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Updated: Jan 14, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

用大型语言模型提取的临床特征预测产后出血.

Elizabeth G Woo¹, Israel Zighelboim¹, Tyler Gifford¹

¹Center for Computational Medicine and Clinical AI, Department of Medicine, and the Section of Ultrasound, Genetics, and the Fetal Neonatal Care Center, Department of Obstetrics and Gynecology, University of Chicago, Chicago, Illinois; the Department of Obstetrics and Gynecology and the Division of Gynecologic Oncology, St. Luke's Cancer Center, St. Luke's University Health Network, Bethlehem, Pennsylvania; the Department of Biomedical Data Science, Stanford University, Stanford, California; and Maternal Fetal Medicine, Oregon Health & Science University, Portland, Oregon.

|October 20, 2025

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大型语言模型 (LLM) 可以使用临床笔记预测产后出血 (PPH),优于传统方法. 基于LLM的方法为更好的PPH预防提供了更好的风险分层.

科学领域:

医疗信息学医疗信息学
医疗保健中的人工智能
产科和妇科产科和妇科

背景情况:

产后出血 (PPH) 仍然是导致孕产妇发病和死亡的主要原因.
在分娩开始前准确预测PPH风险对于及时干预至关重要.
当前的预测模型通常依赖于结构化数据,在临床笔记中可能缺少关键信息.

研究的目的:

从产前临床笔记中评估大语言模型 (LLM) 在预测产后出血 (PPH) 的有效性.
将基于LLM的预测模型的性能与使用结构化数据的传统方法进行比较.
在不同的PPH结果定义中评估LLM绩效,包括基于干预的新定义.

主要方法:

使用来自大型健康网络的电子病历数据进行了一项回顾性队列研究.
使用了两种PPH定义:估计的血液损失 (EBL-QBL) 和基于临床干预的定义 (cPPH).
评估了三个预测管道:仅基于结构化数据的机器学习,来自笔记的LLM直接预测,以及与结构化数据相结合的LLM提取的特征.

主要成果:

基于LLM的直接预测实现了PPH的最高预测性能 (AUROC 0.79-0.80).
结合LLM特征和结构化数据的可解释模型表现强 (AUROC 0.76-0.78).

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Published on: December 6, 2024

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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只有结构化数据模型的性能明显较低 (AUROC 0.65-0.71),而LLM特征提取发现了47个显著预测因素.

结论:

基于LLM的方法显示出显著的潜力,可以提高PPH风险分层超出结构化数据.
该LLM特征提取方法提供了一个平衡的预测准确性和临床可解释性.
将LLM整合到临床工作流中可以实现更早的PPH检测和有针对性的预防策略.