Coverage-Aware Reasoning with Medical Tokens for Diagnosis Prediction

Key points

• In next-visit diagnosis prediction, multiple diagnoses can be simultaneously valid, but independently rewarding one diagnosis per trajectory does not distinguish repeated hits from coverage of different diagnoses.1
• To address both challenges, we propose CARing, a framework that represents diagnoses with compositional Semantic IDs (SIDs) and optimizes reasoning trajectories for multi-label coverage.1
• Concretely, we first encode ontology-enriched disease semantics into compact SIDs through residual quantization, and ground the resulting SID tokens in natural language and longitudinal EHR contexts through multi-task alignment and reasoning-enriched training to unlock transferable LLM reasoning.1
• CARing further improves unordered multi-label prediction through a coverage reward for reinforcement learning and multi-positive supervision.1

Sources (1)

• [1]Coverage-Aware Reasoning with Medical Tokens for Diagnosis Prediction
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 02:39 PM

  Large language models (LLMs) offer promising potential for next-visit diagnosis prediction, owing to their ability to integrate longitudinal clinical evidence and reason over it in natural language.

  In next-visit diagnosis prediction, multiple diagnoses can be simultaneously valid, but independently rewarding one diagnosis per trajectory does not distinguish repeated hits from coverage of different diagnoses.

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