Characterizing shared and distinct symptom clusters in common chronic conditions through natural language processing of nursing notes.
chronic conditions
natural language processing
nursing informatics
signs and symptoms
symptom clusters
Journal
Research in nursing & health
ISSN: 1098-240X
Titre abrégé: Res Nurs Health
Pays: United States
ID NLM: 7806136
Informations de publication
Date de publication:
12 2021
12 2021
Historique:
revised:
14
09
2021
received:
17
03
2021
accepted:
21
09
2021
pubmed:
13
10
2021
medline:
15
12
2021
entrez:
12
10
2021
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 34637147
doi: 10.1002/nur.22190
pmc: PMC8641786
mid: NIHMS1744585
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
906-919Subventions
Organisme : NINR NIH HHS
ID : R00 NR017651
Pays : United States
Organisme : NINR NIH HHS
ID : P30 NR016587
Pays : United States
Organisme : NINR NIH HHS
ID : K99 NR017651
Pays : United States
Informations de copyright
© 2021 Wiley Periodicals LLC.
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