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Identifying Heart Failure Symptoms and Poor Self-Management in Home Healthcare: A Natural Language Processing Study
Book chapter   Open access   Peer reviewed

Identifying Heart Failure Symptoms and Poor Self-Management in Home Healthcare: A Natural Language Processing Study

Sena Chae, Jiyoun Song, Marietta Ojo and Maxim Topaz
Nurses and Midwives in the Digital Age, pp.15-19
Studies in health technology and informatics, v. 284, IOS Press
2021
DOI: 10.3233/SHTI210653
url
https://doi.org/10.3233/SHTI210653View
Published (Version of record) Open Access

Abstract

The goal of this natural language processing (NLP) study was to identify patients in home healthcare with heart failure symptoms and poor self-management (SM). The preliminary lists of symptoms and poor SM status were identified, NLP algorithms were used to refine the lists, and NLP performance was evaluated using 2.3 million home healthcare clinical notes. The overall precision to identify patients with heart failure symptoms and poor SM status was 0.86. The feasibility of methods was demonstrated to identify patients with heart failure symptoms and poor SM documented in home healthcare notes. This study facilitates utilizing key symptom information and patients' SM status from unstructured data in electronic health records. The results of this study can be applied to better individualize symptom management to support heart failure patients' quality-of-life.

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