Extracting Medication Changes in Clinical Narratives using Pre-trained Language Models

TL;DR

This paper presents BERT-based models for extracting detailed medication change information from clinical notes, improving accuracy in identifying change types, initiators, temporality, and negation, which is vital for patient care.

Contribution

The work introduces three novel BERT-based systems that significantly enhance medication change classification from clinical narratives using the CMED dataset.

Findings

Improved classification accuracy over previous methods

Effective extraction of change type, initiator, and temporality

Demonstrated robustness across diverse clinical notes

Abstract

An accurate and detailed account of patient medications, including medication changes within the patient timeline, is essential for healthcare providers to provide appropriate patient care. Healthcare providers or the patients themselves may initiate changes to patient medication. Medication changes take many forms, including prescribed medication and associated dosage modification. These changes provide information about the overall health of the patient and the rationale that led to the current care. Future care can then build on the resulting state of the patient. This work explores the automatic extraction of medication change information from free-text clinical notes. The Contextual Medication Event Dataset (CMED) is a corpus of clinical notes with annotations that characterize medication changes through multiple change-related attributes, including the type of change (start, stop, increase, etc.), initiator of the change, temporality, change likelihood, and negation. Using CMED, we identify medication mentions in clinical text and propose three novel high-performing BERT-based systems that resolve the annotated medication change characteristics. We demonstrate that our proposed systems improve medication change classification performance over the initial work exploring CMED.