幸存者导航器:使用大型语言模型生成个性化幸存者护理计划
Jathurshan Pradeepkumar1, Shivam Pankaj Kumar1, Courtney Bryce Reamer2
1University of Illinois Urbana-Champaign, Urbana, IL.
AMIA ... Annual Symposium proceedings. AMIA Symposium
|February 23, 2026
概括
大型语言模型 (LLM) 可以自动生成癌症幸存者护理计划 (SCP),减少临床医生的负担. 幸存者导航器在创建这些必不可少的后续工具时表现出更好的准确性和准则合规性.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 癌症幸存者护理计划 (SCP) 对于长期的患者随访至关重要.
- 目前的SCP创建是耗时的,给临床医生带来了巨大的负担.
- 手动数据提取和指导方针的应用使这个过程变得复杂.
研究的目的:
- 探索大型语言模型 (LLM) 对于自动化SCP生成的潜力.
- 介绍生存导航器,这是一个简化SCP创建的框架.
- 加强SCP与临床系统的整合.
主要方法:
- 系统地探索用于自动生成SCP的LLM.
- 开发和实施"幸存者导航器"框架.
- 使用自动化评估和人类专家审查进行评估.
主要成果:
- 幸存者导航在SCP生成中显著超过了基线方法.
- 该框架产生了更准确和符合指南的SCP.
- 生成的SCP对临床医生和幸存者来说更容易采取行动.
结论:
- LLM为自动化SCP创建提供了一个有前途的解决方案.
- 幸存者导航器有效地减少了临床医生的负担,并提高了SCP质量.
- 自动化的SCP生成可以增强癌症幸存者护理服务.
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