GraphDx: A Cost-Aware Knowledge-Enhanced Multi-Agent Framework for Sequential Diagnosis
GraphDx is a knowledge-enhanced multi-agent framework that balances diagnostic accuracy and resource costs in sequential diagnosis. It constructs Medical Diagnosis Knowledge Graphs (MDKGs) via an automated LLM pipeline and employs three collaborative agents (Perception, Reasoning, Decision) for cost-aware planning. Experiments on MedQA and MIMIC-IV show diagnostic success rates improved from 50-68% to 79-93% and test costs reduced by 20-54%.
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[Submitted on 8 Apr 2026]
Title:GraphDx: A Cost-Aware Knowledge-Enhanced Multi-Agent Framework for Sequential Diagnosis
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Abstract:Sequential diagnosis requires balancing diagnostic accuracy against resource costs through iterative information gathering. Existing Large Language Model (LLM) approaches exhibit a critical knowledge-reasoning gap: despite encoding extensive medical knowledge, they struggle to reason systematically under cost constraints, often resorting to excessive testing. We propose GraphDx, a knowledge-enhanced framework with two core innovations. First, we design an automated pipeline that leverages LLMs to construct Medical Diagnosis Knowledge Graphs (MDKGs) with quantized typicality, action-centric topology, and dual-objective attributes for both diagnostic relevance and cost-sensitivity. Second, we introduce three collaborative agents (Perception, Reasoning, and Decision) where the Perception and Decision Agents handle language understanding and generation, while the Reasoning Agent performs deterministic evidence scoring and cost-aware planning on the MDKG. Experiments on MedQA and MIMIC-IV across three LLM backbones (DeepSeek-V3, Kimi-k2, Llama-3.3) show that GraphDx improves diagnostic success rates from 50--68% to 79--93% while reducing test costs by 20--54%, providing a robust, economical, and interpretable solution for automated clinical diagnosis.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.15280 [cs.AI]
(or arXiv:2607.15280v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.15280
arXiv-issued DOI via DataCite
Submission history
From: Shaoting Tan [view email] [v1] Wed, 8 Apr 2026 05:47:40 UTC (2,282 KB)
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