Prediction of early‐onset bipolar using electronic health records
Bo Wanga b c d e f g h, Yi-han Sheua b c d e f g h, Hyunjoon Leei j, Robert G. Mealerk l m, Victor Manuel Castron o, Jordan W. Smollera b c d e f g h✉
- aCenter for Precision Psychiatry Massachusetts General Hospital Boston MA USA
- bDepartment of Psychiatry Harvard Medical School Boston MA USA
- cPsychiatric and Neurodevelopmental Genetics Unit Massachusetts General Hospital Boston MA USA
- dStanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge MA USA
- e
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Abstract
BACKGROUND: Early identification of bipolar disorder (BD) provides an important opportunity for timely intervention. In this study, we aimed to develop machine learning models using large-scale electronic health record (EHR) data including clinical notes for predicting early-onset BD. METHODS: Structured and unstructured data were extracted from the longitudinal EHR of the Mass General Brigham health system. We defined three cohorts aged 10-25 years: (1) the full youth cohort (N = 300,398); (2) a subcohort defined by having a mental health visit (N = 105,461); and (3) a subcohort defined by having a diagnosis of mood disorder or ADHD (N = 35,213). By adopting a prospective landmark modeling approach that aligns with clinical practice, we developed and validated a range of machine learning models, across different cohorts and prediction windows. RESULTS: We found the two tree-based models, random forests (RF) and light gradient-boosting machine (LGBM), achieving good discriminative performance across different clinical settings (area under the receiver operating characteristic curve 0.76-0.88 for RF and 0.74-0.89 for LGBM). In addition, we showed comparable performance can be achieved with a greatly reduced set of features, demonstrating computational efficiency can be attained without significant compromise of model accuracy. CONCLUSIONS: 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.