用大模型+逻辑验证实现精准临床试验匹配，提升准确率30%。
Neurosymbolic Clinical Trial Matching via LLM-Driven Abduction and Logical Verification
- 结合大模型推理与逻辑验证，处理模糊病历信息。
- 相比零样本基线，准确率提升最高达30%。
- 适合医疗自动化、临床研究系统开发者参考。
大型语言模型（LLMs）为自动化临床试验匹配（CTM）提供了新路径，但在复杂入组标准的确定性验证上仍存在不足。纯符号方法虽具形式严谨性，却难以应对不完整患者记录和噪声临床证据。为此，本文提出一种融合式神经符号框架αNeSy-CTM，利用大模型的语言与世界知识，支持对噪声和不明确临床文本的推理。大量实验证明，αNeSy-CTM显著优于独立的LLM基线，相较零样本基线相对提升最高达30%。分析进一步验证了归因推理在CTM中的作用，αNeSy-CTM在准确性、特异性与鲁棒性上均优于非归因设置。此外，αNeSy-CTM与思维链（CoT）推理高度互补，凸显混合路由策略潜力。本研究展示了神经符号方法在自动化临床试验匹配中的价值，为下一代可审计、基于大模型的临床应用提供路径。
原文摘要 · Abstract (English)
Large Language Models (LLMs) offer a promising path to automate Clinical Trial Matching (CTM), but still struggle with the deterministic verification required for complex eligibility criteria. Conversely, purely symbolic methods provide formal rigour but break down when faced with incomplete patient records and noisy clinical evidence. To bridge this gap, we investigate a hybrid framework for CTM combining LLMs with logical verification. In particular, we introduce an abductive neurosymbolic CTM framework (αNeSy-CTM), which leverages the linguistic and world knowledge in LLMs to support reasoning over noisy and underspecified clinical text. Extensive evaluation demonstrates that αNeSy-CTM substantially outperforms standalone LLM baselines, achieving up to 30% relative improvement over zero-shot baselines. In addition, our analyses confirm the impact of abductive reasoning on CTM, with αNeSy-CTM exhibiting improved accuracy, specificity, and robustness over a non-abductive neurosymbolic setting. Furthermore, αNeSy-CTM and Chain-of-Thought (CoT) reasoning prove highly complementary, highlighting the potential for a hybrid routing policy. Ultimately, this paper demonstrates the impact of neurosymbolic methods for automating CTM, providing a path toward the next generation of auditable, LLM-driven clinical applications.
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