Personalized Psychiatry: Big Data Analytics in Mental Health

190 Pages·2019·3.26 MB·English

by  Ives Cavalcante Passos

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.
Theuseofgeneraldescriptivenames,registerednames,trademarks,servicemarks,etc.inthispublication
doesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfromtherelevant
protectivelawsandregulationsandthereforefreeforgeneraluse.
Thepublisher,theauthors,andtheeditorsaresafetoassumethattheadviceandinformationinthisbook
arebelievedtobetrueandaccurateatthedateofpublication.Neitherthepublishernortheauthorsor
theeditorsgiveawarranty,expressorimplied,withrespecttothematerialcontainedhereinorforany
errorsoromissionsthatmayhavebeenmade.Thepublisherremainsneutralwithregardtojurisdictional
claimsinpublishedmapsandinstitutionalaffiliations.
ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSwitzerlandAG.
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