@article{ref1,
title="Psychiatric disorders as failures in the prediction machine (editorial)",
journal="Psychiatry and the Clinical Neurosciences",
year="2021",
author="Yamashita, Yuichi",
volume="75",
number="1",
pages="1-2",
abstract="Due to drastic improvements in computer power and the refinement of machine learning (ML) theories and artificial intelligence (AI) technologies, theoretical and mathematical methods are expected to contribute to psychiatry and clinical neuroscience. This emerging field of research is referred to as 'computational psychiatry.'1 There are two major strategies for computational psychiatry. The first is trying to discover covert regularity from biological, neurological, and behavioral 'big data' related to psychiatric disorders using ML/AI techniques, which is referred to as the data‐driven approach. Although big data and cutting‐edge ML/AI techniques are used, the data‐driven approach is still an extension of conventional methods, in the sense that it explores correspondences (correlations) between observed data and phenotypes and does not examine information processing within the brain itself. Therefore, the data‐driven approach alone may be insufficient to overcome the fundamental difficulties in investigating mental illness, such as biological nonspecificity and heterogeneity. In order to address this issue, there is another line of approach in computational psychiatry, referred to as a theory‐driven approach, in which mental disorders are modeled as aberrant information processing ('computation') in the brain using mathematical formulations. The theory‐driven approach is expected to provide mechanistic explanations bridging the different levels of biological observations, including genes, molecules, cells, neural circuits, physiology, behaviors, and symptoms...<p /> <p>Language: en</p>",
language="en",
issn="1323-1316",
doi="10.1111/pcn.13173",
url="http://dx.doi.org/10.1111/pcn.13173"
}