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KT-LLM:一个基于证据和序列的文本框架,用于可审计的脏移植建模
Haofeng Zheng1,2, Zihuan Luo1,2, Kaiming He1
1Department of Renal Transplantation, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
NPJ digital medicine
|January 10, 2026
概括
KT-LLM结合了临床数据和移植 (KT) 政策. 这种可审计的系统改善了决策,规范了护理,并在现实环境中提高了可复制性.
科学领域:
- 科和生物医学信息学
- 医疗保健中的人工智能
- 临床数据科学 临床数据科学
背景情况:
- 在现实世界中,移植 (KT) 面临着跟进和政策遵守不一致的挑战.
- 结构化数据与临床规则之间存在脱节,影响了报告和可重复性.
- 现有系统缺乏基于权威临床指南的可审计推理.
研究的目的:
- 引入KT-LLM,用于移植工作流程的可审计编排层.
- 为改善临床决策,将序列建模与政策意识的推理联系起来.
- 确保临床决策是基于版本,权威的来源,如班夫病理学和OPTN/SRTR政策.
主要方法:
- 开发了KT-LLM,一种使用版本化政策文件的检索增强生成系统.
- 整合了三个可审计的代理:SRTR-MambaSurv用于生存预测,OPTN-BlackClust用于亚型识别,以及Policy-Ops用于合规性检查.
- 使用了非识别的OPTN和UNOS队列进行验证.
主要成果:
- 与基线相比,KT-LLM展示了优越的证据归因和预测校准.
- 该系统成功地在黑人接受者中确定了不同的子组,与结果异质性保持一致.
- KT-LLM提供可审计的,版本化的输出,以提高合规性和减少主观错误.
结论:
- KT-LLM为可审计,时钟同步的移植工作流提供了一个实用的解决方案.
- 该系统增强了可复制性,公平性监测和临床实践的标准化.
- KT-LLM解决了对可扩展和可靠的移植护理的未满足需求.
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