Artificial Intelligence and Data Mining in Healthcare PDF
Preview Artificial Intelligence and Data Mining in Healthcare
Malek Masmoudi Bassem Jarboui Patrick Siarry Editors Artificial Intelligence and Data Mining in Healthcare Artificial Intelligence and Data Mining in Healthcare Malek Masmoudi • Bassem Jarboui • Patrick Siarry Editors Artificial Intelligence and Data Mining in Healthcare Editors MalekMasmoudi BassemJarboui LASPI,G037 DepartmentofBusiness IUTdeRoanne HigherCollegesofTechnology Roanne,France AbuDhabi,UnitedArabEmirates PatrickSiarry LaboratoireLiSSi(EA3956) UniversitéParis-EstCréteilVal-de-Marne Créteil,France ISBN978-3-030-45239-1 ISBN978-3-030-45240-7 (eBook) https://doi.org/10.1007/978-3-030-45240-7 ©SpringerNatureSwitzerlandAG2021 Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpartof thematerialisconcerned,specificallytherightsoftranslation,reprinting,reuseofillustrations,recitation, broadcasting,reproductiononmicrofilmsorinanyotherphysicalway,andtransmissionorinformation storageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilarmethodology nowknownorhereafterdeveloped. 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 Ourhealthcaresystemsarefacingunprecedentedchallengesandparticularlyserious economic pressure. Promising alternativesto the traditional healthcare system are beingdevelopedto improvethe qualityofservice andto reducecost. Researchers in the operations research and artificial intelligence communities are involved to provideefficient healthcare decision supportsystems to help healthcare engineers and managers to make optimal, efficient decisions. How to improve the whole healthcare system performance has become the main issue for researchers from medical, technical, organizational, and decisional points of view. The need for intelligent supportsystems for decision-makingis growing in differenthealthcare fields. The domain is complex and very rich in terms of scientific niches which attract researchers in both the operations research (OR) and artificial intelligence (AI)disciplines. This book, “Artificial Intelligence and Data Mining in Healthcare,” presents recentstudiesandworkinhealthcaremanagementandengineeringusingartificial intelligence and data mining techniques. It focuses on mainly exposing readers to cutting-edge research and applications that are ongoing across the domain of healthcaremanagementandengineeringwhereartificialanddataminingtechniques canbeandhavebeensuccessfullyemployed. Need for aBookon theProposed Topics To the best of our knowledge, there is no book aiming precisely at regrouping artificial intelligence and data mining techniques for healthcare decision-making problems, and the number of book chapters dedicated to this subject is tiny. However,thistopicishighlytopicalandinterestsmanyresearchers,whichexplains the high number of journal papers and international conferences communications dedicatedtothissubject. This provides readers with AI and data mining tools for solving healthcare decision-makingproblems.Itexplainsawealthofbothbasicandadvancedconcepts v vi Preface of AI and data mining applied to organizational tasks such as patient work- flow, capacity/resource management, logistics, medical image compression, life expectancy,etc.Thechaptersincluderelevantcasestudies. Organization oftheBook This book is organized into nine chapters. A brief description of each chapter is givenbelow. The chapter “Artificial Intelligencefor Healthcare Logistics: An Overview and Research Agenda” by M. Reuter-Oppermann and N. Kühl examines the existing literatureonartificialintelligenceandmachinelearningapproachesforthelogistical problemsthatarisewhenwedesign,provide,andimprovehealthcareservices.For the analysis, we distinguish between the planning levels (strategic, tactical, and operational),thecarelevel(primary,secondary,andtertiarycare),andtheresource types(doctors,nurses,technicians,patients,etc.).Basedontheresultsweprovidea researchagendawithopentopicsandfuturechallenges. The chapter “Synergy Between Predictive Mining and Prescriptive Planning of Complex Patient Pathways Considering Process Discrepancies for Effective Hospital-Wide Decision Support” by T. Mellouli and T. Stoeck considers deci- sion making and decision support tasks for planning complex patient-centered clinicalpathwaysin a complexhospitalenvironment,demarcatedby manywards, sharedresources,andmanyotherinterdependencies.Introducingatwo-dimensional scheme with these complexity dimensions, many AI- and OR-oriented tasks in hospitals are classified and several facets of AI/OR synergy for their effective solution are detected. The first type of AI/OR synergy forwards process mining results of complex pathways (AI) to prescriptive optimization models (OR). Case studies of a university hospital show business benefits and better results quality. Basedonaprofounddiscussion,asecondhiddentypeofAI/ORsynergyisdetected, wherehard-to-modelinterdependenciescanbetakenintoconsideration.Theopera- tionalizationofthisAI/ORsynergyisbasedonaprocedurefordiscrepancymining (AI) which is embedded with a prescriptive model (OR) into a plan-and-refine framework. The chapter “Real-Time Capacity Managementand Patient Flow Optimization in Hospitals Using AI Methods” by J.R. Munavalli, H.J. Boersma, S.V. Rao, and G.G.vanMerodedemonstrateshowoptimizationmodelsbasedonmodernartificial intelligence (AI) techniques would manage hospital workflow through decision- makingsystems that are dynamic,robust, and real-time. In particular,multi-agent systems and ant colony optimization have the potential to convert traditional workflowmanagementintointelligentandefficientworkflowmanagementsystems thatimprovehospitalperformance.TheapplicationofAItooperationsmanagement isdemonstratedwithexamplesfromhospitals. The chapter “How Healthcare Expenditure Influences Life Expectancy: Case Study on Russian Regions” by N. Mladenovic, O. Rusetskaya, S. Elleuch, and Preface vii B. Jarbouiexaminesthe influenceof healthcaresupportsof differentkindson life expectancy.Dataarecollectedonall85geographicaldistrictsinRussia,coveringa 15-yearperiod.A symbolicregressionmodelis appliedand solvedusing variable neighborhoodprogramming,arecentpromisingautomaticprogrammingtechnique. Inotherwords,theanalyticfunctionissearchedtopresenttherelationshipbetween life expectancy and a few selected healthcare financial attributes. Some years are usedasatrainingsetandsomeasatestingset.Interestingresultsareobtainedand analyzed.Theyconfirmthefactthatsymbolicregressionandartificialintelligence techniquesmightbetherightapproachtoestimatinglifeexpectancy. Thechapter“OperatingTheaterManagementSystem:Block-Scheduling”byB. BouSaleh,G.BouSaleh,andO.Barakatdealswithblock-schedulinginoperating theatermanagementusingaMILPandadistributedartificialintelligenceapproach. Theprovidedapproachtakes intoconsiderationvariationsin doctorandoperating roomavailabilities. A realcase studyisconsideredto makesimulationsthatshow thesuperiorityofthedistributedartificialmodelincomparisonwithMILP. Thechapter“AnImmuneMemoryandNegativeSelectiontoVisualizeClinical PathwaysfromElectronicHealthRecordData”byM.Berquedich,O.Kamach,M. Masmoudi, and L. Deshayes providesa data-drivenclinical practice development methodologyto extract common clinical pathways from patient-centric electronic healthrecorddata.Analgorithmicmethodologyisproposedtohandlethistypeof routinedata.Inthischapter,theauthorsdesignasystemofcontrolandanalysisof patient records based on an analogy between the elements of the new electronic health records (EHR) and biological immune systems. The detection of patient profiles is handled using bi-clusters. The authors rely on biological immunity to developasetofmodelsforstructuringknowledgeaboutEHRandpathwayanalysis decisions.A specific analysis ofthe functionaldata led to the detectionof several typesofpatientswhosharethesameinformationontheirEHR.Thismethodology demonstratesitsabilitytosimultaneouslyprocessdataandprovideinformationfor understandingandidentifyingthepathofpatientsaswellaspredictingthepathof futurepatients. Thechapter“OptimizedMedicalImageCompressionforTelemedicineApplica- tions”byK.M.Hosny,A.M.Khalid,andE.R.Mohamedprovidesanalgorithmfor highlyefficientcompressionof2D medicalimagesthatuse Legendremomentsto extract features and differential evolution (DE) to select which of these moments are the optimum. The proposed algorithm aims to achieve the best-reconstructed imagequality.Medicalimagesfromdifferentimagingmodalitiessuchasmagnetic resonance imaging (MRI), computed tomography (CT), and X-ray Images are used in testing the proposed algorithm. The mean square error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and normalizedcorrelationcoefficient(NCC)arequantitativemeasuresusedtoevaluate theperformanceoftheproposedalgorithmandwell-knownexistingmedicalimage compression methods. The quality of the reconstructed compressed images using theproposedmethodismuchbetterthanthosecompressedusingconventional2D compressionalgorithms. viii Preface The chapter “Online Variational Learning Using Finite Generalized Inverted DirichletMixtureModelwithFeatureSelectiononMedicalDataSets”byM.Kalra and N. Bouguila provides a statistical framework for online variational learning usingthefinitegeneralizedinvertedDirichletmixturemodelforclusteringmedical images data by simultaneously using feature selection and image segmentation. The model allows one to adjust the mixture model parameters, the number of components, and the feature weights to tackle the challenge of overfitting. The algorithm in this study has been evaluated on synthetic data as well as in three medical applications for brain tumor detection, skin melanoma detection, and computer-aideddetectionofmalaria. Thechapter“Entropy-BasedVariationalInferenceforSemi-boundedDataClus- tering in Medical Applications” by N. Manouchehri, M. Rahmanpour, and N. Bouguila considers inverted Dirichlet mixture models for semi-bounded positive vectors clustering. An entropy-based variational approach is developed to test whethereachcomponentistrulydistributedasaninvertedDirichlet.Toaccomplish thisgoal,thetheoreticalentropyofeachcomponentiscomparedwiththeestimated one and the component with the maximum differential is chosen to be split. The providedapproachwillbetestedinreal-worldmedicalapplicationsandcompared withtheconventionaltechniques. Audience Thisbookisvaluableforresearchersandmaster’sandPhDstudentsindepartments of computer science, information technology, industrial engineering, and applied mathematics,andinparticularforthoseengagedwithAIanddataminingtopicsin thehealthcaredomain. Roanne,France MalekMasmoudi AbuDhabi,UAE BassemJarboui Créteil,France PatrickSiarry Contents 1 ArtificialIntelligenceforHealthcareLogistics:AnOverview andResearchAgenda........................................................ 1 MelanieReuter-OppermannandNiklasKühl 1.1 Introduction ............................................................. 1 1.2 MachineLearningandArtificialIntelligence.......................... 2 1.2.1 MachineLearning .............................................. 2 1.2.2 ArtificialIntelligence........................................... 3 1.2.3 WorkingDefinition ............................................. 4 1.3 FrameworkforHealthcareLogisticsLiterature ....................... 5 1.3.1 PlanningLevels................................................. 5 1.3.2 CareLevels...................................................... 6 1.3.3 UserTypes ...................................................... 6 1.3.4 Framework...................................................... 7 1.4 LiteratureReview ....................................................... 8 1.4.1 AIforOptimisationInput ...................................... 9 1.4.2 AIforHealthcareLogisticsOptimisation ..................... 11 1.4.3 AIforEDLogistics............................................. 13 1.4.4 SynthesisandResearchAgenda ............................... 15 1.5 Conclusion .............................................................. 15 References..................................................................... 16 2 AI/OR SynergiesofProcessMining withOptimalPlanning ofPatientPathwaysforEffectiveHospital-WideDecisionSupport.... 23 TaïebMellouliandThomasStoeck 2.1 MotivationandResearchOutline ...................................... 23 2.1.1 AI/ORSynergiesmeetHospitalDecisionTask Complexities.................................................... 24 2.1.2 PathwayCenteredDecisionSupportTowardAI/OR Synergy.......................................................... 25 2.1.3 ResearchonAI/ORSynergyandChapterOutline............ 27 ix x Contents 2.2 FirstTypeofAI/ORSynergy:ProcessMiningofPathways forAccuratePrescriptivePlanningofWard-and-BedAllocation..... 28 2.2.1 Synergy between Predictive and Prescriptive Analytics:CasesofSimplevs.ComplexStructures.......... 28 2.2.2 First Type of AI/OR Synergy and Its Benefits forEffectiveHospitalDecisionSupport:CaseStudy ofaUniversityHospital ........................................ 30 2.3 Detecting AI/OR Synergies Within Hospital Decision Support:Interdependencies,Dimensionsof Complexity, Two-DimensionalScheme,andTypesofAI/ORSynergy............ 33 2.3.1 TypesofInterdependencies:FirstGroup...................... 33 2.3.2 DimensionsofComplexityandOverviewAboutOR andAITasksandSynergies.................................... 36 2.3.3 A New Two-Dimensional Scheme for Simulation-/Optimization-Based Decision Support in Hospitals Applied to Overall Bed ManagementinInterdependentWards......................... 38 2.3.4 AITasksandAI/AISynergy:StepwiseAggregation fromProcessMiningtoMoreAccurateHospitalData Mining........................................................... 41 2.3.5 ORTasksandOR/ORSynergies............................... 42 2.3.6 FirstTypeofAI/ORSynergyandDetecting aSecondType................................................... 43 2.4 Second Type of AI/OR Synergy: Mining of Process DiscrepanciesandItsInterplaywithPrescriptivePlanning TowardEffectiveHospital-WideDecisionSupport ................... 46 2.4.1 Types of Interdependencies: Second Group andModel–RealityGap ........................................ 46 2.4.2 MiningProcessDiscrepanciesbyType ofInterdependency ............................................. 49 2.4.3 InterplayBetween MiningProcessDiscrepancies with PrescriptivePlanningandOperationalization of the Second Type of AI/OR Synergy byaDiscrepancy-DrivenApproach............................ 50 2.5 Conclusion .............................................................. 52 References..................................................................... 53 3 Real-Time Capacity Management and Patient Flow OptimizationinHospitalsUsingAIMethods............................. 55 Jyoti R. Munavalli, Henri J. Boersma, Shyam Vasudeva Rao, andG.G.vanMerode 3.1 Introduction ............................................................. 56 3.2 CapacityManagementinHospitals.................................... 56 3.2.1 TraditionalHospitalCapacityManagement................... 56 3.2.2 QueuingandSynchronizationinHospitals.................... 57
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