Using a Natural Language Processing Approach to Support Rapid Knowledge Acquisition

Taneya Y Koonce; Dario A Giuse; Annette M Williams; Mallory N Blasingame; Poppy A Krump; Jing Su; Nunzia B Giuse

doi:10.2196/53516

Using a Natural Language Processing Approach to Support Rapid Knowledge Acquisition

JMIR Med Inform. 2024 Jan 30:12:e53516. doi: 10.2196/53516.

Authors

Taneya Y Koonce^#¹, Dario A Giuse^#², Annette M Williams¹, Mallory N Blasingame¹, Poppy A Krump¹, Jing Su¹, Nunzia B Giuse^{1

2}

Affiliations

¹ Center for Knowledge Management, Vanderbilt University Medical Center, Nashville, TN, United States.
² Department of Biomedical Informatics, Vanderbilt University School of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States.

^# Contributed equally.

PMID: 38289670
PMCID: PMC10865202
DOI: 10.2196/53516

Abstract

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.

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

©Taneya Y Koonce, Dario A Giuse, Annette M Williams, Mallory N Blasingame, Poppy A Krump, Jing Su, Nunzia B Giuse. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 30.01.2024.