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

Molecular Psychiatry (2024)Cite this article

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Abstract

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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Fig. 1: Schematic illustration of trends in ML development relevant to psychiatric research.
Fig. 2: Distribution of types of ML methods used across 1461 surveyed publications.
Fig. 3: Distribution of types of disorders and phenotypes represented across 1461 surveyed publications.
Fig. 4: Distribution of types of ML models with respect to data modality and psychiatric outcomes across 1461 surveyed publications.

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Acknowledgements

SJG is supported by grants from the U.S. National Institutes of Health (R01AG064955 and R21MH126494), the U.S. National Science Foundation, the Sidney R. Baer, Jr. Foundation, and NARSAD: The Brain & Behavior Research Foundation. GP is supported by a grant from the U.S. National Institutes of Health (R01HG011407) and an IBM Faculty Award. PL is supported a grant from the European Union’s Horizon 2020 research and innovation programme (634880), EDC-MixRisk, and Patient-Centered Outcome Research Institute (PCORI). JLH is supported by a grant from the U.S. National Institutes of Health (R21MH126494 and R01NS128535) and NARSAD: The Brain & Behavior Research Foundation. SVF is supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 965381; NIMH grants U01AR076092-01A1, 1R21MH1264940, R01MH116037; 1R01NS128535 – 01; Oregon Health and Science University, Otsuka Pharmaceuticals, Noven Pharmaceuticals Incorporated, and Supernus Pharmaceutical Company.

Author information

Author notes

  1. These authors contributed equally: Thomas P. Quinn, Jonathan L. Hess.

Authors and Affiliations

  1. Applied Artificial Intelligence Institute (A2I2), Burwood, VIC, 3125, Australia

    Thomas P. Quinn

  2. Department of Psychiatry and Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA

    Jonathan L. Hess, Yanli Zhang-James, Jiahui Hou, Stephen V. Faraone & Stephen J. Glatt

  3. Institute of Medical Science, University of Toronto, Toronto, ON, M5S 1A1, Canada

    Victoria S. Marshe

  4. Pharmacogenetics Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5S 1A1, Canada

    Victoria S. Marshe, Samar S. M. Elsheikh, Xiaoyu Men & Daniel J. Müller

  5. School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, 2308, Australia

    Michelle M. Barnett & Murray J. Cairns

  6. Precision Medicine Research Program, Hunter Medical Research Institute, Newcastle, NSW, 2308, Australia

    Michelle M. Barnett & Murray J. Cairns

  7. Department of Medical Informatics, Medical University Center Göttingen, Göttingen, Lower Saxony, 37075, Germany

    Anne-Christin Hauschild

  8. Hospital Zurich, University of Zurich, Zurich, 8091, Switzerland

    Malgorzata Maciukiewicz

  9. Department of Rheumatology and Immunology, University Hospital Bern, Bern, 3010, Switzerland

    Malgorzata Maciukiewicz

  10. Department for Biomedical Research (DBMR), University of Bern, Bern, 3010, Switzerland

    Malgorzata Maciukiewicz

  11. Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, M5S 1A1, Canada

    Xiaoyu Men

  12. Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Baden-Württemberg, J5 68159, Germany

    Emanuel Schwarz, Han Cao & Junfang Chen

  13. Department Human Genetics, McGill University Health Centre, Montreal, QC, H4A 3J1, Canada

    Yannis J. Trakadis

  14. Psychiatry, Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA

    Michael S. Breen

  15. Department of Neuroscience and Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA

    Eric J. Barnett, Jiahui Hou, Stephen V. Faraone & Stephen J. Glatt

  16. Department of Business Administration, Gies College of Business, University of Illinois at Urbana-Champaign, Champaign, IL, 61820, USA

    Mehmet Eren Ahsen

  17. Department of Biomedical and Translational Sciences, Carle-Illinois School of Medicine, University of Illinois at Urbana-Champaign, Champaign, IL, 61820, USA

    Mehmet Eren Ahsen

  18. Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY, 13244, USA

    Asif Salekin

  19. Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, NSW, 2052, Australia

    Ping-I Lin

  20. Mental Health Research Unit, South Western Sydney Local Health District, Liverpool, NSW, 2170, Australia

    Ping-I Lin

  21. Usher Institute, University of Edinburgh, Edinburgh, EH8 9YL, UK

    Kristin K. Nicodemus

  22. Clinical Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Baden-Württemberg, J5 68159, Germany

    Andreas Meyer-Lindenberg

  23. Biomedical and Health Informatics/Computer Science Department, State University of New York at Oswego, Oswego, NY, 13126, USA

    Isabelle Bichindaritz

  24. Intelligent Bio Systems Lab, State University of New York at Oswego, Oswego, NY, 13126, USA

    Isabelle Bichindaritz

  25. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA

    Gaurav Pandey

  26. Department of Psychiatry, University of Toronto, Toronto, ON, M5S 1A1, Canada

    Daniel J. Müller

  27. Department of Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University Hospital of Würzburg, Würzburg, 97080, Germany

    Daniel J. Müller

  28. Department of Public Health and Preventive Medicine, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA

    Stephen J. Glatt

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Consortia

on behalf of the Machine Learning in Psychiatry (MLPsych) Consortium

  • Thomas P. Quinn
  • , Jonathan L. Hess
  • , Victoria S. Marshe
  • , Michelle M. Barnett
  • , Anne-Christin Hauschild
  • , Malgorzata Maciukiewicz
  • , Samar S. M. Elsheikh
  • , Xiaoyu Men
  • , Emanuel Schwarz
  • , Yannis J. Trakadis
  • , Michael S. Breen
  • , Eric J. Barnett
  • , Yanli Zhang-James
  • , Mehmet Eren Ahsen
  • , Han Cao
  • , Junfang Chen
  • , Jiahui Hou
  • , Asif Salekin
  • , Ping-I Lin
  • , Kristin K. Nicodemus
  • , Andreas Meyer-Lindenberg
  • , Isabelle Bichindaritz
  • , Stephen V. Faraone
  • , Murray J. Cairns
  • , Gaurav Pandey
  • , Daniel J. Müller
  •  & Stephen J. Glatt

Contributions

TPQ, JLH, SVF, DJM, KKN, ES, YZJ, and SJG conceived of the study or a section of the study. MSB, VSM, XM, SSME, MM, ES, A-CH, YJT, and AH performed the systematic review and data extraction. TPQ, JLH, JC, GP, ES, AS, IB, P-IL, YZJ, MEA, DJM contributed a section to the initial draft of the manuscript. VSM, MMB, AH, MM, SSME, XM, ES, YJT, MSB, EJB, YZJ, MEA, HC, JH, AS, P-IL, KKN, AML, IB, SVF, MJC, GP, DJM, and SJG critically revised the manuscript. DJM and SJG supervised the project. All authors approved the final version of the manuscript for submission.

Corresponding author

Correspondence to Stephen J. Glatt.

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Competing interests

SVF in the past year, received income, potential income, travel expenses continuing education support and/or research support from Aardvark, Aardwolf, AIMH, Tris, Otsuka, Ironshore, Kanjo, Johnson & Johnson/Kenvue, KemPharm/Corium, Akili, Supernus, Atentiv, Noven, Sky Therapeutics, Axsome and Genomind. With his institution, he has US patent US20130217707 A1 for the use of sodium-hydrogen exchange inhibitors in the treatment of ADHD. He also receives royalties from books published by Guilford Press: Straight Talk about Your Child’s Mental Health, Oxford University Press: Schizophrenia: The Facts and Elsevier: ADHD: Non-Pharmacologic Interventions. He is Program Director of www.ADHDEvidence.org and www.ADHDinAdults.com.

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Quinn, T.P., Hess, J.L., Marshe, V.S. et al. A primer on the use of machine learning to distil knowledge from data in biological psychiatry. Mol Psychiatry (2024). https://doi.org/10.1038/s41380-023-02334-2

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