Automated HIV Screening on Dutch Electronic Health Records with Large Language Models

TL;DR

This paper presents a novel approach using Large Language Models to analyze unstructured clinical notes in electronic health records for HIV screening, achieving high accuracy and low false negatives.

Contribution

It introduces a new pipeline leveraging LLMs to extract valuable information from unstructured EHR text for HIV risk assessment, which was previously underexplored.

Findings

High accuracy in HIV screening from EHR text

Low false negative rate in identifying at-risk patients

Effective use of LLMs on clinical notes

Abstract

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.