Recommendations and future directions for supervised machine learning in psychiatry

• Published in: Translational psychiatry Vol. 9; no. 1; pp. 271 - 12
• Main Authors: Cearns, Micah, Hahn, Tim, Baune, Bernhard T.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 22.10.2019; Nature Publishing Group
• Subjects: 692/53/2423; 706/648; Artificial intelligence; Behavioral Sciences; Biological Psychology; Humans; Machine learning; Medicine; Medicine & Public Health; Mental Disorders - diagnosis; Mental Disorders - therapy; Neurosciences; Pharmacotherapy; Practice Guidelines as Topic; Psychiatry; Psychiatry - methods; Review; Review Article; Supervised Machine Learning - standards; Supervised Machine Learning - trends
• ISSN: 2158-3188; 2158-3188
• DOI: 10.1038/s41398-019-0607-2

Machine learning methods hold promise for personalized care in psychiatry, demonstrating the potential to tailor treatment decisions and stratify patients into clinically meaningful taxonomies. Subsequently, publication counts applying machine learning methods have risen, with different data modalities, mathematically distinct models, and samples of varying size being used to train and test models with the promise of clinical translation. Consequently, and in part due to the preliminary nature of such works, many studies have reported largely varying degrees of accuracy, raising concerns over systematic overestimation and methodological inconsistencies. Furthermore, a lack of procedural evaluation guidelines for non-expert medical professionals and funding bodies leaves many in the field with no means to systematically evaluate the claims, maturity, and clinical readiness of a project. Given the potential of machine learning methods to transform patient care, albeit, contingent on the rigor of employed methods and their dissemination, we deem it necessary to provide a review of current methods, recommendations, and future directions for applied machine learning in psychiatry. In this review we will cover issues of best practice for model training and evaluation, sources of systematic error and overestimation, model explainability vs. trust, the clinical implementation of AI systems, and finally, future directions for our field.