用大模型分析病历文本，自动筛查艾滋病高危患者。
Automated HIV Screening on Dutch Electronic Health Records with Large Language Models
- 用大语言模型解析电子病历中的非结构化文本
- 在荷兰一家医院数据上实现高准确率与低漏诊率
- 适合医疗AI研究者和临床筛查系统开发者
高效筛查和早期诊断HIV对减少传播至关重要。尽管大规模实验室检测不可行，但电子健康记录（EHR）的广泛应用为此提供了新可能。现有研究多基于患者人口统计等结构化数据，使用机器学习提升诊断效果，但常忽略临床笔记等非结构化文本中潜在的HIV风险信息。本研究提出一种新流程，利用大语言模型（LLM）分析未结构化的EHR文本，判断患者是否需进一步进行HIV检测。在鹿特丹伊拉斯谟大学医学中心的临床数据上，该方法实现了高准确率并保持了低假阴性率。
原文摘要 · Abstract (English)
Efficient screening and early diagnosis of HIV are critical for reducing onward transmission. Although large scale laboratory testing is not feasible, the widespread adoption of Electronic Health Records (EHRs) offers new opportunities to address this challenge. Existing research primarily focuses on applying machine learning methods to structured data, such as patient demographics, for improving HIV diagnosis. However, these approaches often overlook unstructured text data such as clinical notes, which potentially contain valuable information relevant to HIV risk. In this study, we propose a novel pipeline that leverages a Large Language Model (LLM) to analyze unstructured EHR text and determine a patient's eligibility for further HIV testing. Experimental results on clinical data from Erasmus University Medical Center Rotterdam demonstrate that our pipeline achieved high accuracy while maintaining a low false negative rate.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。