Natural Language Processing to Detect Cognitive Concerns in Electronic Health Records Using Deep Learning

• URL: http://arxiv.org/abs/2011.06489v1
• Date: Thu, 12 Nov 2020 16:59:56 GMT
• Title: Natural Language Processing to Detect Cognitive Concerns in Electronic Health Records Using Deep Learning
• Authors: Zhuoqiao Hong, Colin G. Magdamo, Yi-han Sheu, Prathamesh Mohite, Ayush Noori, Elissa M. Ye, Wendong Ge, Haoqi Sun, Laura Brenner, Gregory Robbins, Shibani Mukerji, Sahar Zafar, Nicole Benson, Lidia Moura, John Hsu, Bradley T. Hyman, Michael B. Westover, Deborah Blacker, Sudeshna Das
• Abstract summary: Dementia is under-recognized in the community, under-diagnosed by healthcare professionals, and under-coded in claims data. Information on cognitive dysfunction is often found in unstructured clinician notes within medical records but manual review by experts is time consuming and often prone to errors. In order to identify patients with cognitive concerns in electronic medical records, we applied natural language processing (NLP) algorithms.
• Score: 0.970914263240787
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Dementia is under-recognized in the community, under-diagnosed by healthcare professionals, and under-coded in claims data. Information on cognitive dysfunction, however, is often found in unstructured clinician notes within medical records but manual review by experts is time consuming and often prone to errors. Automated mining of these notes presents a potential opportunity to label patients with cognitive concerns who could benefit from an evaluation or be referred to specialist care. In order to identify patients with cognitive concerns in electronic medical records, we applied natural language processing (NLP) algorithms and compared model performance to a baseline model that used structured diagnosis codes and medication data only. An attention-based deep learning model outperformed the baseline model and other simpler models.

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