Personalized Psychiatry: Big Data Analytics in Mental Health PDF
Preview Personalized Psychiatry: Big Data Analytics in Mental Health
Personalized Psychiatry Big Data Analytics in Mental Health Ives Cavalcante Passos Benson Mwangi Flávio Kapczinski Editors 123 Personalized Psychiatry Ives Cavalcante Passos • Benson Mwangi Flávio Kapczinski Editors Personalized Psychiatry Big Data Analytics in Mental Health 123 Editors IvesCavalcantePassos BensonMwangi LaboratoryofMolecularPsychiatry UTCenterofExcellenceonMoodDisorders HospitaldeClinicasdePortoAlegre DepartmentofPsychiatryandBehavioral PortoAlegre,Brazil Sciences TheUniversityofTexasHealthScience Programa de Pós-Graduação em Psiquiatria CenteratHouston eCiênciasdoComportamento McGovernMedicalSchool UniversidadeFederaldoRioGrandedoSul Houston,TX,USA PortoAlegre,Brazil FlávioKapczinski DepartmentofPsychiatryandBehavioural Neurosciences McMasterUniversity Hamilton,ON,Canada ISBN978-3-030-03552-5 ISBN978-3-030-03553-2 (eBook) https://doi.org/10.1007/978-3-030-03553-2 LibraryofCongressControlNumber:2018968426 ©SpringerNatureSwitzerlandAG2019 Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpartof thematerialisconcerned,specificallytherightsoftranslation,reprinting,reuseofillustrations,recitation, broadcasting,reproductiononmicrofilmsorinanyotherphysicalway,andtransmissionorinformation storageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilarmethodology nowknownorhereafterdeveloped. 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Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland Foreword BigDataIsWatchingYou Theseareexcitingtimesinthehistoryofpsychiatryforanumberofreasons.First and foremost, with mapping of the brain and functioning of various parts, it is gettingclosertoourunderstandingofcognitionsandemotions.Bothresearchersand cliniciansarebeginningtounderstandtheroleofgenomeandpsychopharmacoge- nomics is beginning to guide prescription patterns of psychiatric diseases. Trials are under way to indicate which of our patients are fast metabolisers and which are slow metabolisers so that targeted doses of medication can be used in gaining theoptimumeffect.Atonelevel,psychiatryhasalwaysbeenpersonalisedbecause the patients sitting in front of us even with similar symptoms have very different responses to therapeutic interactions. Who will respond to which treatment needs big data. With an increase in the use of social media, personal apps for managing some distress and symptoms, the importance of data and information cannot be underestimated.Oneoftheearliestinterventionsinpsychiatrywaspsychoanalysis analysing the individual to make sense of their experiences and development. The practiceofpsychiatryhasmovedonfromanalysisbyhumanbeingstoanalysisof databymachineswhichhasitsadvantagesanddisadvantages. Various authors in this volume remind us that human beings have always been interested in big data. Data is collected on individuals from birth to death. Some countrieshavemajordatasetsoneachcitizencreatingthousandsofvariableswhich canenableustomakesenseofindividualexperiencesinthecontextoflargersocial structuresbetheyhealthorsocialcare. Predictive psychiatry is an exciting new field where using large data sets may allow us to predict responses and outcomes. Machines such as smartphones and computers are an integral part of human functioning and human lives. Designed algorithmstellusthatifwelikedaparticularbookorsong,wearelikelytoprefer book B or song B. These algorithms can be helpful. In the recent WPA-Lancet PsychiatryCommissionontheFutureofPsychiatry(Bhugraetal.2017),oneofthe recommendationswasthatpsychiatristsneedtobeup-to-dateintheevolvingdigital world bearing in mind the potential risks of commercialised unproven treatments andinterventions.However,aslongaswidercollaborationbetweenstakeholdersis maintained,itshouldbepossibletoreaptherewardsofdigitalpsychiatry,andthis v vi Foreword volume provides an excellent example of that. Widely used digital tools and their ability to collect huge data sets or deliver services related to mental and physical health are only now beginning to be realised. The reality of digital psychiatry is certainly not without its challenges, and authors in this volume tackle these head- on. Inclinicalpsychiatry,therehasbeenalongtraditionofanalysinghistoryandthe patientinthecontextoftheirdevelopment,andatonelevel,itappearsfrightening andscarythatmachinescandothisforourdecisionsbetheyclinicalornonclinical. In the past 2 decades, computers, smartphones, and social media algorithms have bothenrichedourlivesandalsoproducedafeelingofconcernastowherethismight lead. These interactions are based on algorithms which are also used in clinical decision-making relying on evidence based more so in some medical specialities ratherthanothers.Digitalpsychiatrycancontributeatremendousamountofsupport to clinicians especially when patients and their doctors live miles apart. There are alreadyinnovativepracticesusinge-mentalhealthandtele-mentalhealthpractices inmanypartsoftheworld. The access to new technologies may well vary across countries, but with an increaseduseofsmartphonesaroundtheworldmeansthatlevelsofphysicalactiv- ities, pulse rates and blood pressure can be easily measured and monitored. New technologies may enable mental health and physical health to be integrated more readily than has been the case so far. As is clear from contributions to this unique and excellent volume, the data sets generated from the use of machines such as smartphonesandlaptopscanhelpusmakesenseofwellbeingofindividuals.Thus, closecollaborationbetweendatascientistsandpsychiatristsaswellasothermental health professionalsiscriticaltohelpdevelop algorithms forfutureunderstanding ofpersonalisedclinicalpractice.Thisvolumeoffersauniqueviewpointandinsight onthejourneyinscientificdevelopmentofpsychiatry. Big data on the one hand comprises of velocity, volume, and variety which are readilyvisibleinouruseofsmartphones.Asseveralauthorsinthisvolumeremind us,thedatacanbestored,andyetrapidaccesstobillionsofdatasetswithcapacity increases on a daily basis. As is strongly emphasised in this volume, big data for psychiatry is unlike any other. Data related to investigations including brain scans and other neuroimaging studies can also contribute to big data. Big data can also help collect large sets of phenotypes to facilitate our understanding of biological causesofmentalillnessesandenablesuitablepersonalisedinterventions.Thesedata sets can facilitate development of individualised nosology of psychiatric disorders perhapsmovingawayfromone-size-fits-allphenomenology. Ofcourse,therearecriticalissuesrelatedtoconfidentiality,probity,andsecurity in data collection and data management of clinical matters. On the other hand, patients do not fit into tight categories of the machine-generated algorithms. Such informationshouldbeseenassupplementarysourcesofinformation,e.g.ascertain- ingphysicalactivitiesandnotonlyinformationwhilereachingaclinicaldiagnosis or planning therapeutic interventions. However, it is also important that clinicians aretaughtandtrainedhowtousetheseresourcesproperlyandappropriately. Foreword vii Theeditorsandauthorsinthissplendidvolumearetobecongratulatedfortheir visionandpioneeringspiritwhichhopefullywillleadtobetter,individualised,and focusedcareofpatientswithpsychiatricproblems. Reference Bhugra D, Tasman A, Pathare S, et al (2017) The WPA-lancet commission on the futureofpsychiatry.LancetPsychiatry4:775–818 EmeritusProfessor,MentalHealthandCulturalDiversity DineshBhugra IoPPN,KingsCollege,London,UK Preface This book was written to address the emerging need to deal with the explosion of information available about individual behaviours and choices. Importantly, we believethattherearestilluntappedopportunitiestotransformsuchinformationinto intelligencethatwouldenablepersonalisedcareinmentalhealth. Our unprecedented ability to gain knowledge about each individual will be paramountinallowingustoimplementpersonalisedcareinmentalhealth.Ground- breakingdiscoveries andchanges atthepopulation levelwillinvolvedataintegra- tion enabling a person-centred approach. Big data tools will be needed to assess the phenome, genome, and exposome of patients. That will include data from imaging, insurance, pharmacy, social media, as well as -omics data (genomics, proteomics,andmetabolomics).Briefly,bigdataarecharacterisedbyhighvolume, high velocity, and variety. We believe therefore that attention has to shift to new analytical tools from the field of machine learning and artificial intelligence that willbecriticalforanyonepracticingmedicine,psychiatry,andbehaviouralsciences inthetwenty-firstcentury. Integrationofdatafrommultiplelevelscanbetranslatedintoclinicalpracticeby boththegenerationofhomogeneousgroupsofpatientsandtheuseofcalculatorsto accurately predict outcomes at an individual level. That will facilitate important clinical decisions. An inventive approach to big data analytics in mental health will be needed to translate data from large and complex datasets into the care of consumers. That will transform predictions and information into a greater understandingofriskassessmentandbettermentalhealthcare. Personalisedinterventionswillbetheoutcomeofthedevelopmentofthisfield. Innovativemethodsforriskassessmentwillallowthedevelopmentofpersonalised interventions at the level of prevention, treatment, and rehabilitation. A creative approach to big data analytics in mental health will be crucial in promoting, generating, and testing new interventions for mental health problems. Big data analytics will be at the core of the next level of innovation in mental health care. Thus,ourvisionforthefutureisaworldinwhichmentalhealthprofessionalswill have the tools to deal with multilevel information that will provide patients and caregiverswiththeintelligenceneededtoenablebettercare. This book will benefit clinicians, practitioners, and scientists in the fields of psychiatry, psychology, and behavioural sciences and ultimately patients with ix x Preface mental illness. We also intend to reach graduate and undergraduate students in these fields. Our main aims are (1) to empower researchers with a different way to conceptualise studies in mental health by using big data analytics approaches; (2) to provide clinicians with a broad perspective about how clinical decisions suchastreatmentoptions,preventivestrategies,andprognosisorientationswillbe transformedbybigdataapproaches;(3)toprovideauniqueopportunitytoshowcase innovativesolutionstacklingcomplexproblemsinmentalhealthusingbigdataand machinelearning;and(4)todiscusschallengesintermsofwhatdatacouldbeused withoutjeopardisingindividualprivacyandfreedom. This volume has a total of nine chapters, which are structured as follows: Chapter 1 introduces the concepts of big data and machine learning and also provides a historical perspective of how big data analytics meet health sciences. Chapter 2 explores the challenges and limitations of machine learning—the most important technique to analyse big data. Chapter 3 provides a clinical perspective on big data in mental health. Chapters 4 and 5 present the state of art of tools to predict treatment response and suicide, respectively. Chapter 6 explores the emergingshiftsinneuroimagingdataanalysis,whileChapter7discussesmethods, such as unsupervised machine learning, for deconstructing diagnosis in mental health. Chapter 8 describes how to integrate data from multiple biological layers tobuildmultimodalsignatures.Lastly,Chapter9addressesethicsintheeraofbig data. Contributorsofthisbookaretrueleadersofthisemergingfieldandarefosteringa revolutionfromtheexistingevidencemedicineandtraditionalaveragegroup-level studies to the current personalised care scenario. In this new paradigm, large and complex datasets will be digested into calculators and predictive tools. These will provide clinicians with real-time intelligence that will guide personalised care in mentalhealth. PortoAlegre,RS,Brazil IvesCavalcantePassos Houston,TX,USA BensonMwangi Hamilton,ON,Canada FlávioKapczinski Contents 1 BigDataandMachineLearningMeettheHealthSciences............. 1 Ives Cavalcante Passos, Pedro Ballester, Jairo Vinícius Pinto, BensonMwangi,andFlávioKapczinski 2 MajorChallengesandLimitationsofBigDataAnalytics............... 15 BoCaoandJimReilly 3 AClinicalPerspectiveonBigDatainMentalHealth.................... 37 JohnTorous,NikanNamiri,andMatcheriKeshavan 4 BigDataGuidedInterventions:PredictingTreatmentResponse ...... 53 AlexanderKautzky,RupertLanzenberger,andSiegfriedKasper 5 TheRoleofBigDataAnalyticsinPredictingSuicide.................... 77 Ronald C. Kessler, Samantha L. Bernecker, Robert M. Bossarte, Alex R. Luedtke, John F. McCarthy, Matthew K. Nock, Wilfred R. Pigeon, Maria V. Petukhova, Ekaterina Sadikova, TylerJ.VanderWeele,KellyL.Zuromski,andAlanM.Zaslavsky 6 EmergingShiftsinNeuroimaging DataAnalysisintheEra of“BigData” ................................................................. 99 DaniloBzdok,Marc-AndreSchulz,andMartinLindquist 7 Phenomapping: MethodsandMeasuresforDeconstructing DiagnosisinPsychiatry...................................................... 119 AndreF.Marquand,ThomasWolfers,andRichardDinga 8 HowtoIntegrateDatafromMultipleBiologicalLayers inMentalHealth?............................................................ 135 RogersF.SilvaandSergeyM.Plis 9 EthicsintheEraofBigData ............................................... 161 DiegoLibrenza-Garcia Index............................................................................... 173 xi
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