Characterizing shared and distinct symptom clusters in common chronic conditions through natural language processing of nursing notes. Issue 6 (12th October 2021) - Koleck, Theresa A.; Topaz, Maxim; Tatonetti, Nicholas P.; George, Maureen; Miaskowski, Christine; Smaldone, Arlene; Bakken, Suzanne
Abstract: Data‐driven characterization of symptom clusters in chronic conditions is essential for shared cluster detection and physiological mechanism discovery. This study aims to computationally describe symptom documentation from electronic nursing notes and compare symptom clusters among patients diagnosed with four chronic conditions–chronic obstructive pulmonary disease (COPD), heart failure, type 2 diabetes mellitus, and cancer. Nursing notes ( N = 504, 395; 133, 977 patients) were obtained for the 2016 calendar year from a single medical center. We used NimbleMiner, a natural language processing application, to identify the presence of 56 symptoms. We calculated symptom documentation prevalence by note and patient for the corpus. Then, we visually compared documentation for a subset of patients ( N = 22, 657) diagnosed with COPD ( n = 3339), heart failure ( n = 6587), diabetes ( n = 12, 139), and cancer ( n = 7269) and conducted multiple correspondence analysis and hierarchical clustering to discover underlying groups of patients who have similar symptom profiles (i.e., symptom clusters) for each condition. As expected, pain was the most frequently documented symptom. All conditions had a group of patients characterized by no symptoms. Shared clusters included cardiovascular symptoms for heart failure and diabetes; pain and other symptoms for COPD, diabetes, and cancer; and a newly‐identified cognitive and neurological symptom cluster for heart failure, diabetes, and cancer. Cancer (gastrointestinal symptoms and fatigue) and COPD (mental health symptoms) each contained a unique cluster. In summary, we report both shared and distinct, as well as established and novel, symptom clusters across chronic conditions. Findings support the use of electronic health record‐derived notes and NLP methods to study symptoms and symptom clusters to advance symptom science.