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Computational Nosology and Precision Psychiatry
Open accessAbstract
This article provides an illustrative treatment of psychiatric morbidity that offers an alternative to the standard nosological model in psychiatry. It considers what would happen if we treated diagnostic categories not as causes of signs and symptoms, but as diagnostic consequences of psychopathology and pathophysiology. This reformulation (of the standard nosological model) opens the door to a more natural description of how patients present—and of their likely responses to therapeutic interventions. In brief, we describe a model that generates symptoms, signs, and diagnostic outcomes from latent psychopathological states. In turn, psychopathology is caused by pathophysiological processes that are perturbed by (etiological) causes such as predisposing factors, life events, and therapeutic interventions. The key advantages of this nosological formulation include (i) the formal integration of diagnostic (e.g., DSM) categories and latent psychopathological constructs (e.g., the dimensions of the Research Domain Criteria); (ii) the provision of a hypothesis or model space that accommodates formal, evidence-based hypothesis testing (using Bayesian model comparison); and (iii) the ability to predict therapeutic responses (using a posterior predictive density), as in precision medicine. These and other advantages are largely promissory at present: The purpose of this article is to show what might be possible, through the use of idealized simulations.
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“From the homo- geneity assumption, the comorbidity (17) and heterogeneity (18,19) problems emerge and limit psychiatric research (20–26).”
From Methods and challenges for assessing heterogeneity (2020) · cites this paper as [26]
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Metadata
- Authors
- Friston, K. J., Redish, A. D., & Gordon, J. A.
- Affiliations
- Wellcome Centre for Human Neuroimaging (GB)
- University of Minnesota (US)
- National Institute of Mental Health (US)
- Corresponding author
- Friston, Karl J.
- Venue
- Computational Psychiatry
- Publisher
- Ubiquity Press, Ltd.
- Published
- 2017-10-01
- Volume
- 1
- Issue
- 0
- Pages
- 2
- ISSN
- 2379-6227 (ISSN-L)
- PMC
- PMC5774181
- OpenAlex
- W2625121567
- Cited by
- 126
- References
- 52
- Citations by year
- 2026: 14 · 2025: 7 · 2024: 8 · 2023: 13 · 2022: 18 · 2021: 20 · 2020: 14 · 2019: 15 · 2018: 14 · 2017: 3
- FWCI
- 16.38
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Computational Psychiatry (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- Wellcome Trust (088130/Z/09/Z)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Social Sciences › Arts and Humanities › Philosophy › Mental Health and Psychiatry
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Keywords
- precision psychiatry, psychiatric diagnosis, psychopathology, pathophysiology, Research Domain Criteria, Bayesian model selection, posterior predictive distribution, treatment response prediction
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