使用大型语言模型自动判断心血管事件
Sonish Sivarajkumar1,2, Kimia Ameri1, Chuqin Li1
1Advanced Analytics and Data Sciences, Eli Lilly and Company, USA.
AMIA ... Annual Symposium proceedings. AMIA Symposium
|February 23, 2026
概括
在使用大型语言模型 (LLM) 的临床试验中自动化心血管死亡判断显著加快了过程,并减少了变化. 这种AI框架提高了事件分类的准确性和透明度.
科学领域:
- 人工智能的人工智能
- 临床试验 临床试验
- 医疗信息学 医疗信息学
背景情况:
- 在临床试验中判断心血管事件至关重要,但传统上是手动的,导致延迟,不一致性和高成本.
- 手动审查临床文件以判断事件是耗时的,容易出现人为错误.
研究的目的:
- 开发和评估使用大型语言模型 (LLM) 的两阶段框架,以自动化在临床试验中判断心血管死亡.
- 提高心血管事件裁决的效率,一致性和透明度.
主要方法:
- 一个采用大型语言模型 (LLM) 进行自动裁决的两阶段框架.
- 第一个阶段:简单的LLM从非结构化的临床文件中提取结构化的证据 (事件,否定,日期,跨度).
- 第二阶段:一个思维树的判断者使用临床终点委员会 (CEC) 的指导方针进行分类和推理生成.
主要成果:
- 基于LLM的框架在从临床文档中提取结构化证据时实现了高精度 (0.96) 和F1得分 (0.82).
- 判定阶段使用GPT-4思维树证明了0.68的准确性,超过了基线总结者加判定者的方法.
- CLEART评分 (0.67) 量化了理由质量,确定了时间推理和相关性作为需要改进的领域.
结论:
- 使用LLMs对心血管死亡的自动判断提供了一个有希望的解决方案,以提高临床试验的效率和减少临床试验的变化.
- 拟议的框架提供了一个可审计的理由,提高了裁决过程的透明度.
- 需要进一步改进,以优化自动化裁决系统中的时间推理和相关性.
相关概念视频
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01:27Regulation of Stroke Volume
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Preload refers to the degree of stretch on the heart before it contracts. It's analogous to the stretching of a rubber band; the more it's stretched, the more forcefully it snaps back. This concept is encapsulated in the Frank-Starling law of the...
01:18Neural Regulation of Blood Pressure
The neural regulation of blood pressure involves intricate interactions between the autonomic nervous system (ANS) and cardiovascular system, ensuring adequate perfusion of tissues. This regulation primarily occurs through baroreceptor and chemoreceptor reflexes, involving both short-term and long-term mechanisms.
Baroreceptor Reflex
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Baroreceptor Reflex
Baroreceptors, located in the carotid sinuses and aortic arch, detect changes in blood pressure. When blood pressure rises, these stretch-sensitive receptors...
01:17Autoregulation of Blood Flow
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Chemical Signaling in Autoregulation
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Chemical Signaling in Autoregulation
Chemical signaling operates at the precapillary sphincter level, inciting either contraction or relaxation.