Simulation-Based Natural Language Processing of Synthetic Clinical Notes to Detect Early Linguistic Markers of Antidepressant-Induced Hypomania in Bipolar Disorder: A Proof-of-Concept Study

Antidepressant-induced hypomania in bipolar disorder (BD) is a clinically consequential pharmacological safety event that may be preceded by changes in patient and clinician language. This proof-of-concept simulation study evaluates whether natural language processing (NLP) can recover such signals from fully synthetic outpatient clinical notes and simulated hypomania labels; it does not establish clinical validity. We generated one synthetic note for each of N = 700 simulated antidepressant-exposed BD patients (hypomania prevalence 25.1%). A three-tier feature architecture encoded: i) 18 psycholinguistic and syntactic features; ii) 30 latent semantic analysis components derived from TF-IDF representations; and iii) 12 simulated clinical meta-features. A four-layer multilayer perceptron, termed ClinicalBERT-sim to distinguish it from an actually fine-tuned ClinicalBERT model, was trained on the combined feature matrix. An out-of-fold stacking ensemble fused ClinicalBERT-sim, LightGBM, and Random Forest outputs. The proposed ensemble achieved AUC = 0.912 (95% CI: 0.842 - 0.969), F1 = 0.708, sensitivity = 0.595, and specificity = 0.974 on the held-out synthetic test set. SHAP analysis identified hypomanic term score, word count, energy word score, physician concern score, and positive affect ratio as the dominant simulated predictors. Longitudinal illustrative trajectories showed rising hypomanic term score and word count across simulated weekly notes before simulated onset. These findings demonstrate technical feasibility within the designed simulator, but performance may partly reflect the rules used to generate notes and labels. Validation on independently annotated, real outpatient BD notes is required before any clinical screening or workflow use.