Using a Natural Language Processing Approach to Support Rapid Knowledge Acquisition

• Published in: JMIR medical informatics Vol. 12; p. e53516
• Main Authors: Koonce, Taneya Y, Giuse, Dario A, Williams, Annette M, Blasingame, Mallory N, Krump, Poppy A, Su, Jing, Giuse, Nunzia B
• Format: Journal Article
• Language: English
• Published: Canada JMIR Publications 30.01.2024
• Subjects: Archives & records; Atrophy; Disease; Electronic health records; Knowledge acquisition; Knowledge management; National libraries; Natural language processing; Nervous system; Orthostatic hypotension; Patients; Population; Risk factors; Semantics; Viewpoint; NLP; electronic health records; natural language processing; knowledge management; machine learning; data mining
• ISSN: 2291-9694; 2291-9694
• DOI: 10.2196/53516

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.