Uncovering predictors of bipolar II conversion to bipolar I: A machine learning analysis of national health records in Taiwan.

BACKGROUND

Bipolar II disorder (BD-II) can progress to bipolar I disorder (BD-I), carrying profound psychosocial consequences. However, limited research has investigated the transition from BD-II to BD-I. Our study uses machine learning to predict the conversion using real-world data.

METHODS

We conducted a retrospective cohort study using the Taiwan National Health Insurance Research Database (2000-2013) to identify adults diagnosed with BD-II. A predictive model for BD-I conversion was developed using extreme gradient boosting (XGBoost), incorporating demographic factors, healthcare utilization, comorbidities, and psychotropic medication use. The dataset was randomly divided into development (80 %) and test (20 %) sets. Model performance was evaluated by accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Feature importance was interpreted using Shapley Additive Explanations (SHAP).

RESULTS

Among the 1890 individuals diagnosed with BD-II, 14 % subsequently converted to BD-I. The final machine learning model demonstrated strong predictive performance, achieving an accuracy of 86 %, sensitivity of 77 %, specificity of 87 %, and an area under the receiver operating characteristic curve (AUC) of 0.91 in the external validation cohort. Younger age, female sex, fewer outpatient visits before and more after diagnosis were key predictors. Lower incidence of new-onset medical (e.g., respiratory, gastrointestinal) and psychiatric (e.g., anxiety, insomnia) comorbidities also indicated higher risk. Increased use of lithium, anticonvulsants, second-generation antipsychotics, and hypnotics further characterized high-risk profiles.

CONCLUSION

Our findings highlight the utility of machine learning in stratifying BD-II patients at elevated risk for BD-I conversion, offering a data-driven framework to support early clinical intervention and personalized care.