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Personalized Psychiatry

Big Data Analytics in Mental Health

  • Ives Cavalcante Passos
  • Benson Mwangi
  • Flávio Kapczinski
Book

Table of contents

  1. Front Matter
    Pages i-xv
  2. Ives Cavalcante Passos, Pedro Ballester, Jairo Vinícius Pinto, Benson Mwangi, Flávio Kapczinski
    Pages 1-13
  3. John Torous, Nikan Namiri, Matcheri Keshavan
    Pages 37-51
  4. Alexander Kautzky, Rupert Lanzenberger, Siegfried Kasper
    Pages 53-76
  5. Ronald C. Kessler, Samantha L. Bernecker, Robert M. Bossarte, Alex R. Luedtke, John F. McCarthy, Matthew K. Nock et al.
    Pages 77-98
  6. Danilo Bzdok, Marc-Andre Schulz, Martin Lindquist
    Pages 99-118
  7. Andre F. Marquand, Thomas Wolfers, Richard Dinga
    Pages 119-134
  8. Rogers F. Silva, Sergey M. Plis
    Pages 135-159
  9. Diego Librenza-Garcia
    Pages 161-172
  10. Back Matter
    Pages 173-180

About this book

Introduction

This book integrates the concepts of big data analytics into mental health practice and research.

Mental disorders represent a public health challenge of staggering proportions. According to the most recent Global Burden of Disease study, psychiatric disorders constitute the leading cause of years lost to disability. The high morbidity and mortality related to these conditions are proportional to the potential for overall health gains if mental disorders can be more effectively diagnosed and treated. In order to fill these gaps, analysis in science, industry, and government seeks to use big data for a variety of problems, including clinical outcomes and diagnosis in psychiatry. Multiple mental healthcare providers and research laboratories are increasingly using large data sets to fulfill their mission. Briefly, big data is characterized by high volume, high velocity, variety and veracity of information, and to be useful it must be analyzed, interpreted, and acted upon. As such, focus has to shift to new analytical tools from the field of machine learning that will be critical for anyone practicing medicine, psychiatry and behavioral sciences in the 21st century.   
             
Big data analytics is gaining traction in psychiatric research, being used to provide predictive models for both clinical practice and public health systems. As compared with traditional statistical methods that provide primarily average group-level results, big data analytics allows predictions and stratification of clinical outcomes at an individual subject level.

Personalized Psychiatry – Big Data Analytics in Mental Health provides a unique opportunity to showcase innovative solutions tackling complex problems in mental health using big data and machine learning. It represents an interesting platform to work with key opinion leaders to document current achievements, introduce new concepts as well as project the future role of big data and machine learning in mental health. 

Keywords

Big data Psychiatry Mental health Personalized care Machine learning Predictive analytics Web-based risk calculators Data science Precision psychiatry Precision medicine

Editors and affiliations

  • Ives Cavalcante Passos
    • 1
  • Benson Mwangi
    • 2
  • Flávio Kapczinski
    • 3
  1. 1.Laboratory of Molecular PsychiatryHospital de Clinicas de Porto AlegrePorto AlegreBrazil
  2. 2.UT Center of Excellence on Mood Disorders, Department of Psychiatry and Behavioral SciencesThe University of Texas Health Science Center at Houston, McGovern Medical SchoolHoustonUSA
  3. 3.Department of Psychiatry and Behavioural NeurosciencesMcMaster UniversityHamiltonCanada

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-03553-2
  • Copyright Information Springer Nature Switzerland AG 2019
  • Publisher Name Springer, Cham
  • eBook Packages Medicine
  • Print ISBN 978-3-030-03552-5
  • Online ISBN 978-3-030-03553-2
  • Buy this book on publisher's site
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