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Personalized psychiatrybig data anal...

Kapczinski, Flavio.

Personalized psychiatrybig data analytics in mental health /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Personalized psychiatryedited by Ives Cavalcante Passos, Benson Mwangi, Flavio Kapczinski.
其他題名:	big data analytics in mental health /
其他作者:	Passos, Ives Cavalcante.
出版者:	Cham :Springer International Publishing :2019.
面頁冊數:	xv, 180 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	PsychiatryData processing.
電子資源:	https://doi.org/10.1007/978-3-030-03553-2
ISBN:	9783030035532$q(electronic bk.)

Personalized psychiatrybig data analytics in mental health /
Personalized psychiatrybig data analytics in mental health /[electronic resource] :edited by Ives Cavalcante Passos, Benson Mwangi, Flavio Kapczinski. - Cham :Springer International Publishing :2019. - xv, 180 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9783030035532$q(electronic bk.)

Standard No.: 10.1007/978-3-030-03553-2doiSubjects--Topical Terms:

677091
Psychiatry
--Data processing.

LC Class. No.: RC455.2.D38

Dewey Class. No.: 616.8900285

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505 0 $a 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.
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