A Lightweight Multimodal Transformer for Predicting Imminent Mood State Transitions in Synthetic Bipolar Trajectories

Anticipating short-term affective instability in bipolar disorder represents a longstanding challenge in computational psychiatry. Early signalling of transitions from stable to depressive, manic, or mixed states could support just-in-time interventions, yet real-world digital phenotyping datasets rarely provide dense temporal sampling, precise transition labels, or sufficient event frequency for training predictive models. To address these methodological barriers, we construct a clinically principled synthetic dataset of multimodal behavioural signals for 50 virtual subjects monitored over 60 days. Six behavioural modalities, physical activity, sleep duration, social interaction, voice features, keystroke dynamics, and screen time are generated 12 times daily using state-specific Gaussian processes modulated by circadian dynamics. A transition-aware labelling function marks the preceding 12 samples of each state change as switch imminent. A sliding-window procedure yields 11,800 sequences consisting of 12 consecutive time steps across 6 behavioural modalities (12 time points × 6 features), creating a highly imbalanced binary classification task (≈8% positive sequences). We develop a lightweight transformer encoder (199,619 parameters) optimized for short sequence modelling and computational efficiency. The model achieves 55.38% accuracy, 0.622 ROC-AUC, 0.597 sensitivity, and 0.174 F1-score on the stratified held-out set. Although performance remains modest due to the synthetic environment’s noise and imbalance, the model successfully identifies temporal micro-patterns preceding transitions patterns not detectable from static features. Behavioural distributions, intramodality correlations, sequence-level label frequencies, training dynamics, confusion structure, and ROC analysis collectively contextualize the model’s discriminative capacity. This synthetic benchmark provides a reproducible, mathematically grounded testbed for developing early-warning algorithms for bipolar instability. Our findings demonstrate that compact transformers can operate effectively under heavy noise and imbalance, offering a viable foundation for future real-world digital phenotyping systems.