期刊
CURRENT OPINION IN NEUROBIOLOGY
卷 55, 期 -, 页码 152-159出版社
CURRENT BIOLOGY LTD
DOI: 10.1016/j.conb.2019.02.006
关键词
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资金
- Medical Research Council Career Development Award [MR/N02401X/1]
- Brain & Behavior Research Foundation
- PS Fund
- UCLH NIHR BRC
- UCL
- Max Planck Society
- Wellcome Trust [203147/Z/16/Z]
- MRC [MR/N02401X/1] Funding Source: UKRI
Psychiatry is a medical field concerned with the treatment of mental illness. Psychiatric disorders broadly relate to higher functions of the brain, and as such are richly intertwined with social, cultural, and experiential factors. This makes them exquisitely complex phenomena that depend on and interact with a large number of variables. Computational psychiatry provides two ways of approaching this complexity. Theory-driven computational approaches employ mechanistic models to make explicit hypotheses at multiple levels of analysis. Data-driven machine-learning approaches can make predictions from high-dimensional data and are generally agnostic as to the underlying mechanisms. Here, we review recent advances in the use of big data and machine-learning approaches toward the aim of alleviating the suffering that arises from psychiatric disorders.
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