Semantic-Level New Information Identification in Electronic Health Records Using Text-Mining Techniques

Ya Han Hu, Hsiao Ting Tseng, Chun Feng Huang

研究成果: 書貢獻/報告類型會議論文篇章同行評審

摘要

Electronic health records (EHRs) are widely used in healthcare systems to store and transmit patients' health records. They have many advantages, such as saving space, increasing efficiency, and facilitating communication. However, they also have a major drawback: information redundancy. Healthcare professionals often use copy and paste to write clinical notes, which leads to excessive similarity and low diversity in EHRs. This impairs the readability and quality of EHRs and hinders decision making. To address this problem, this study proposes a text-mining approach to identify new information at semantic-level in EHRs. Unlike previous studies that focused on word-level identification, we use concept occurrence and concept similarity score methods to annotate new information at semantic-level and evaluate them with gold standards. The experimental evaluation demonstrates that the method proposed in this study achieves an F1-score ranging from 78.57 to 80.31 under various parameter combinations. The proposed method enables healthcare professionals to read EHRs more efficiently and make more informed decisions.

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主出版物標題Proceedings of the 57th Annual Hawaii International Conference on System Sciences, HICSS 2024
編輯Tung X. Bui
發行者IEEE Computer Society
頁面3336-3343
頁數8
ISBN(電子)9780998133171
出版狀態已出版 - 2024
事件57th Annual Hawaii International Conference on System Sciences, HICSS 2024 - Honolulu, United States
持續時間: 3 1月 20246 1月 2024

出版系列

名字Proceedings of the Annual Hawaii International Conference on System Sciences
ISSN(列印)1530-1605

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???event.eventtypes.event.conference???57th Annual Hawaii International Conference on System Sciences, HICSS 2024
國家/地區United States
城市Honolulu
期間3/01/246/01/24

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