The promise of a model-based psychiatry: building computational models of mental ill health. Issue 11 (November 2022)

• Record Type: Journal Article
• Authors: Hauser, Tobias U; Skvortsova, Vasilisa; De Choudhury, Munmun; Koutsouleris, Nikolaos
• Is Part Of: Lancet. Volume 4:Issue 11(2022)
• Page Start: e816
• Page End: e828
• Publication Date: 2022-11
• Subjects: Medical care -- Data processing -- Periodicals; Medical care -- Information technology -- Periodicals; Medical informatics -- Periodicals; 610.285
• DOI: 10.1016/S2589-7500(22)00152-2
• Languages: English
• ISSNs: 2589-7500

Abstract

Summary: Computational models have great potential to revolutionise psychiatry research and clinical practice. These models are now used across multiple subfields, including computational psychiatry and precision psychiatry. Their goals vary from understanding mechanisms underlying disorders to deriving reliable classification and personalised predictions. Rapid growth of new tools and data sources (eg, digital data, gamification, and social media) requires an understanding of the constraints and advantages of different modelling approaches in psychiatry. In this Series paper, we take a critical look at the range of computational models that are used in psychiatry and evaluate their advantages and disadvantages for different purposes and data sources. We describe mechanism-driven and mechanism-agnostic computational models and discuss how interpretability of models is crucial for clinical translation. Based on these evaluations, we provide recommendations on how to build computational models that are clinically useful.