Artificial Intelligence and Machine Learning for Healthcare: Vol. 2: Emerging Methodologies and Trends

282 Pages·2022·4.957 MB·English

by  Chee Peng Lim, Ashlesha Vaidya, Yen-Wei Chen, Vaishnavi Jain, Lakhmi C. Jain

Intelligent Systems Reference Library
Volume 229
SeriesEditors
JanuszKacprzyk,PolishAcademyofSciences,Warsaw,Poland
LakhmiC.Jain,KESInternational,Shoreham-by-Sea,UK
The aim of this series is to publish a Reference Library, including novel advances
and developments in all aspects of Intelligent Systems in an easily accessible and
wellstructuredform.Theseriesincludesreferenceworks,handbooks,compendia,
textbooks,well-structuredmonographs,dictionaries,andencyclopedias.Itcontains
wellintegratedknowledgeandcurrentinformationinthefieldofIntelligentSystems.
Theseriescoversthetheory,applications,anddesignmethodsofIntelligentSystems.
Virtuallyalldisciplinessuchasengineering,computerscience,avionics,business,
e-commerce,environment,healthcare,physicsandlifescienceareincluded.Thelist
oftopicsspansalltheareasofmodernintelligentsystemssuchas:Ambientintelli-
gence,Computationalintelligence,Socialintelligence,Computationalneuroscience,
Artificiallife,Virtualsociety,Cognitivesystems,DNAandimmunity-basedsystems,
e-Learningandteaching,Human-centredcomputingandMachineethics,Intelligent
control,Intelligentdataanalysis,Knowledge-basedparadigms,Knowledgemanage-
ment, Intelligent agents, Intelligent decision making, Intelligent network security,
Interactive entertainment, Learning paradigms, Recommender systems, Robotics
and Mechatronics including human-machine teaming, Self-organizing and adap-
tive systems, Soft computing including Neural systems, Fuzzy systems, Evolu-
tionarycomputingandtheFusionoftheseparadigms,PerceptionandVision,Web
intelligenceandMultimedia.
IndexedbySCOPUS,DBLP,zbMATH,SCImago.
AllbookspublishedintheseriesaresubmittedforconsiderationinWebofScience.
· · ·
Chee Peng Lim Ashlesha Vaidya Yen-Wei Chen
·
Vaishnavi Jain Lakhmi C. Jain
Editors
Artificial Intelligence
and Machine Learning
for Healthcare
Vol. 2: Emerging Methodologies and Trends
Editors
CheePengLim AshleshaVaidya
DeakinUniversity RoyalAdelaideHospital
Victoria,VIC,Australia Adelaide,SA,Australia
Yen-WeiChen VaishnaviJain
CollegeofInformationScience ASFInsignia
andEngineering KyndrylSolutionsPvtLtd.
RitsumeikanUniversity Gurugram,Haryana,India
Kusuatsu,Shiga,Japan
LakhmiC.Jain
LiverpoolHopeUniversity
Liverpool,UK
KESInternational
Shoreham-by-Sea,UK
ISSN 1868-4394 ISSN 1868-4408 (electronic)
IntelligentSystemsReferenceLibrary
ISBN 978-3-031-11169-3 ISBN 978-3-031-11170-9 (eBook)
https://doi.org/10.1007/978-3-031-11170-9
©TheEditor(s)(ifapplicable)andTheAuthor(s),underexclusivelicensetoSpringerNature
SwitzerlandAG2023
Thisworkissubjecttocopyright.AllrightsaresolelyandexclusivelylicensedbythePublisher,whether
thewholeorpartofthematerialisconcerned,specificallytherightsoftranslation,reprinting,reuse
ofillustrations,recitation,broadcasting,reproductiononmicrofilmsorinanyotherphysicalway,and
transmissionorinformationstorageandretrieval,electronicadaptation,computersoftware,orbysimilar
ordissimilarmethodologynowknownorhereafterdeveloped.
Theuseofgeneraldescriptivenames,registerednames,trademarks,servicemarks,etc.inthispublication
doesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfromtherelevant
protectivelawsandregulationsandthereforefreeforgeneraluse.
Thepublisher,theauthors,andtheeditorsaresafetoassumethattheadviceandinformationinthisbook
arebelievedtobetrueandaccurateatthedateofpublication.Neitherthepublishernortheauthorsor
theeditorsgiveawarranty,expressedorimplied,withrespecttothematerialcontainedhereinorforany
errorsoromissionsthatmayhavebeenmade.Thepublisherremainsneutralwithregardtojurisdictional
claimsinpublishedmapsandinstitutionalaffiliations.
ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSwitzerlandAG
Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland
Preface
ThisvolumeisasequeloftheeditiononArtificialIntelligenceandMachineLearning
forHealthcare.Anumberofartificialintelligence/machinelearning(AL/ML)-based
imageanddataanalyticssolutionsforaddressingmedicalandhealthcareproblems
are presented in the first volume. In this second volume, a total of ten chapters
dedicated to emerging methodologies and future trends of AL/ML in the medical
andhealthcaredomainsarecovered.Adescriptionofeachcontributionisasfollows.
RuizandVelasquezanalysednewparadigmstodeveloppreventive,participatory,
predictive,andpersonalisedmedicine(4Pmedicine).Thefoundationandpotential
forAIandMLtosolicitknowledgethroughintelligentimageanddataprocessingare
explained.Consideringhealth-relateddatabasesonlygrowincomplexityandsize,AI
andMLplayacriticalroletoestablishautomatedtoolsforprocessingandextraction
ofhealthcareinformationandknowledge.Theformationofmulti-disciplinaryteams
to design and develop new intelligent solutions for supporting decision-making is
vital,contributingtowardstheadvancementof4Pmedicineinthefuture.
Belciugprovidedaguideonsurvivalanalysisandpracticalexamplesinoncology.
Severalmethodologies,whichincludeKaplan–Meiersurvivalcurves,logranktest,
hazard ratio, and Cox regression, are introduced to determine the best course
of medical treatment. A step-by-step guide for performing survival analysis and
interpretingtheobtainedresultsisprovided.
Kapoteli et al. devised a general approach to sentiment analysis pertaining to
COVID-19vaccinationandreviewedseveralusecases.BasedonaTwitterdataset
onCOVID-19vaccination,bothsupervisedandunsupervisedmodelsforsentiment
analysisareexplored.Thedevelopedmethodologyisusefulformininginformation
onpublicattitudes,forecastingopinionsandreactionsrelatedtovaccineupdatein
nearrealtime,whichiscriticalindealingwithhealthemergencysituations.
Michailidis et al. utilised data mining techniques for incident prediction and
generalmedicalknowledgeacquisition.MLmethodologiesthatareusefulforhealth-
caresupportareexplained,whichincludeclassification,regression,clustering,and
associationrules.Astudyonstrokepredictionisusedtoillustratetheeffectiveness
ofMLmodels,whileissuesandchallengesofdatamininginstrokepredictionand
generalhealthcaresupportarealsodiscussed.
v
vi Preface
Ageingisarealconcernnowadaysinmanycountries.WithintheEuropeanageing
population, hearing loss, cardiovascular diseases, cognitive impairments, mental
health, and balance disorders are prevalent medical conditions that cause tremen-
dous social and financial issues. Bellandi et al. conducted a study on preventing,
slowingthedevelopmentof,ordealingeffectivelywiththeeffectsofthesemedical
conditions and related issues. An eHealth platform leveraging AI and ML models
isdevised.Personalisednotificationsandalertscanbegeneratedbycollectingand
analysingdailyactivitydataoftheelderlythroughtheintegrationofheterogeneous
smart devices, e.g. wearables and environment sensors. This allows an affordable,
secure, and privacy-preserving living environment to be established for promoting
healthyandindependentlivingfortheageingpopulation.
Game-basedvirtualreality(VR)rehabilitationprotocolsofferapromisingthera-
peuticalternativeforactivatingneuro-motorfunctionsaswellassustainingmotiva-
tionandachievingrehabilitationgoalsofpatients.Jeyakumaretal.studiedawide
rangeofVR-basedapplicationsforrehabilitationtreatmentsanddiscussedtheroleof
virtualgamesforrapidrecoveryduringpost-therapy.Casestudiesonsportsrehabil-
itationexercisesforathletessufferingfrommusculoskeletalinjuriesandguidelines
forthemtoreturntoplayarealsopresented.
Learningandplayinggamesareusefulforchildrentodevelopsocialskills.Mihova
etal.reviewedontheuseofseriousgamestoimprovethecommunicationskillsof
youngsters with Autism Spectrum Disorder (ASD). Specialised games are useful
tools to help the development of a child’s skills based on certain parameters. The
reviewandfindingsassiststakeholdersinunderstandingtheimpactsofusingsmart-
phones, mobileapps,and computer games as atherapeutic approach toenhancing
socialskillsofASDchildren.
Ovalle-Magallanesetal.presentedanoverviewandfuturetrendondeeplearning-
basedcoronarystenosisdetectioninX-rayangiographyimages.Thebasicmethod-
ologies of convolutional neural networks, attention modules, vision transformers,
andquantumcomputingareexplained.Theuseofhybridmethodsforenhancingthe
effectivenessofdeeplearningmodelsisdescribed.Challengesandfuturedirections
onstenosisdetectionmethodsarealsodiscussed.
Hoppeetal.conductedasystematicliteraturereviewonthepotentialbenefitofAI
inthehealthcaresector.ThemainfactorsconcerningAIinhealthcareareanalysed,
whichincludemanagementtasks,medicaldiagnostics,medicaltreatment,anddrug
discovery. Utilising structural equation modelling, medical diagnostics, and drug
discoveryisidentifiedaspositiveandsignificantinfluencingfactorswithrespectto
thepotentialbenefitofAIinhealthcare.Variousrecommendationstofurtherexploit
thepotentialofAIinhealthcareareprovided.
Beltempoetal.analysedthebarriersassociatedwithAIinthehealthcaresector.
Basedonasystematicliteraturereview,severalkeybarriersconcerningAIinhealth
care are identified and examined, namely disagreement in data protection, lack of
compatibilitywithethicalaspects,qualityoftrainingdata,knowledge,andtrustof
physiciansinAI-supportedsystems.Opinionssolicitedfrominterviewswithmedical
professionalsandAIdevelopersarecomparedanddiscussed.Potentialresolutions
Preface vii
toundertaketheidentifiedbarriersarederived,contributingtowardsadvancingAI
forthehealthcaresectorinfuture.
The editors are grateful to all authors for their contributions and to Springer
editorialteamfortheirsupportthroughoutthecompilationofbothvolumesofthis
edition. We sincerely hope that the selected chapters in both volumes offer new
knowledgeandideasforreaderstodesign,develop,andimplementAI/MLsystems
fordeliveringbetterhealthcareservicesandrealising4Pmedicineforthebetterment
ofoursociety.
Victoria,Australia CheePengLim
Adelaide,Australia AshleshaVaidya
Kusuatsu,Japan Yen-WeiChen
Gurugram,India VaishnaviJain
Liverpool/Shoreham-by-Sea,UK LakhmiC.Jain
May2022
Contents
1 ArtificialIntelligencefortheFutureofMedicine ................. 1
RocíoB.RuizandJuanD.Velásquez
1 Introduction ............................................. 2
2 HowDoMachinesLearn? ................................. 3
2.1 MachineLearningProcess ......................... 4
2.2 MachineLearninginMedicine ..................... 6
3 ArtificialIntelligenceinMedicine .......................... 6
4 AIApplicationsinMedicine ............................... 8
4.1 PredictiveMedicine ............................... 9
4.2 ParticipatoryMedicine ............................ 11
4.3 PersonalizedMedicine ............................. 16
4.4 PreventiveMedicine .............................. 19
5 Summary ............................................... 19
References .................................................... 22
2 ASurvivalAnalysisGuideinOncology .......................... 29
SmarandaBelciug
2.1 Introduction ............................................. 29
2.2 SurvivalAnalysis ........................................ 31
2.3 Kaplan–MeierSurvivalCurve .............................. 34
2.4 TheLogrankTest ........................................ 36
2.5 TheHazardRatio ........................................ 39
2.5.1 CoxRegressionModel ............................ 41
2.6 Conclusions ............................................. 45
References .................................................... 45
3 Social Media Sentiment Analysis Related to COVID-19
Vaccinations .................................................. 47
EvridikiKapoteli, VasilikiChouliara, ParaskevasKoukaras,
andChristosTjortjis
3.1 Introduction ............................................. 48
3.2 LiteratureReview ........................................ 51
ix
x Contents
3.2.1 MachineLearning-BasedSentimentAnalysis
Studies .......................................... 51
3.2.2 Lexicon-BasedSentimentAnalysisStudies ........... 54
3.2.3 HybridSentimentAnalysisStudies .................. 56
3.3 Methodology ............................................ 57
3.3.1 MethodologyOutline .............................. 57
3.4 Experiments ............................................. 57
3.4.1 Dataset .......................................... 57
3.4.2 DatasetPre-Processing ............................ 58
3.4.3 SentimentAnalysis ............................... 58
3.5 ExperimentalResults ..................................... 61
3.6 Conclusion .............................................. 64
3.6.1 Discussion ....................................... 64
3.6.2 OverviewofContribution .......................... 65
3.6.3 FutureDirections ................................. 66
References .................................................... 66
4 Healthcare Support Using Data Mining: A Case Study
onStrokePrediction ........................................... 71
GeorgiosMichailidis, MichailVlachos-Giovanopoulos,
ParaskevasKoukaras,andChristosTjortjis
4.1 Introduction ............................................. 72
4.1.1 DataMining ..................................... 73
4.1.2 DataMininginHealthcare ......................... 74
4.1.3 ApplicationsofDataMininginHealthcare ........... 75
4.1.4 ChapterOverview ................................ 76
4.2 LiteratureReview ........................................ 76
4.2.1 DataMiningApplicationsinHealthcare .............. 77
4.2.2 Machine Learning Concepts Related
withHealthcareSupport ........................... 80
4.3 MethodologyandResults .................................. 82
4.3.1 MethodologyOutline .............................. 82
4.3.2 Experiments ..................................... 83
4.4 Conclusion .............................................. 89
4.4.1 Discussion ....................................... 89
4.4.2 IssuesandChallengesofDataMininginStroke
PredictionandHealthcare .......................... 89
4.4.3 FutureDirectionsandInsights ...................... 90
References .................................................... 91
5 A Big Data Infrastructure in Support of Healthy
andIndependentLiving:ARealCaseApplication ................ 95
ValerioBellandi
5.1 Introduction ............................................. 95
5.2 Architecture ............................................. 97
5.2.1 HomeHub ....................................... 99