English(EN) OncoReason: Structuring Clinical Reasoning in LLMs for Robust and Interpretable Survival Prediction

通过推理框架增强大型语言模型在癌症生存预测方面的能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-02 04:00

研究人员开发了一个名为OncoReason的新框架，以提高大型语言模型（LLMs）在预测癌症治疗结果方面的可解释性和准确性。这种多任务学习方法训练LLMs执行生存分类、时间回归，并为其预测生成自然语言解释。使用LLaMa3-8B和Med42-8B模型的实验表明，思维链提示（Chain-of-Thought prompting）和组相对策略优化（Group Relative Policy Optimization）显著提高了预测性能和可解释性，为肿瘤学领域值得信赖的LLMs树立了新标杆。 AI

影响增强了大型语言模型在临床决策支持方面的可解释性和准确性，有望改善患者预后。

排序理由该集群包含一篇研究论文，详细介绍了大型语言模型在特定领域的新框架和方法论。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CL TIER_1 English(EN) · Raghu Vamshi Hemadri, Geetha Krishna Guruju, Kristi Topollai, Anna Ewa Choromanska · 2026-06-02 04:00

OncoReason：为 LLM 构建临床推理，实现稳健且可解释的生存预测

arXiv:2510.17532v2 Announce Type: replace Abstract: Predicting cancer treatment outcomes requires models that are both accurate and interpretable, particularly in the presence of heterogeneous clinical data. While large language models (LLMs) have shown strong performance in biom…

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OncoReason：为 LLM 构建临床推理，实现稳健且可解释的生存预测

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