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Studying Healthy Psychosislike Experiences to Improve Illness Prediction

To identify the key insights or developments described in this article
1 Credit CME
Abstract

Importance  Distinguishing delusions and hallucinations from unusual beliefs and experiences has proven challenging.

Observations  The advent of neural network and generative modeling approaches to big data offers a challenge and an opportunity; healthy individuals with unusual beliefs and experiences who are not ill may raise false alarms and serve as adversarial examples to such networks.

Conclusions and Relevance  Explicitly training predictive models with adversarial examples should provide clearer focus on the features most relevant to casehood, which will empower clinical research and ultimately diagnosis and treatment.

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Article Information

Accepted for Publication: January 9, 2023.

Published Online: March 8, 2023. doi:10.1001/jamapsychiatry.2023.0059

Corresponding Author: Philip R. Corlett, PhD, Department of Psychiatry, Yale University School of Medicine, 34 Park St, New Haven, CT 06519 (philip.corlett@yale.edu).

Conflict of Interest Disclosures: Dr Corlett is cofounder and equity holder of Tetricus Labs, a startup incorporated in 2022 that attempts to diagnose and treat mental illnesses using behavioral testing and artificial neural networks. Dr Gold reported personal fees from Vera Sci outside the submitted work. No other disclosures were reported.

Funding/Support: Drs Corlett, Bansal, and Gold were supported by National Institute of Mental Health grants R01MH112887 (all), R01 MH120090 (Dr Gold), and R01 MH120089 (Dr Corlett). Dr Corlett was also supported in part by the State of Connecticut, Department of Mental Health and Addiction Services.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: This publication does not express the views of the Department of Mental Health and Addiction Services or the State of Connecticut. The views and opinions expressed are those of the authors.

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