大型语言模型可以识别MASH的存在,并从非结构化文档中提取VCTE测量结果
Aryana T Far1, Aryan Ayati2, Jordan Guillot2
1Division of Gastroenterology and Hepatology, Department of Medicine, University of California - San Francisco, San Francisco, CA, USA.
Digestive diseases and sciences
|November 8, 2025
概括
大型语言模型 (LLM) 准确地从临床笔记中提取代谢功能障碍相关的脂肪肝炎 (MASH) 和振动控制的短暂弹性图 (VCTE) 数据. 这些LLM提取的VCTE参数预测患者的结果,有助于肝病研究.
科学领域:
- 人工智能在医学中的应用
- 肝病学和胃肠病学 肝病学和胃肠学
- 在临床研究中的数据科学.
背景情况:
- 代谢功能障碍相关的脂肪肝炎 (MASH) 是肝硬化的主要驱动因素.
- 对于MASH评估至关重要的振动控制暂时弹性图 (VCTE) 数据通常被困在非结构化的临床笔记中.
- 大型语言模型 (LLM) 对于提取MASH和VCTE信息的实用性仍然在很大程度上未得到量化.
研究的目的:
- 评估GPT-4o和Claude 3.5 Sonnet在从临床文档中提取MASH和VCTE (硬度和CAP) 测量的准确性和效率.
- 评估使用LLM用于此数据提取任务的成本效益.
- 探索LLM提取的VCTE特征与死亡或脱补偿等临床结果的关联.
主要方法:
- 分析了493名补偿MASH肝硬化患者的队列.
- 将GPT-4o和Claude 3.5 Sonnet进行了比较,以确定MASH和提取峰值VCTE刚度和控制衰减参数 (CAP) 值的能力.
- 拉索-科克斯回归被用于探索性分析,将提取的特征与临床终点联系起来.
主要成果:
- GPT-4o和Claude 3.5在MASH识别方面获得了高F1分数 (分别为90.5%和80.0%).
- 与Claude 3.5 (93.3%和94.1%) 相比,GPT-4o在提取峰值VCTE刚度 (99.3%) 和CAP (99.1%) 中表现出更高的准确性.
- LLM提取成本较低 (每笔约0.012美元-0.014美元),提取的VCTE度和CAP分数是死亡或脱补偿的重要预测因素.
结论:
- LLM提供了一个高度准确和具有成本效益的解决方案,用于从非结构化的临床文本中提取关键的MASH和VCTE数据.
- 来自LLM的VCTE参数是MASH肝硬化患者临床结果的有价值预测指标.
- 鼓励肝病研究人员整合基于LLM的方法来简化现实世界的数据分析和推进临床研究.
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