Automated HIV Screening on Dutch Electronic Health Records with Large Language Models
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arXiv
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| Auteurs principaux: | , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866918171045265408 |
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| author | Zhou, Lang Jhingoer, Amrish Luo, Yinghao Vliegenthart--Jongbloed, Klaske Jordans, Carlijn Werkhoven, Ben Seinen, Tom van Mulligen, Erik Rokx, Casper Li, Yunlei |
| author_facet | Zhou, Lang Jhingoer, Amrish Luo, Yinghao Vliegenthart--Jongbloed, Klaske Jordans, Carlijn Werkhoven, Ben Seinen, Tom van Mulligen, Erik Rokx, Casper Li, Yunlei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_19879 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automated HIV Screening on Dutch Electronic Health Records with Large Language Models Zhou, Lang Jhingoer, Amrish Luo, Yinghao Vliegenthart--Jongbloed, Klaske Jordans, Carlijn Werkhoven, Ben Seinen, Tom van Mulligen, Erik Rokx, Casper Li, Yunlei Computation and Language 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. |
| title | Automated HIV Screening on Dutch Electronic Health Records with Large Language Models |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2510.19879 |