Prediction of early‐onset bipolar using electronic health records
Good discriminative performance for models predicting early-onset BD can be achieved utilizing large-scale EHR data. Our study offers a scalable and accurate method for identifying youth at risk for BD that could help inform clinical decision-making and facilitate early intervention. Future work includes evaluating the portability of our approach to other healthcare systems and exploring considerations regarding possible implementation.
Authors
- Hyunjoon Lee (ORCID: https://orcid.org/0000-0003-4694-7016)
- Yi-han Sheu (ORCID: https://orcid.org/0000-0002-2767-0851)
- Jordan W. Smoller (ORCID: https://orcid.org/0000-0002-0381-6334)
- Robert G. Mealer (ORCID: https://orcid.org/0000-0001-5963-2836)
- Víctor M. Castro (ORCID: https://orcid.org/0000-0001-7390-6354)
- Bo Wang (ORCID: https://orcid.org/0000-0002-7046-2759)
Institutions
- Broad Institute (US)
- Political Research Associates (US)
- Harvard University (US)
- Oregon Health & Science University (US)
- Massachusetts General Hospital (US)
- Mass General Brigham (US)
- Vanderbilt University Medical Center (US)
Publication Details
- Journal
- Journal of Child Psychology and Psychiatry
- Published
- 2025-02-18
- DOI
- https://doi.org/10.1111/jcpp.14131
- Citations
- 2
- Primary Topic
- Bipolar Disorder and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 2.94
Funders
- Biogen
- Ryan Licht Sang Bipolar Foundation
- National Institute of Mental Health