Hostname: page-component-78c5997874-dh8gc Total loading time: 0 Render date: 2024-11-17T19:28:06.695Z Has data issue: false hasContentIssue false
  • English
  • Français

Commentary: the ethical challenges of machine learning in psychiatry: a focus on data, diagnosis, and treatment

Published online by Cambridge University Press:  12 May 2021

Daniel S. Barron Open the ORCID record for Daniel S. Barron [Opens in a new window]
Daniel S. Barron*
Affiliation:
Department of Psychiatry, Yale University, New Haven, CT, USA Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA, USA Department of Psychiatry, Brigham & Women's Hospital, Harvard University, Boston, MA, USA Department of Anesthesiology & Pain Medicine, Brigham & Women's Hospital, Harvard University, Boston, MA, USA
*
Author for correspondence: Daniel S. Barron, Email: daniel.s.barron@yale.edu
Get access Rights & Permissions [Opens in a new window]

Extract

The clinical interview is the psychiatrist's data gathering procedure. However, the clinical interview is not a defined entity in the way that ‘vitals’ are defined as measurements of blood pressure, heart rate, respiration rate, temperature, and oxygen saturation. There are as many ways to approach a clinical interview as there are psychiatrists; and trainees can learn as many ways of performing and formulating the clinical interview as there are instructors (Nestler, 1990). Even in the same clinical setting, two clinicians might interview the same patient and conduct very different examinations and reach different treatment recommendations. From the perspective of data science, this mismatch is not one of personal style or idiosyncrasy but rather one of uncertain salience: neither the clinical interview nor the data thereby generated is operationalized and, therefore, neither can be rigorously evaluated, tested, or optimized.

Keywords

Schizophreniadiagnosismachine learning
Type
Invited Commentary
Information
Psychological Medicine , Volume 51 , Issue 15 , November 2021 , pp. 2522 - 2524
Check for updates
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press.

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Barron, D. (2021). Reading Our minds: The rise of Big data psychiatry. New York City: Columbia University Press, Columbia Global Reports.Google Scholar
Bzdok, D., & Ioannidis, J. P. A. (2019). Exploration, inference, and prediction in neuroscience and biomedicine. Trends in Neurosciences, 42, 251–262.CrossRefGoogle ScholarPubMed
Chekroud, A. M., Gueorguieva, R., Krumholz, H. M., Trivedi, M. H., Krystal, J. H., & McCarthy, G. (2017). Reevaluating the efficacy and predictability of antidepressant treatments: A symptom clustering approach. JAMA Psychiatry, 74, 370.CrossRefGoogle ScholarPubMed
First, M. B., Williams, J., & Karg, R. (2016) Structured Clinical Interview for DSM-5 Disorders (SCID-5), Clinician Version (SCID-5-CV).Google Scholar
Just, M. A., Pan, L., Cherkassky, V. L., McMakin, D. L., Cha, C., Nock, M. K., & Brent, D. (2017). Machine learning of neural representations of suicide and emotion concepts identifies suicidal youth. Nature Human Behaviour, 1, 911–919.CrossRefGoogle ScholarPubMed
Lieberman, J. A., & Ogas, O. (2015). Shrinks: The untold story of psychiatry. New York, NY: Back Bay Books.Google Scholar
Nestler, E. J. (1990). The case of double supervision: A resident's perspective on common problems in psychotherapy supervision. Academic Psychiatry, 14, 129–136.CrossRefGoogle ScholarPubMed
Starke, G., Clercq, E. D., Borgwardt, S., & Elger, B. S. (2020). Computing schizophrenia: Ethical challenges for machine learning in psychiatry. Psychological Medicine, 1–7. https://doi.org/10.1017/S0033291720001683.CrossRefGoogle Scholar
Waszczuk, M. A., Zimmerman, M., Ruggero, C., Li, K., MacNamara, A., Weinberg, A., … Kotov, R. (2017). What do clinicians treat: Diagnoses or symptoms? The incremental validity of a symptom-based, dimensional characterization of emotional disorders in predicting medication prescription patterns. Comprehensive Psychiatry, 79, 80–88.CrossRefGoogle ScholarPubMed
Young, G., Lareau, C., & Pierre, B. (2014). One quintillion ways to have PTSD comorbidity: Recommendations for the disordered DSM-5. Psychological Injury and Law, 7, 61–74.CrossRefGoogle Scholar
Zimmerman, M., Morgan, T. A., & Stanton, K. (2018). The severity of psychiatric disorders. World Psychiatry, 17, 258–275.CrossRefGoogle ScholarPubMed