rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Personalized Psychiatry
    sub title: Big Data Analytics in Mental Health
  name:
    name part: Passos, Ives Cavalcante.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Mwangi, Benson.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Kapczinski, Flávio.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2019
    edition: 1st ed. 2019.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XV, 180 p. 24 illus., 21 illus. in color. online resource.
  abstract: 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. .
  table of contents: 1. Big data and Machine Learning Techniques Meet Health Sciences -- 2. Major challenges and limitations of Big data analytics -- 3. A Clinical Perspective on Big Data in Mental Health -- 4. Big Data Guided Interventions: Predicting Treatment Response -- 5. The role of big data analytics in predicting suicide -- 6. Emerging Shifts in Neuroimaging Data Analysis in the Era of "Big Data" -- 7. Phenomapping: methods and measures for deconstructing diagnosis in psychiatry -- 8. How to integrate data from multiple biological layers in mental health? -- 9. Ethics in the Era of Big Data.
  note: edited by Ives Cavalcante Passos, Benson Mwangi, Flávio Kapczinski.
  subject:
    topic: Psychiatry
  subject:
    topic: Big data
  subject:
    topic: Psychiatry
  subject:
    topic: Big Data/Analytics
  subject:
    topic: Big Data
  classification: RC434.2-574
  classification: 616.89
  related item:
    title info:
      title: Springer eBooks
  identifier: 9783030035532
  identifier: https://doi.org/10.1007/978-3-030-03553-2
  record info:
    record creation date: 190212
    record change date: 20190616021302.0
    record identifier: 978-3-030-03553-2