Deep Natural Language Processing to Identify Symptom Documentation in Clinical Notes for Patients With Heart Failure Undergoing Cardiac Resynchronization Therapy

2020 ◽  
Vol 60 (5) ◽  
pp. 948-958.e3
Author(s):  
Richard E. Leiter ◽  
Enrico Santus ◽  
Zhijing Jin ◽  
Katherine C. Lee ◽  
Miryam Yusufov ◽  
...  
Keyword(s):  
Heart Failure ◽  
Natural Language Processing ◽  
Natural Language ◽  
Cardiac Resynchronization Therapy ◽  
Language Processing ◽  
Cardiac Resynchronization ◽  
Resynchronization Therapy ◽  
Clinical Notes ◽  
Patients With Heart Failure

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

2021 ◽  
Author(s):  
Sena Chae ◽  
Jiyoun Song ◽  
Marietta Ojo ◽  
Maxim Topaz
Keyword(s):  
Heart Failure ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Symptom Management ◽  
Home Healthcare ◽  
Self Management ◽  
Clinical Notes ◽  
Patients With Heart Failure

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.

Sign in / Sign up

Export Citation Format

Share Document