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

arXiv: https://arxiv.org/abs/2208.08417 · HTML: https://mlsanity.com/paper/2208.08417/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 53 references that are in the map.
- First posted on arXiv 17 Aug 2022.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://mlsanity.com/paper/2007.15779/) (2020)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets](https://mlsanity.com/paper/1906.05474/) (2019)
- [ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing](https://mlsanity.com/paper/1902.07669/) (2019)
- [Span-based Joint Entity and Relation Extraction with Transformer Pre-training](https://mlsanity.com/paper/1909.07755/) (2019)
- [A Frustratingly Easy Approach for Entity and Relation Extraction](https://mlsanity.com/paper/2010.12812/) (2020)
- [Drug-Drug Interaction Extraction from Biomedical Text Using Long Short Term Memory Network](https://mlsanity.com/paper/1701.08303/) (2017)
- [Toward Understanding Clinical Context of Medication Change Events in Clinical Narratives](https://mlsanity.com/paper/2011.08835/) (2020)
- [Learning the grammar of drug prescription: recurrent neural network grammars for medication information extraction in clinical texts](https://mlsanity.com/paper/2004.11622/) (2020)
- [Neural Medication Extraction: A Comparison of Recent Models in Supervised and Semi-supervised Learning Settings](https://mlsanity.com/paper/2110.10213/) (2021)

## Built on by (most cited first, 4 of 4)

- [Large Language Model Capabilities in Perioperative Risk Prediction and Prognostication](https://mlsanity.com/paper/2401.01620/) (2024)
- [Zero- and Few-shot Named Entity Recognition and Text Expansion in Medication Prescriptions using ChatGPT](https://mlsanity.com/paper/2409.17683/) (2024)
- [Scalable Medication Extraction and Discontinuation Identification from Electronic Health Records Using Large Language Models](https://mlsanity.com/paper/2506.11137/) (2025)
- [MasonNLP+ at SemEval-2023 Task 8: Extracting Medical Questions, Experiences and Claims from Social Media using Knowledge-Augmented Pre-trained Language Models](https://mlsanity.com/paper/2304.13875/) (2023)

## Abstract (arXiv)

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.