Characterizing shared and distinct symptom clusters in common chronic conditions through natural language processing of nursing notes
Open accessAbstract
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.
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Koleck et al. (2021) utilised NLP to identify symptoms and symptom clusters from EHR nursing notes as opposed to using AI algorithms to build a predictive model.
“Although Koleck et al. (2021) had a clinical focus, it utilised NPL to identify symptoms and symptom clusters from EHR nursing notes as opposed to using AI algorithms to build a predictive model.”
From The application and use of artificial intelligence in cancer nursing: A systematic review (2024) · cites this paper as Koleck et al., 2021
Pain was the most frequent symptom.
“Pain most frequent symptom.”
From The application and use of artificial intelligence in cancer nursing: A systematic review (2024) · cites this paper as Koleck et al., 2021
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The application and use of artificial intelligence in cancer nursing: A systematic review
O'Connor, S., Vercell, A., Wong, D., Yorke, J., Fallatah, F. A., Cave, L., & Anny Chen, L.-Y. (2024) · European Journal of Oncology Nursing · doi:10.1016/j.ejon.2024.102510 · Cited by 51 · see citing works
- Authors
- Koleck, T. A., Topaz, M., Tatonetti, N. P., George, M., Miaskowski, C., Smaldone, A., & Bakken, S.
- Affiliations
- University of Pittsburgh (US)
- Columbia University (US)
- Columbia University Irving Medical Center (US)
- University of California, San Francisco (US)
- School of Nursing Columbia University New York New York USA
- Corresponding author
- Koleck, Theresa A.
- ORCID
- Venue
- Research in Nursing & Health
- Publisher
- Wiley
- Published
- 2021-10-12
- Volume
- 44
- Issue
- 6
- Pages
- 906-919
- ISSN
- 0160-6891 (ISSN-L)
- 1098-240X
- PMC
- PMC8641786
- OpenAlex
- W3207574080
- Cited by
- 33
- References
- 65
- Citations by year
- 2026: 6 · 2025: 7 · 2024: 6 · 2023: 7 · 2022: 7
- FWCI
- 3.60
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Research in Nursing & Health (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Institute of Nursing Research (K99NR017651, P30NR016587, R00NR017651)
- Topics
- Health Sciences › Medicine › Epidemiology › Chronic Disease Management Strategies
- Health Sciences › Nursing › Issues, ethics and legal aspects › Nursing Diagnosis and Documentation
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- symptom clusters, chronic disease, nursing notes, electronic health records, natural language processing, chronic obstructive pulmonary disease, heart failure, type 2 diabetes, cancer, pain, cardiovascular symptoms, fatigue, symptom documentation
- MeSH terms
- Chronic Disease, Diabetes Mellitus, Type 2, Heart Failure, Humans, Natural Language Processing, Neoplasms, Cluster Analysis, Pulmonary Disease, Chronic Obstructive, Electronic Health Records, Symptom Assessment
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