A primer on the use of machine learning to distil knowledge from data in biological psychiatry.

Quinn, Thomas P; Hess, Jonathan L; Marshe, Victoria S; Barnett, Michelle M; Hauschild, Anne-Christin; Maciukiewicz, Malgorzata; Elsheikh, Samar S M; Men, Xiaoyu; Schwarz, Emanuel; Trakadis, Yannis J; Breen, Michael S; Barnett, Eric J; Zhang-James, Yanli; Ahsen, Mehmet Eren; Cao, Han; Chen, Junfang; Hou, Jiahui; Salekin, Asif; Lin, Ping-I; Nicodemus, Kristin K; ... (2024). A primer on the use of machine learning to distil knowledge from data in biological psychiatry. Molecular psychiatry, 29(2), pp. 387-401. Nature Publishing Group 10.1038/s41380-023-02334-2

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Applications of machine learning in the biomedical sciences are growing rapidly. This growth has been spurred by diverse cross-institutional and interdisciplinary collaborations, public availability of large datasets, an increase in the accessibility of analytic routines, and the availability of powerful computing resources. With this increased access and exposure to machine learning comes a responsibility for education and a deeper understanding of its bases and bounds, borne equally by data scientists seeking to ply their analytic wares in medical research and by biomedical scientists seeking to harness such methods to glean knowledge from data. This article provides an accessible and critical review of machine learning for a biomedically informed audience, as well as its applications in psychiatry. The review covers definitions and expositions of commonly used machine learning methods, and historical trends of their use in psychiatry. We also provide a set of standards, namely Guidelines for REporting Machine Learning Investigations in Neuropsychiatry (GREMLIN), for designing and reporting studies that use machine learning as a primary data-analysis approach. Lastly, we propose the establishment of the Machine Learning in Psychiatry (MLPsych) Consortium, enumerate its objectives, and identify areas of opportunity for future applications of machine learning in biological psychiatry. This review serves as a cautiously optimistic primer on machine learning for those on the precipice as they prepare to dive into the field, either as methodological practitioners or well-informed consumers.

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