Mining Unstructured Medical Texts With Conformal Active Learning

TL;DR

This paper introduces a flexible active learning framework for extracting relevant information from unstructured EHR texts, enabling rapid, privacy-preserving epidemiological surveillance with minimal manual labeling and lightweight models.

Contribution

It presents a novel active learning approach that reduces manual labeling needs and achieves high performance using simple models for extracting data from unstructured medical texts.

Findings

Strong performance with only 200 labeled texts

Lightweight models outperform some deep learning approaches

Enables privacy-preserving, real-time epidemiological monitoring

Abstract

The extraction of relevant data from Electronic Health Records (EHRs) is crucial to identifying symptoms and automating epidemiological surveillance processes. By harnessing the vast amount of unstructured text in EHRs, we can detect patterns that indicate the onset of disease outbreaks, enabling faster, more targeted public health responses. Our proposed framework provides a flexible and efficient solution for mining data from unstructured texts, significantly reducing the need for extensive manual labeling by specialists. Experiments show that our framework achieving strong performance with as few as 200 manually labeled texts, even for complex classification problems. Additionally, our approach can function with simple lightweight models, achieving competitive and occasionally even better results compared to more resource-intensive deep learning models. This capability not only accelerates processing times but also preserves patient privacy, as the data can be processed on weaker on-site hardware rather than being transferred to external systems. Our methodology, therefore, offers a practical, scalable, and privacy-conscious approach to real-time epidemiological monitoring, equipping health institutions to respond rapidly and effectively to emerging health threats.