利用大语言模型从呼吸治疗临床记录中提取特征以增强拔管失败预测
Enhancing Extubation Failure Prediction with LLM-Derived Features from Respiratory Therapy Clinical Notes
- University of Washington（华盛顿大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究利用大语言模型从呼吸治疗临床笔记中提取特征，结合逻辑回归提升拔管失败预测性能，并揭示目标人群差异对模型泛化的影响。
AI中文摘要：
有创机械通气是一种挽救生命的疗法，但及时、安全地撤机对于预防拔管失败（EF）及其相关健康风险至关重要。我们提出了一种新颖的EF预测方法，该方法利用大语言模型和逻辑回归流程，从自由文本呼吸治疗记录中分类提取特征。应用于华盛顿大学医学中心的一个患者队列时，我们的方法识别出临床上与EF相关的特征，这些特征在纳入结构化患者数据后能提升EF预测性能。我们进一步强调了先前EF预测研究中目标人群差异（如异质性纳入标准和EF定义）如何导致模型性能的系统性差异，并阻碍研究间的泛化性。
英文摘要：
Invasive mechanical ventilation is a lifesaving therapy, but timely, safe discontinuation is essential to preventing extubation failure (EF) and related risks to health. We present a novel approach to EF prediction that leverages features classified in free-text respiratory therapy notes using a large language model and logistic regression pipeline. Applied to a patient cohort from University of Washington Medicine, our method identifies clinically meaningful EF-related features that improve EF prediction performance when included alongside structured patient data. We further highlight how differences in target populations in prior EF prediction studies, such as heterogenous inclusion criteria and EF definition, can lead to systematic differences in model performance and hinder generalizability between studies.
