Computational psychiatry is an emerging field that examines phenomena in mental illness
using formal techniques from computational neuroscience, mathematical psychology,
and machine learning (
1
,
2
,
3
,
4
,
5
,
6
). These techniques can be used in a theory-driven manner to gain insight into neural
or cognitive processes and in a data-driven way to identify predictive and explanatory
relationships in complex datasets. The approaches complement each other: theory-driven
models can be used to infer mechanisms, and the resulting measurements can be used
in data-driven approaches for prediction. Recent computational studies have successfully
described and measured novel mechanisms in a range of disorders (
7
,
8
,
9
,
10
,
11
), have framed disorders in new and informative ways (
12
), and have identified predictors of treatment response (
13
,
14
). These methods hold the potential to improve identification of relevant clinical
variables and could be superior to classification based on traditional behavioral
or neural data alone (
15
,
16
,
17
,
18
). However, these promising results have been slow to influence clinical practice
or to improve patient outcomes.To read this article in full you will need to make a payment
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Article info
Publication history
Published online: February 26, 2020
Accepted:
December 30,
2019
Received in revised form:
December 30,
2019
Received:
August 23,
2019
Identification
Copyright
© 2020 Society of Biological Psychiatry.
