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BRIEF RESEARCH REPORT article

Front. Psychiatry
Sec. Schizophrenia
Volume 15 - 2024 | doi: 10.3389/fpsyt.2024.1520173
This article is part of the Research Topic Machine Learning Algorithms and Software Tools for Early Detection and Prognosis of Schizophrenia View all articles

Predicting Conversion to Psychosis Using Machine Learning: Response to Cannon

Provisionally accepted
Jason Smucny Jason Smucny 1*Tyrone Cannon Tyrone Cannon 2Carrie E Bearden Carrie E Bearden 3Jean Addington Jean Addington 4Kristin Suzanne Cadenhead Kristin Suzanne Cadenhead 5Barbara Cornblatt Barbara Cornblatt 6Matcheri S Keshavan Matcheri S Keshavan 7Daniel H Mathalon Daniel H Mathalon 8Diana Perkins Diana Perkins 9William Stone William Stone 7Elaine F Walker Elaine F Walker 10Scott Woods Scott Woods 2Ian N Davidson Ian N Davidson 1Cameron S Carter Cameron S Carter 11
  • 1 University of California, Davis, Davis, United States
  • 2 Yale University, New Haven, Connecticut, United States
  • 3 University of California, Los Angeles, Los Angeles, California, United States
  • 4 University of Calgary, Calgary, Alberta, Canada
  • 5 University of California, San Diego, La Jolla, California, United States
  • 6 Northwell Health, New York, New York, United States
  • 7 Harvard University, Cambridge, Massachusetts, United States
  • 8 University of California, San Francisco, San Francisco, California, United States
  • 9 University of North Carolina Hospitals, Chapel Hill, North Carolina, United States
  • 10 Emory University, Atlanta, Georgia, United States
  • 11 University of California, Irvine, Irvine, California, United States

The final, formatted version of the article will be published soon.

    Background: We previously reported that machine learning could be used to predict conversion to psychosis in individuals at clinical high risk (CHR) for psychosis with up to 90% accuracy using the North American Prodrome Longitudinal Study-3 (NAPLS-3) dataset. A definitive test of our predictive model that was trained on the NAPLS-3 data, however, requires further support through implementation in an independent dataset. In this report we tested for model generalization using the previous iteration of NAPLS-3, the NAPLS-2, using the identical machine learning algorithms employed in our previous study. Method: Standard machine learning algorithms were trained to predict conversion to psychosis in clinical high risk individuals on the NAPLS-3 dataset and tested on the NAPLS-2 dataset. Results: NAPLS-2 and -3 individuals significantly differed on most features used in machine learning models. All models performed above chance, with Naive Bayes and random forest methods showing the best overall performance. Importantly, however, overall performance did not match those previously observed when using only NAPLS-3 data. Conclusion: The results of this study suggest that a machine learning model trained to predict conversion to psychosis on one dataset can be used to train an independent dataset. Performance on the test set was not in the range necessary for clinical application, however. Possible reasons that limited performance are discussed

    Keywords: Schizophrenia, clinical high risk (CHR), NAPLs, Out of sample evaluation, Scale of Psychosis Risk Symptoms, Generalizability

    Received: 30 Oct 2024; Accepted: 18 Dec 2024.

    Copyright: © 2024 Smucny, Cannon, Bearden, Addington, Cadenhead, Cornblatt, Keshavan, Mathalon, Perkins, Stone, Walker, Woods, Davidson and Carter. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Jason Smucny, University of California, Davis, Davis, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.