DMAP: A Deep-Model Driven Multi-Agent Framework for Reliable Diagnosis Prediction

Abstract

Deep learning based diagnosis prediction from Electronic Health Record (EHR) data has demonstrated strong performance but remains hindered by limited interpretability, reducing its clinical applicability. Existing multi-agent frameworks attempt to address this challenge; however, they often rely on superficial, semantics-only predictions from a large language model (LLM) or adopt multi-classification heads, thereby inheriting the opacity of conventional deep learning approaches. We introduce DMAP, a multi-agent framework designed to enhance EHR modeling through structured collaboration and interpretable decision-making. Inspired by multidisciplinary clinical workflows, DMAP employs three types of agents: DeepL Agents, a Leader Agent, and a Critical Agent, which work together to analyze patient data and generate reliable reports. DeepL Agents process structured EHR inputs, model outputs, and interpretability signals to provide initial clinical evaluations. Leader Agent coordinates these insights through iterative discussions, synthesizing consensus-driven disease predictions and draft report. Critical Agent evaluates the medical soundness of the draft report, identifying gaps or inconsistencies. When uncertainty arises, a retrieval-augmented generation (RAG) module integrates medical knowledge to support final decisions. By simulating expert collaboration and integrating structured reasoning with external knowledge, DMAP delivers reliable, interpretable predictions and reports. Extensive experiments on two EHR datasets demonstrate its superior performance in disease prediction and report generation, highlighting its potential to advance clinical decision support through explainable and adaptive multi-agent collaboration.

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