MOSAIC: Mixture-Of-Specialty Adaptation for Individualized Clinical-prediction

Electronic health record (EHR) foundation models share one set of parameters across all patients: a patient's history enters only through the input context, and adapting these models to a clinical setting requires updating millions of parameters. Yet clinical personalization has natural structure: specialties reason differently about the same findings, and patients within a specialty follow different trajectories. We present MOSAIC (Mixture-Of-Specialty Adaptation for Individualized Clinical-prediction), which captures both levels through composition over a frozen LLM rather than per-patient parameter updates. At the cohort level, evolutionary prompt optimization discovers specialty-specific clinical reasoning rules in natural language. At the patient level, micro-adapters are composed through trajectory-conditioned routing, yielding adapter weights that reflect each patient's disease history. On a frozen Qwen2.5-7B backbone with only 78K trainable parameters, MOSAIC exceeds 18 baselines on MIMIC-IV next-visit diagnosis prediction at four levels of diagnostic granularity and outperforms standard LoRA at a matched parameter budget. Cohort reasoning contributes mainly at coarse granularity and patient calibration mainly at fine granularity. The approach transfers to eICU, a multi-center critical-care database. MOSAIC also predicts across a broad diagnostic vocabulary and improves monotonically with patient history length, where deep EHR baselines plateau.