# Toward Understanding Clinical Context of Medication Change Events in Clinical Narratives

arXiv: https://arxiv.org/abs/2011.08835 · HTML: https://mlsanity.com/paper/2011.08835/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2020.

## Built on by (most cited first, 6 of 6)

- [Contextualized Medication Information Extraction Using Transformer-based Deep Learning Architectures](https://mlsanity.com/paper/2303.08259/) (2023)
- [Medical Data Augmentation via ChatGPT: A Case Study on Medication Identification and Medication Event Classification](https://mlsanity.com/paper/2306.07297/) (2023)
- [Extracting Medication Changes in Clinical Narratives using Pre-trained Language Models](https://mlsanity.com/paper/2208.08417/) (2022)
- [Scalable Medication Extraction and Discontinuation Identification from Electronic Health Records Using Large Language Models](https://mlsanity.com/paper/2506.11137/) (2025)
- [Benchmarking Open-Source Large Language Models on Healthcare Text Classification Tasks](https://mlsanity.com/paper/2503.15169/) (2025)
- [Ensemble BERT for Medication Event Classification on Electronic Health Records (EHRs)](https://mlsanity.com/paper/2506.23315/) (2025)

## Abstract (arXiv)

Understanding medication events in clinical narratives is essential to achieving a complete picture of a patient's medication history. While prior research has explored classification of medication changes from clinical notes, studies to date have not considered the necessary clinical context needed for their use in real-world applications, such as medication timeline generation and medication reconciliation. In this paper, we present the Contextualized Medication Event Dataset (CMED), a dataset for capturing relevant context of medication changes documented in clinical notes, which was developed using a novel conceptual framework that organizes context for clinical events into various orthogonal dimensions. In this process, we define specific contextual aspects pertinent to medication change events, characterize the dataset, and report the results of preliminary experiments. CMED consists of 9,013 medication mentions annotated over 500 clinical notes, and will be released to the community as a shared task in 2021.