Complementary Evidence Reasoning over Structured and Unstructured Electronic Health Records

Structured and unstructured features derived from the same record often overlap, so learning from both requires distinguishing complementary evidence from redundant information. In Electronic Health Records (EHRs), clinical notes capture key observations that are not readily standardized into medical codes, such as symptom severity and treatment response, while much of their remaining content is already in the structured record. Existing EHR methods encode or summarize notes before fusing them with the structured record, without asking what the note adds beyond the codes. Reasoning-enhanced methods reason to represent an input on its own terms, without feedback from the downstream task. In both cases, reasoning is not trained to select complementary evidence for predictive utility. To this end, we propose CERE (Complementary Evidence Reasoning for EHRs), which uses LLM reasoning to learn task-aligned representations of clinical notes that complement structured records. CERE first maps visit codes to soft prompts that tell the LLM what the structured record already captures. Guided by these prompts, the LLM reasons about what the note adds and encodes that reasoning as a dense representation trained for prediction. Finally, reinforcement learning refines the reasoning itself, rewarding evidence selections that lower prediction loss. Experiments on MIMIC-III and MIMIC-IV show that CERE consistently outperforms strong baselines across multiple clinical prediction tasks.