Using a Natural Language Processing Approach to Support Rapid Knowledge Acquisition

被引：1

作者：

Koonce, Taneya Y. ^{[1
,3
]}

Giuse, Dario A. ^{[2
]}

Williams, Annette M. ^{[1
]}

Blasingame, Mallory N. ^{[1
]}

Krump, Poppy A. ^{[1
]}

Su, Jing ^{[1
]}

Giuse, Nunzia B. ^{[1
,2
]}

机构：

[1] Vanderbilt Univ, Ctr Knowledge Management, Med Ctr, Nashville, TN USA

[2] Vanderbilt Univ, Med Ctr, Dept Biomed Informat, Sch Med, Nashville, TN USA

[3] Vanderbilt Univ, Ctr Knowledge Management, Med Ctr, 3401 West End,Suite 304, Nashville, TN 37203 USA

来源：

JMIR MEDICAL INFORMATICS | 2024年 / 12卷

关键词：

natural language processing; electronic health records; machine learning; data mining; knowledge management; NLP;

D O I：

10.2196/53516

中图分类号：

R-058 [];

学科分类号：

摘要：

Implementing artificial intelligence to extract insights from large, real-world clinical data sets can supplement and enhance knowledge management efforts for health sciences research and clinical care. At Vanderbilt University Medical Center (VUMC), the in-house developed Word Cloud natural language processing system extracts coded concepts from patient records in VUMC's electronic health record repository using the Unified Medical Language System terminology. Through this process, the Word Cloud extracts the most prominent concepts found in the clinical documentation of a specific patient or population. The Word Cloud provides added value for clinical care decision-making and research. This viewpoint paper describes a use case for how the VUMC Center for Knowledge Management leverages the condition-disease associations represented by the Word Cloud to aid in the knowledge generation needed to inform the interpretation of phenome-wide association studies.

引用

页数：6

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