Artificial Intelligence and Big Data Analytics for Smart Healthcare (Next Generation Technology Driven Personalized Medicine And Smart Healthcare)

277 Pages·2021·10.401 MB·English

by  Miltiadis D. Lytras (editor), Akila Sarirete (editor), Anna Visvizi (editor), Kwok Tai Chui (editor)

ARTIFICIAL INTELLIGENCE AND BIG
DATA ANALYTICS FOR SMART
HEALTHCARE
Next Generation Technology-Driven Personalized
Medicine and Smart Healthcare Series
ARTIFICIAL
INTELLIGENCE AND
BIG DATA ANALYTICS
FOR SMART
HEALTHCARE
Edited by
M D. L
ILTIADIS YTRAS
EffatCollegeofEngineering,EffatUniversity,Jeddah,SaudiArabia;KingAbdulazizUniversity,
Jeddah,SaudiArabia
A S
KILA ARIRETE
CollegeofEngineering,EffatUniversity,Jeddah,SaudiArabia
A V
NNA ISVIZI
SGHWarsawSchoolofEconomics,Warsaw,Poland;EffatCollegeofBusiness,EffatUniversity,
Jeddah,SaudiArabia
K T C
WOK AI HUI
SchoolofScienceandTechnology,HongKongMetropolitanUniversity,Kowloon,HongKongSAR,China
Series Editor
M D. L
ILTIADIS YTRAS
A V
NNA ISVIZI
E D
RNESTO AMIANI
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Dedication
To all healthcare specialists, doctors, researchers, and volunteers involved in
fighting the Covid-19 pandemic.
To our parents.
Contents
List of contributors xiii 2.3.2 Professionalaccreditationofhealth-care
practitioners 17
Preface: artificial intelligence and big data
2.3.3 MLforrecommending(individualized)
analytics for smart healthcare: a digital
professionaldevelopmentactivitiesand
transformation of healthcare primer xvii programs 18
Acknowledgments xxix 2.3.4 Theutilityofnaturallanguage
processingtoimproveperformanceat
1. Healthcare in the times of theSCFHS 18
artificial intelligence: 2.3.5 Theutilityofrobotics/RPAtoimprove
setting a value-based context 1 performanceattheSCFHS 19
2.4 AIimplementationisanopportunityfor
DimitriosM.Lytras,HaraLytraandMiltiadisD.Lytras successfulhuman(cid:1)machinecollaboration 21
2.5 Conclusionandethicalconsiderations 21
1.1 Introduction—mappingthecurrentchallenges
References 21
inthehealthdomain 1
Furtherreading 23
1.2 Value-basedapproachtohealthcare 3
1.3 Currentstateofartificialintelligenceutilization
3. Big data infrastructure: data mining,
inthehealthdomain/artificialintelligence
text mining, and citation context analysis
metaphorsanditscontributiontothedigital
transformationofhealthcare 5 in scientific literature 25
1.4 Conclusion 8
UsmanAhmad,MohammedAlsaqer,SalemAlelyani,IqraSafder,
References 8 SehrishIqbal,Saeed-UlHassanandNaifRadiAljohani
Furtherreading 9
3.1 Introduction 25
3.2 Literaturereview 28
2. High-level strategy for implementing 3.3 Dataandmethodology 32
artificial intelligence at the Saudi 3.3.1 Dataandpreprocessing 33
Commission for Health Specialties 11 3.3.2 Featureengineering 34
3.4 Resultsanddiscussion 36
AbdulrahmanHousawi,BasimAlsaywid,MiltiadisD.Lytras, 3.4.1 Trainingandtestingdata 36
AretiApostolaki,AbrarW.Tolah,MahaAbuzenadaand
3.4.2 DiscussionofROCcurves 37
ManalHassanAlmehda
3.4.3 Discussiononprecision(cid:1)recall
2.1 Introduction 11 curves 38
2.2 Literaturereview 14 3.4.4 Discussiononimportantfeatures 38
2.3 CurrentstateofAIutilizationatthe 3.4.5 Evaluation 39
SCFHS 17 3.5 Concludingremarks 40
2.3.1 Matchingprospectivetrainees Acknowledgment 40
(residents)toresidencytraining AppendixA 40
programs 17 References 42
vii
viii Contents
4. Place attachment theories: 6. QoS of mobile cloud computing
a spatial approach to smart health applications in healthcare 81
and healing 47
Jesu´sPeral,V´ıctorSa´nchez,MargaritaGuerrero,
HiginioMoraandDavidGil
SarahJorgensenandVasilikiGeropanta
6.1 Introduction 81
4.1 Introduction—smarthealthcare,smart-home
6.2 Cloudcomputingandmobilecloud
services,andtheplaceattachmenttheory 47
computing 85
4.1.1 Contributions 49
6.3 QoSinCCandMCC 86
4.1.2 Linkingthisstudytoartificial
6.4 CCandMCCapplicationsinthe
intelligenceandbigdata
healtharea 87
analytics 49
6.5 NewtrendsofsecurityofCCin
4.2 Literaturereview—usingplaceattachmentto
thehealtharea 90
define“home” 50
6.6 EvaluationofperformanceandQoSin
4.2.1 Homeasaplaceforhealing 50
thehealtharea 91
4.2.2 Placeattachmentandthehome
6.7 Conclusion 94
environment 51
Acknowledgments 94
4.3 Methodology—casestudies 52
References 95
4.3.1 Casestudy1—smartlighting 53
4.3.2 Casestudy2—IoTconnectivityof 7. Analysis of Parkinson’s disease based on
devices 54 mobile application 97
4.3.3 Casestudy3—personalizationof
spaces 55 MiguelTorres-Ruiz,GiovanniGuzma´n,MarcoMoreno-Ibarraand
AnaAcosta-Arenas
4.4 Implementation 57
4.4.1 Ascenarioofimplementingthethree 7.1 Introduction 97
casestudies—StGeorge’sHospital, 7.2 Relatedwork 100
PortElizabeth,andathree-dimensional 7.3 Methodsandmaterials 103
analysis 57 7.3.1 Monitoringanddatacollection 103
4.5 Conclusionandrecommendations 58 7.3.2 Datapreprocessing 107
4.6 Futureresearch 59 7.4 Experimentalresults 110
References 59 7.4.1 Themanualdexterityactivity 111
7.4.2 Thewalkingactivity 111
5. Utilizing IoT-based sensors and 7.4.3 Thememoryactivity 114
prediction model for health-care 7.5 Conclusionandfuturework 116
monitoring system 63 Acknowledgments 117
References 117
GanjarAlfian,MuhammadSyafrudin,NormaLatifFitriyani,
M.AlexSyaekhoniandJongtaeRhee 8. Mobile Partogram—m-Health
technology in the promotion of parturient’s
5.1 Introduction 63
health in the delivery room 121
5.2 Literaturereview 65
5.3 Health-caremonitoringsystem 68 KarlaMariaCarneiroRolim,M´ırianCal´ıopeDantasPinheiro,
5.3.1 Systemdesignandimplementation 68 PlacidoRogerioPinheiro,MirnaAlbuquerqueFrota,
5.3.2 Bloodglucosepredictionmodel 70 Jose´EuricodeVasconcelosFilho,IzabeladeSousaMartins,
MariaSolangeNogueiradosSantosand
5.4 Resultanddiscussion 75
FirminaHermelindaSaldanhaAlbuquerque
5.4.1 Health-caremonitoringsystem 75
5.4.2 Bloodglucosepredictionmodel 76 8.1 Introduction 121
5.5 Conclusion 78 8.2 TheMobilePartogramconception—m-Health
Acknowledgment 78 technologyinparturientcareinthe
References 78 deliveryroom 123
Contents ix
8.3 Participatoryuser-centeredinteractiondesign 10.3.2 Crossareacalculationofship
tosupportandunderstandtheconceptionof domain 151
partogramamobile 125 10.3.3 Shipcollisionriskassessment 153
8.4 Identifyingneedsanddefining 10.4 Experimentalresultsandanalysis 154
requirements 126 10.4.1 TheverificationofMonteCarlo
8.4.1 Designofalternatives 129 probabilisticalgorithm 155
8.5 Buildinganinteractiveversion 10.4.2 Simulatethreesituationsofship
(high-fidelityprototype) 129 behavior 155
8.6 Evaluation(usability) 130 10.4.3 AISdataexperiment 155
8.7 Finalconsiderations 130 10.5 Conclusion 157
8.8 Teachingassignments 131 References 158
References 132
11. Neurofeedback using video games for
9. Self-evaluation mobile application on
attention-deficit/hyperactivity
mild cognitive impairment based on
disorder 161
Mini(cid:1)Mental State Examination with
NighatMir,MuhammadAsmatullahKhanandYumnaAnsari
bilingual support 135
11.1 Introduction 161
Lap-KeiLee,Yin-ChunFung,Nga-InWu,Ka-YuenLeung,
Tsz-KinTsangandChun-HoHo 11.2 ProblemsofADHD 162
11.3 Background 163
9.1 Introduction 135 11.3.1 Whyneurofeedback 163
9.1.1 Ourcontribution 136 11.3.2 Limitationsofneurofeedback 164
9.2 OverviewoftheMini(cid:1)MentalState 11.3.3 TreatmentsofADHD 164
Examination 136 11.3.4 Supportivetreatments 165
9.3 Ourmobileapplication 137 11.3.5 Neurofeedbacktraining 166
9.3.1 Overviewofthesolution 137 11.3.6 Neurofeedbacktreatment
9.3.2 Userinterfacedesignforseniors protocols 167
andtheelderly 138 11.3.7 Hypothesis 168
9.3.3 Questiontypesoftheevaluation 138 11.3.8 Datacollection 169
9.3.4 Recordtracking 139 11.3.9 Gamearchitecture 172
9.4 Preliminaryevaluation 140 11.4 Conclusionandfuturerecommendations 173
9.4.1 Evaluationwithusers 140 References 173
9.4.2 Discussionwithselectedusers 141 Furtherreading 175
9.4.3 Feedbacksfromnursingdomain
experts 142 12. Medical diagnosis in Alzheimer’s
9.5 Conclusionandfutureenhancement 142 disease based on supervised and
References 143
semisupervised learning 177
10. Spatiotemporal Big Data-Driven Vessel MingboZhao,YuanGao,ZhaoZhangandBingLi
Traffic Risk Estimation for Promoting
12.1 Introduction 177
Maritime Healthcare: Lessons Learnt from
12.2 Notationsandreviewofrelatedwork 179
Another Domain than Healthcare 145 12.2.1 Notations 179
12.2.2 Lineardiscriminantanalysis 179
ZikunFeng,YanLi,ZhaoLiuandRyanWenLiu
12.2.3 Reviewofgraph-basedsemisupervised
10.1 Introduction 145 learning 180
10.2 Shipdomain 148 12.3 Traceratiolineardiscriminantanalysisfor
10.3 Proposedmethod 149 medicaldiagnosis:acasestudyofdementiavia
10.3.1 Trajectorydatainterpolation 149 supervisedlearning 181
x Contents
12.3.1 Animprovedalgorithmsforsolving 13.5.4 Newknowledgeandskillsto
thetraceratioproblemof learn 203
TR-LDA 181 13.5.5 Urbanversusruralhealth 203
12.4 Identifyingdementedpatientsvia 13.5.6 Linkeddatabases 204
TR-LDA 184 13.5.7 Optimizingtreatment 204
12.4.1 Datadescriptions 184 13.5.8 Privacy 204
12.4.2 Predictionstage 184 13.6 Researchlimitationsandfutureresearch
12.5 Simulations 185 directions 205
12.5.1 Diagnosisresults 185 13.7 Visionsandconclusion 205
12.5.2 Visualization 186 References 206
12.6 Compactgraph-basedsemisupervisedlearning
formedicaldiagnosisinAlzheimer’sdisease: 14. COVID-19 detection from X-ray
acasestudyofdementiaviasemisupervised images using artificial intelligence 209
learning 187
AbdulhamitSubasi,SaqibAhmedQureshi,
12.6.1 Reviewofgraphconstruction 187
TayebBrahimiandAkilaSerireti
12.6.2 Identifyingdementedpatientsvia
compactgraphsemisupervised 14.1 Introduction 209
learning 190 14.2 DeeplearninginCOVID-19prognosisusing
12.6.3 Simulation 191 X-rayimages 213
12.7 Conclusion 193 14.3 Classificationmethods 217
References 193 14.3.1 Convolutionalneuralnetworks 217
Furtherreading 195 14.3.2 Transferlearning 218
14.4 Resultsanddiscussion 218
13. A support vector machine(cid:1)based 14.4.1 Dataset 218
14.4.2 Experimentalsetup 219
voice disorders detection using human
14.4.3 Performancemeasures 219
voice signal 197
14.4.4 Experimentalresults 219
PakHoLeung,KwokTaiChui,KennethLoand 14.4.5 Discussion 220
PatriciaOrdo´n˜ezdePablos 14.5 Conclusion 221
References 222
13.1 Introduction 197
13.2 Literaturereview 198 15. Empowering the One Health approach
13.3 Methodologyofsupportvector
and health resilience with digital
machine(cid:1)basedvoicedisorders
technologies across OECD countries:
detection 199
13.3.1 Programmingtool 199 the case of COVID-19 pandemic 225
13.3.2 VOIceICarfEDericoII(VOICED)
ParaskeviPapadopoulouandMiltiadisD.Lytras
database 199
13.3.3 Featureextraction 200 15.1 Introduction 225
13.3.4 Voicedisordersdetectionusing 15.2 Aimsandmethodologyofthisstudy 229
supportvectormachine 200 15.3 Findingsandsuggestionsregardingthe
13.4 Performanceevaluationofproposedsupport researchquestions 229
vectormachinealgorithmforvoicedisorders 15.3.1 TheCOVID19caseinOECD
detection 201 countries:somebackground
13.5 Researchchallengesofsmarthealth-care information 229
applications 202 15.3.2 Digitaltechnologiesintheserviceof
13.5.1 Datacollection 203 healthandhealthcare 230
13.5.2 Dataselection 203 15.3.3 Multidimensionalframeworkand
13.5.3 Expenditure 203 futurerecommendations 235
Contents xi
15.4 Conclusion 238 16.3.1 Health-caremonitoringandkeeping
References 239 well 247
Furtherreading 241 16.3.2 Diseasediagnosisandprediction 247
16.3.3 Drugdiscoveryand
16. An overview of artificial intelligence development 248
and big data analytics for smart healthcare: 16.3.4 Intensivecare 248
requirements, applications, and 16.3.5 Educationandtraining 249
challenges 243 16.4 Challenges 249
16.4.1 Large-scaleopenhealth-care
KwokTaiChui,MiltiadisD.Lytras,AnnaVisviziand data 249
AkilaSarirete
16.4.2 Technologytransfer 250
16.1 Introduction 243 16.4.3 PublicacceptanceinAI-and
16.2 Requirementsofsmarthealth-care BDA-basedapplications 250
applications 246 16.4.4 Policyestablishment 251
16.2.1 Missioncriticalapplications 246 16.5 Conclusion 251
16.2.2 Scalabledesign 246 References 251
16.2.3 Cost-effectivedesign 246
16.2.4 User-centereddesign 247 Index 255
16.3 Smarthealth-careapplicationsusingAIand
BDAtechniques 247