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

• URL: http://arxiv.org/abs/2208.08417v1
• Date: Wed, 17 Aug 2022 17:22:48 GMT
• Title: Extracting Medication Changes in Clinical Narratives using Pre-trained Language Models
• Authors: Giridhar Kaushik Ramachandran, Kevin Lybarger, Yaya Liu, Diwakar Mahajan, Jennifer J. Liang, Ching-Huei Tsou, Meliha Yetisgen, \"Ozlem Uzuner
• Abstract summary: 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. 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.
• Score: 1.1646531300723846
• License: http://creativecommons.org/licenses/by/4.0/
• 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 architectures improve medication change classification performance over the initial work exploring CMED. We identify medication mentions with high performance at 0.959 F1, and our proposed systems classify medication changes and their attributes at an overall average of 0.827 F1.

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