The Resource Personalized Psychiatry : Big Data Analytics in Mental Health, edited by Ives Cavalcante Passos, Benson Mwangi, Flávio Kapczinski, (electronic resource)

Personalized Psychiatry : Big Data Analytics in Mental Health, edited by Ives Cavalcante Passos, Benson Mwangi, Flávio Kapczinski, (electronic resource)

Label
Personalized Psychiatry : Big Data Analytics in Mental Health
Title
Personalized Psychiatry
Title remainder
Big Data Analytics in Mental Health
Statement of responsibility
edited by Ives Cavalcante Passos, Benson Mwangi, Flávio Kapczinski
Contributor
Editor
Editor
Provider
Subject
Language
eng
Summary
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
http://bibfra.me/vocab/relation/httpidlocgovvocabularyrelatorsedt
  • qK-_zYipCeI
  • Xe433asUEjo
  • FxDzvZbysBc
Image bit depth
0
LC call number
RC434.2-574
Literary form
non fiction
http://library.link/vocab/relatedWorkOrContributorName
  • Passos, Ives Cavalcante.
  • Mwangi, Benson.
  • Kapczinski, Flávio.
  • SpringerLink
http://library.link/vocab/subjectName
  • Psychiatry
  • Big data
  • Psychiatry
  • Big Data/Analytics
  • Big Data
Label
Personalized Psychiatry : Big Data Analytics in Mental Health, edited by Ives Cavalcante Passos, Benson Mwangi, Flávio Kapczinski, (electronic resource)
Instantiates
Publication
Antecedent source
mixed
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
not applicable
Content category
text
Content type code
txt
Content type MARC source
rdacontent
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
Dimensions
unknown
Extent
XV, 180 p. 24 illus., 21 illus. in color.
File format
multiple file formats
Form of item
electronic
Isbn
9783030035532
Level of compression
uncompressed
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other control number
10.1007/978-3-030-03553-2
Other physical details
online resource.
Quality assurance targets
absent
Reformatting quality
access
Specific material designation
remote
System control number
(DE-He213)978-3-030-03553-2
Label
Personalized Psychiatry : Big Data Analytics in Mental Health, edited by Ives Cavalcante Passos, Benson Mwangi, Flávio Kapczinski, (electronic resource)
Publication
Antecedent source
mixed
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
not applicable
Content category
text
Content type code
txt
Content type MARC source
rdacontent
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
Dimensions
unknown
Extent
XV, 180 p. 24 illus., 21 illus. in color.
File format
multiple file formats
Form of item
electronic
Isbn
9783030035532
Level of compression
uncompressed
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other control number
10.1007/978-3-030-03553-2
Other physical details
online resource.
Quality assurance targets
absent
Reformatting quality
access
Specific material designation
remote
System control number
(DE-He213)978-3-030-03553-2

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