Journal
BRITISH JOURNAL OF PSYCHIATRY
Volume 220, Issue 4, Pages 175-178Publisher
CAMBRIDGE UNIV PRESS
DOI: 10.1192/bjp.2022.23
Keywords
Precision psychiatry; machine learning; biomarkers; risk assessment; treatment response
Categories
Funding
- UK Research and Innovation (UKRI) Medical Research Council (MRC) Psychosis Immune Mechanism Stratified Medicine Study (PIMS) [UKRI MR/S037675/1]
- Brain and Behavior Research Foundation [28474]
- EU-FP7 project PRONIA ('Personalised Prognostic Tools for Early Psychosis Management') [602152]
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Progress in personalised care for mental disorders is supported by machine learning studies, but the generalisability and clinical implementation of models need further testing and validation. Collaboration among clinicians, researchers, and patients is essential to generate large-scale data for real-world psychiatric care.
Progress in developing personalised care for mental disorders is supported by numerous proof-of-concept machine learning studies in the area of risk assessment, diagnostics and precision prescribing. Most of these studies primarily use clinical data, but models might benefit from additional neuroimaging, blood and genetic data to improve accuracy. Combined, multimodal models might offer potential for stratification of patients for treatment. Clinical implementation of machine learning is impeded by a lack of wider generalisability, with efforts primarily focused on psychosis and dementia. Studies across all diagnostic groups should work to test the robustness of machine learning models, which is an essential first step to clinical implementation, and then move to prospective clinical validation. Models need to exceed clinicians' heuristics to be useful, and safe, in routine decision-making. Engagement of clinicians, researchers and patients in digitalisation and 'big data' approaches are vital to allow the generation and accessibility of large, longitudinal, prospective data needed for precision psychiatry to be applied into real-world psychiatric care.
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