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020 _a9783030035532
024 7 _a10.1007/978-3-030-03553-2
_2doi
040 _bspa
_cES-MaUEC
_dES-MaUEC
050 4 _aRC454
_b.P47 2019EB
245 0 0 _aPersonalized Psychiatry.
_bBig Data Analytics in Mental Health
_cedited by Ives Cavalcante Passos, Benson Mwangi, Flávio Kapczinski.
264 1 _aCham
_bSpringer International Publishing :
_bImprint: Springer
_c2019.
300 _aXV, 180 páginas 24 ilustraciones, 21 ilustraciones a color
336 _2rdacontent
_aTexto (visual)
_btxt
337 _2rdamedia
_aelectrónico
_bc
338 _2rdacarrier
_arecurso electrónico
_bcr
347 _atext file
_bPDF
_2rda
490 0 _aMedicine (Springer-11650)
505 0 _a1. 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.
520 3 _aThis 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. .
650 1 4 _aPsychiatry.
_0http://scigraph.springernature.com/things/product-market-codes/H53003
_9547589
653 0 _aPsychiatry.
653 0 _aBig data.
700 1 _aPassos, Ives Cavalcante.
_eeditor literario
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aMwangi, Benson.
_eeditor literario
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aKapczinski, Flávio.
_eeditor literario
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
_9106937
776 0 8 _iPrinted edition:
_z9783030035525
776 0 8 _iPrinted edition:
_z9783030035549
856 4 0 _uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-03553-2
_zAcceso a este recurso digital (usuarios Universidad Europea de Valencia)
942 _2lcc
_cLE
998 _db
_el