalization potential [9, 10]. Furthermore, understanding the studied population and data characteristics can further help define the data to be used, variable selection, and proper model set up [10].
Statistical modeling is based on SLT and use of mathematical models and statistical assumptions to generate sample data and make predictions about the real world occurrences. A statistical model is often represented as a collection of probability distributions on a set of all possible outcomes. Furthermore, statistical modeling has evolved in the last few decades and shaped the future of business analytics and data science, including the current use and applications of ML algorithms. On the other hand, machine learning does not require many assumptions and interventions when running algorithms in order to accurately predict studied outcomes [7].
There are similarities between ML and statistical modeling that are prevalent across most analytical efforts. Both techniques use historical data as input to predict new output values, but they vary as noted above on the underlying assumptions and the level of analyst intervention and data preparation.
Overall, machine learning foundations are based from statistical learning theory, and it is recommended for the data scientists to apply SLT’s guiding rules during analysis. While it may seem as a statistical background and understanding is not required when analyzing the underlying data, this misconception often leads to data scientist’s inability to set up proper research hypothesis and analysis due to a lack of understanding of the problem and the underlying data assumptions as well as caveats. This issue can in turn result in biased and irrelevant results as well as unfounded conclusions and insights. With that in mind, it is important to evaluate the problem at hand, and consider both statistical modeling and ML as possible methods to be applied. Understanding the underlying assumptions of the data and statistical inference can help support proper technique selection and guide the pathway to solution [11]. In the later sections of the chapter, application of both techniques will be provided and the reasoning for selecting the methods presented to guide future research.
As mentioned above, the similarities between ML and statistical modeling start with the underlying assumption that data or observations from the past can be used to predict the future [7]. The variables included in the analysis generally represent two types: dependent variables, that in ML are called targets, and independent variables, that in ML are called features. The definition of the variables is the same across both techniques [8]. Furthermore, both ML and statistical modeling leverage the available data in a way that allow for generalization of results to larger population [7]. The loss and risk associated with the models accuracy and representation of the real world occurrence is described frequently in terms of mean squared error (MSE). In statistical modeling, MSE is the difference between the predicted value and the actual value and is used to measure loss of the performance of predictions. In the ML, the same MSE concept is presented via a confusion matrix that evaluates a classification problem\'s accuracy [9].
Differences between machine learning and statistical modeling are distinct and based on purposes and needs for the analysis as well as the outcomes. Assumptions and purposes for the analysis and approach can vastly differ. For example, statistics typically assumes that predictors or features are known and additive, models are parametric, and testing of hypotheses and uncertainty are forefront. On the other hand, ML does not make these assumptions [12]. In ML, many models are based on non-parametric approaches where the structure of model is not specified or unknown, additivity is not expected, and assumptions about normal distributions, linearity or residuals, for example, are not needed for modeling [10].
The purpose of ML is predictive performance using general purpose learning algorithms to find patterns that are less known, unrelated, and in complex data without a priori view of underlying structures [10]. Whereas in statistical modeling, consideration for inferences, correlations, and the effects of a small number of variables are drivers [12].
Due to the differences in the methods’ characteristics, it is important to understand the variations in application of the techniques when solving healthcare problems. For example, one typical application of statistics is to analyze whether a population has a particular medical condition. For some diseases such as diabetes, the condition is easily screened for and diagnosed using distinct lab values, such as elevated and increasing HbA1C over time, high glucose levels and low insulin levels, often due to insulin depletion occurring from unmanaged diabetes. Also conditions such as hypertension can easily be detected at home or in the healthcare provider’s office using simple blood pressure measurement and monitoring, and wearables can identify when patients are experiencing atrial fibrillation, abnormal heart rhythms and even increased patient falls (possible syncope). Therefore, analyses of patients with these easily measurable conditions can be done simply by qualifying patients based on lab values or biomarkers falling within or outside of certain ranges. One of the simplest examples is identifying patients with diabetes [13]. This can be accomplished by using A1C levels to group patients as having no diabetes (A1C < 5.7), pre-diabetes (AIC of 5.7–6.4), or diabetes (A1C > 6.4). These ranges are based on American Diabetes Association Diagnosis Guidelines and a very high, medically accepted correlation between AIC levels and the diagnosis of diabetes [14].
On the other hand, if the objective of the research is to predict which pre-diabetic patients are most likely to progress to diabetes, a myriad of factors influence diabetes progression including extent of chronic kidney disease, high blood pressure, insulin levels over time, body mass index/obesity, age, years with diabetes, success of prior therapy, number and types of prior therapies, family history, coronary artery disease, prior cardiovascular events, infections, etc. A complicated combination of comorbidities, risk factors, and patient behavior can lead to differing diabetes complications and varying outcomes makes prediction more challenging and thus it represents a good candidate for the use of machine learning techniques. Classification models such as gradient boosting tree algorithms have been used to successfully predict diabetes progression, especially earlier in the disease. While there any many diabetes risk factors and co-morbidities, these disease characteristics are well studied over many years, thus enabling stable predictive models which perform well over time [14].
Overall, machine learning is highly effective when the model uses more than a handful of independent variables/features [10]. ML is required when the number of features (p) is larger than the number of records or observations (n) – this is called the curse of dimensionality [15, 16], which increases the risk of overfitting, but can be overcome with dimensionality reductive techniques (i.e., PCA), as part of modeling [15] and clinical/expert input on the importance or lack thereof of certain features, is it relates to the disease or its treatment. Additionally, statistical learning theory teaches that learning algorithms increase their ability to translate complex structures from data at a greater and faster rate than the increase of sample size capture can alone provide [8]. Therefore, statistical learning theory and ML offer methods for addressing high-dimensional data or big data (high velocity, volume and variety) and smaller sample sizes [17], such as recursive feature elimination and support vector machines, boosting, or cross validation which can also minimize prediction error [18].
In the healthcare industry, machine learning models are frequently used in cancer prediction, generally in three areas: (1) predicting a patient with a cancer prognosis/diagnosis, (2) predicting cancer progression, and (3) predicting cancer mortality. Of these, predicting whether a patient may have a cancer prognosis/diagnosis can be more or less difficult depending on the tumor type. Certain cancers such as lung cancer, breast cancer, prostate cancer, and skin cancer are evaluated based on specific signs and symptoms, and non-invasive imaging or blood tests. These cancers are easier to predict. Conversely, cancers with non-descript symptoms such fatigue, dizziness, GI pain and distress, and lack of appetite are much more difficult to predict even with machine learning models as these symptoms are associated with multiple tumor types (for example esophageal, stomach, bladder, liver, and pancreatic cancer) and also mimic numerous other conditions [14].
For cancers with vague symptoms, understanding the patient journey is very important to cancer prediction. If a prediction period is too long and does not reflect the time period before diagnosis when symptoms develop, the model may overfit due to spurious variables not related to the condition. If the prediction period is too short, key risk factors from the patient record could be missing. Variable pruning is required in these situations. A multi-disciplinary team including business and clinical experts can help trim unrelated variables and improve model performance [14].
Model validation is an inherent part of the ML process where the data is split into training data and test data, with the larger portion of data used to train the model to learn outputs based on known inputs. This process allows for rapid structure knowledge for primary focus on building the ability to predict future outcomes [15]. Beyond initial validation of the model within the test data set, the model should be further tested in the real world using a large, representative, and more recent sample of data [19]. This can be accomplished by using the model to score the eligible population and using a look forward period to assess incidence or prevalence of the desired outcome. If the model is performing well, probability scores should be directly correlated to incidence/prevalence (the higher the probability score, the higher the incidence/prevalence). Model accuracy, precision, and recall can also be assessed using this approach [20].
Epidemiology studies and prior published machine learning research in related areas of healthcare can help benchmark the performance of the model relative to the baseline prevalent or incident population for the condition to be predicted. Machine learning models created using a few hundred or thousand patients often do not perform as well in the real world. Careful variable pruning, cohort refinement and adjustment of modeling periods can often resolve model performance problems. Newer software can be used to more quickly build, test, and iterate models, allowing users to easily transform and combine features as well as run many models simultaneously and visualize model performance, diagnosis and solve model issues [21].
Machine learning algorithms are a preferred choice of technique vs. a statistical modeling approach under specific circumstances, data configurations, and outcomes needed.
As noted above, machine learning algorithms are leveraged for prediction of the outcome rather than present the inferential and causal relationship between the outcome and independent variables/data elements [17, 22]. Once a model has been created, statistical analysis can sometime elucidate and validate the importance and relationship between independent and dependent variables.
Machine Learning algorithms are learner algorithms and learn on large amount of data often presented by a large number of data elements, but not necessarily with many observations [23]. Ability of multiple replications of samples, cross validation or application of boot strapping techniques for machine learning allows for wide datasets with many data elements and few observations, which is extremely helpful in predicting rare disease onset [24] as long as the process is accompanied with real world testing to ensure the models are not suffering from overfitting [18, 19]. With the advent of less expensive and more powerful computing power and storage, multialgorithm, ensembled models using larger cohorts can be more efficiently built. Larger modeling samples that are more representative of the overall population can help reduce the likelihood of overfitting or underfitting [25]. A large cohort imposes various issues and of priority is the ability to identify the set of independent variables that are most meaningful and impactful. These significant independent variables provide a predictive and/or inferential model that can be readily acceptable in providing a real-world application. The variables in such instances may also result into more realistic magnitude and direction of the causal relationship between the independent and outcomes variables of interest.
A recent example for a real-world example in healthcare for machine learning algorithm application is to identify the likelihood of hospitalization for high-risk patients diagnosed with Covid 19. The dataset leveraged included over 20,000 independent variables across healthcare claims data for diagnostics and treatment variables. The best optimal ML model consisted of approximately 200 important predictors variables such as age, diagnosis like Type 2 diabetes/CKD/Hypertension, frequency of office visits, Obesity amongst others. None of the variables in this example were ‘new’, however, the magnitude and direction as a result of the ML exercise may illustrate the ‘true’ impact of each independent variable, a feature that is a serious limitation in traditional statistical modeling [26].
Furthermore, as explained above, statistical models tend to not operate well on very large datasets and often require manageable datasets with a fewer number of pre-defined attributes/data elements for analysis [23]. The recommended number of attributes is up to 12 in a statistical model, because these techniques are highly prone to overfitting [25]. This limitation creates a challenge when analyzing large healthcare datasets and require application of dimension reduction techniques or expert guidance in allowing to eliminate the number of independent variables in the study [23].
In machine learning algorithms, there are fewer assumptions that need to be made on the dataset and the data elements [5]. However, a good model is usually preceded by profiling of the target and control groups and some knowledge of the domain. Understanding relationships within the data improve outcomes and interpretability [27].
Machine learning algorithms are comparatively more flexible than statistical models, as they do not require making assumptions regarding collinearity, normal distribution of residuals, etc. [5]. Thus, they have a high tolerance for uncertainty in variable performance (e.g., confidence intervals, hypothesis tests [28]. In statistical modeling emphasis is put in uncertainty estimates, furthermore, a variety of assumptions have to be satisfied before the outcome from a statistical model can be trusted and applied [28]. As a result, the statistical models have a low uncertainty tolerance [25].
Machine learning algorithms tend to be preferred over statistical modeling when the outcome to be predicted does not have a strong component of randomness, e.g., in visual pattern recognition an object must be an E or not an E [5], and when the learning algorithm can be trained on an unlimited number of exact replications [29].
ML is also appropriate when the overall prediction is the goal, with less visibility to describe the impact of any one independent variable or the relationships between variables [30], and when estimating uncertainty in forecasts or in effects of selected predictors is not a requirement [28]. However, often data scientists and data analysts leverage regression analytics to understand the estimated impact, including directionality of the relationships between the outcome and data elements, to help with model interpretation, relevance, and validity for the studied [27]. ML is also preferred when the dataset is wide and very large [23] with underlying variables are not fully known and previously described [5].
Machine learning requires no prior assumptions about the underlying relationships between the data elements. It is generally applied to high dimensional data sets and does not require many observations to create a working model [5]. However, understanding the underlying data will support building representative modeling cohorts, deriving features relevant for the disease state and population of interest, as well as understanding how to interpret modeling results [19, 27].
In contrast, statistical model requires a deeper understanding how the data was collected, statistical properties of the estimator (p-value, unbiased estimators), the underlying distribution of the population, etc. [17]. Statistical modeling techniques are usually applied to low dimensional data sets [25].
Robert Tibshirani, a statistician and machine learning expert at Stanford University, calls machine learning “glorified statistics,” which presents the dependence of machine learning techniques on statistics in a successful execution that not only allows for a high level of prediction, but interpretation of the results to ensure validity and applicability of the results in the healthcare [17]. Understanding the association and knowing their differences enables data scientists and statisticians to expand their knowledge and apply variety of methods outside their domain of expertise. This is the notion of “data science,” which aims to bridge the gap between the areas as well as bring other important to consider aspects of research [5]. Data science is evolving beyond statistics or more simple ML approaches to incorporate self-learning and autonomy with the ability to interpret context, assess and fill in data gaps, and make modeling adjustment over time [31]. While these modeling approaches are not perfect and more difficult to interpret, they provide exciting new options for difficult to solve problems, especially where the underlying data or environment is rapidly changing [27].
Collaboration and communication between not only data scientists and statisticians but also medical and clinical experts, public policy creators, epidemiologists, etc. allows for designing successful research studies that not only provide predictions and insights on relationships between the vast amount of data elements and health outcomes [30], but also allow for valid, interpretable and relevant results that can be applied with confidence to the project objectives and future deployment in the real [30, 32].
Finally, it is important to remember that machine learning foundations are based in statistical theory and learning. It may seem machine learning can be done without a sound statistical background, but this leads to not really understanding the different nuances in the data and presented results [17]. Well written machine learning code does not negate the need for an in-depth understanding of the problem, assumptions, and the importance of interpretation and validation [29].
As mentioned earlier in the chapter, machine learning algorithms can be leveraged in the healthcare industry to help evaluate a continuum of access, diagnostic and treatment outcomes, including prediction of patient diagnoses, treatment, adverse events, side effects, and improved quality of life as well as lower mortality rates [24].
As shown in Figure 1, often these algorithms can be helpful in predicting a variety of disease conditions and shortening the time from awareness to diagnosis and treatment, especially in rare and underdiagnosed conditions, estimate the ‘true’ market size, predicting disease progression such as identifying fast vs. slow progressing patients as well as determinants of suitable next line change [32]. Finally, the models can be leveraged for patient and physician segmentation and clustering to identify appropriate targets for in-person and non-personal promotion [30].
Examples of Machine Learning Applications in Healthcare Analytics [
There are, however, instances in which machine learning might not be the right tool to leverage, including when the condition or the underlying condition have a few known variables, when the market is mature and has known predetermined diagnostic and treatment algorithm, and when understanding correlations and inference is more important than making prediction [5].
One aspect of the machine learning process is to involve a cross functional team of experts in the healthcare area to ensure that the questions and problem statement along with hypothesis are properly set up [33, 34]. Many therapeutic areas require in-depth understanding of the clinical and medical concepts (i.e., diagnostic process, treatment regimens, potential adverse effects, etc.), which can help with the research design and selection of the proper analytical techniques. If the expert knowledge is not considered or properly captured in the research design, it might lead to irrelevant, invalid, and biased results, and ultimately invalidate the entire research study [33, 34].
Using a real example of a project with the goal of predicting the risk of hypertension due to underlying comorbid conditions or induced by medication, the decision to lead with machine learning vs. statistical modeling can be based on explicit criteria that can be weighed and ranked based on the desired outcome of the work [17, 32]. Please see Figure 2 presenting an example of the approach.
Criteria for Choosing the Predominant Approach for a Project.
As shown in Figure 2, pending the research objectives, machine learning or statistical modeling or both techniques could be the right method(s) to apply. For example, shifts in market trends, including shifts in patient volume of diagnosis and treatment present a suitable example when a statistical modeling type of analysis should be utilized. On the other hand, trying to predict patients with a high risk for hypertension requires the utilization of ML approaches. Leveraging both methods is best suited when predictive power and explanatory reasoning is needed to understand the important factors driving the outcome and their relative magnitudes and inferences.
Machine learning requires fewer assumptions about the underlying relationships between the data elements. It is generally applied to high dimensional data sets and require fewer observations to create a working model [5]. In contrast, statistical model requires an understanding of how the data was collected, statistical properties of the estimator (p-value, unbiased estimators), the underlying distribution of the population, etc. [17]. Statistical modeling techniques are usually applied to low dimensional data sets [25]. Statistical modeling and ML are not at odds but rather complementary approaches that offer choice of techniques based on need and desired outcomes. Data scientists and analysts should not necessarily have to choose between either machine learning or statistical modeling as a mutually exclusive decision tree. Instead, selected approaches from both areas should be considered as both types of methodologies are based on the same mathematical principles but expressed somewhat differently [5, 10].
The authors declare no conflict of interest.
Authors work for Symphony Health, ICON plc Organization.
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\n\nThe Open Access publishing model followed by IntechOpen eliminates subscription charges and pay-per-view fees, thus enabling readers to access research at no cost to themselves. In order to sustain these operations, and keep our publications freely accessible, we levy an Open Access Publishing Fee on all manuscripts accepted for publication to help cover the costs of editorial work and the production of books.
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