SAGEAgent：用于多模态生存预测中成本感知模态获取的自进化智能体
SAGEAgent: A Self-Evolving Agent for Cost-Aware Modality Acquisition in Multimodal Survival Prediction
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中文总结 AI 辅助
研究多模态生存预测中如何平衡预测准确性与临床侵入性，提出基于自进化大语言模型的SAGEAgent，通过多种记忆和工具为患者决定诊断方式，实验证明其能在胶质瘤队列中降低获取负担并保持竞争力。
中文摘要 AI 辅助
在多模态临床肿瘤学中，诊断方式遵循临床规定的负担递增顺序。当前多模态生存方法存在不足，本文将其视为序贯决策问题，提出SAGEAgent。它是基于自进化大语言模型的临床智能体，通过临床工具、情景记忆和语义记忆为患者决定获取的诊断方式，平衡预测准确性和临床侵入性。实验表明，SAGEAgent在胶质瘤队列中预测准确率具竞争力，同时将平均获取负担降低55%。
英文摘要
Does every cancer patient truly need a complete diagnostic workup for accurate survival prediction? In multimodal clinical oncology, diagnostic modalities follow a clinically mandated order of escalating burden -- from demographics collected at intake to genomic profiling requiring specialized tissue analysis. Current multimodal survival methods either assume all modalities are available or passively handle missing data, but none actively reason about whether acquiring the next modality is justified for a given patient along this ordered workflow. We formulate this as a sequential decision problem and propose SAGEAgent (Sequential Acquisition Guided by Experience), a self-evolving LLM-based clinical agent that decides which diagnostic modalities to acquire for each patient, balancing predictive accuracy against clinical invasiveness. SAGEAgent reasons about each patient's evolving diagnostic state through clinical tools that translate numerical predictions into text, an episodic memory that retrieves similar past cases, and a semantic memory that accumulates reusable decision patterns from experience. Experiments on a glioma cohort combining TCGA-LGG, TCGA-GBM, and BraTS with four diagnostic modalities demonstrate that SAGEAgent achieves competitive survival prediction accuracy while reducing average acquisition burden by 55%.
发表机构
- Vanderbilt University（范德堡大学）
- Vanderbilt University Medical Center（范德堡大学医学中心）
机构由 AI 辅助整理，请以论文原文为准。
