Understanding the Clinical Context of Medication Change Events in Clinical Narratives using Pre-trained Clinical Language Models

Tzu Ying Chen, Jean Aristide Aquino, Yu Wen Chiu, Wen Chao Yeh, Yung Chun Chang

研究成果: 書貢獻/報告類型會議貢獻

摘要

The ability to understand medication events in clinical narratives is crucial to gaining a comprehensive picture of the patient's medication history. There has been some prior research on identifying medication changes in clinical notes. However, because clinical documentation is longitudinal and narrative, capturing medication changes without the necessary clinical context is not sufficient in real-world applications, such as medication reconciliation and medication timeline generation. In this research, we propose a framework to use multiple clinical-based Bidirectional Encoder Representations from Transformers (BERT) for Contextualized Medication Event Extraction, which is a task to capture the multi-dimensional context of medication changes documented in clinical notes. In addition, the BERT models in the proposed framework infused clinical context-sensitive features into the method to learn the text information around the descriptions of medication. The experiments are conducted by using Contextualized Medication Event Dataset, and the results demonstrate that the proposed method outperforms ClinicalBERT, the state-of-the-art BERT model in the previous study.
原文英語
主出版物標題ICMHI 2023 - 2023 the 7th International Conference on Medical and Health Informatics
發行者Association for Computing Machinery (ACM)
頁面98-103
頁數6
ISBN(電子)9798400700712
DOIs
出版狀態已發佈 - 5月 12 2023
事件7th International Conference on Medical and Health Informatics, ICMHI 2023 - Kyoto, 日本
持續時間: 5月 12 20235月 14 2023

出版系列

名字ACM International Conference Proceeding Series

會議

會議7th International Conference on Medical and Health Informatics, ICMHI 2023
國家/地區日本
城市Kyoto
期間5/12/235/14/23

ASJC Scopus subject areas

  • 人機介面
  • 電腦網路與通信
  • 電腦視覺和模式識別
  • 軟體

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