Symptom Network Analysis and Unsupervised Clustering of Oncology Patients Identifies Drivers of Symptom Burden and Patient Subgroups With Distinct Symptom Patterns
Computational approaches that combine unbiased stratification of patients and in-depth modeling of symptom relationships can capture the heterogeneity in patients' symptom experiences. When validated, the core symptoms for each of the subgroups and the associated clinical determinants may inform precision-based symptom management.
Authors
- Yvette P. Conley (ORCID: https://orcid.org/0000-0002-1784-6067)
- Terri S. Armstrong (ORCID: https://orcid.org/0000-0002-2414-0492)
- Orieta Celiku (ORCID: https://orcid.org/0000-0002-0500-1032)
- Steven M. Paul (ORCID: https://orcid.org/0000-0002-3731-683X)
- Brandon Hwa-Lin Bergsneider (ORCID: https://orcid.org/0000-0001-7013-8150)
- Marilyn J. Hammer (ORCID: https://orcid.org/0000-0002-9561-6144)
- Bruce A. Cooper (ORCID: https://orcid.org/0000-0001-8932-4113)
- Christine Miaskowski (ORCID: https://orcid.org/0000-0001-5170-2027)
- Jon D. Levine
Institutions
- National Institutes of Health (US)
- University of Pittsburgh (US)
- University of California, San Francisco (US)
- Dana-Farber Cancer Institute (US)
- National Cancer Institute (US)
- Stanford University (US)
Publication Details
- Journal
- Cancer Medicine
- Published
- 2024-10-01
- DOI
- https://doi.org/10.1002/cam4.70278
- Citations
- 7
- Primary Topic
- Mental Health Research Topics
- Type
- article
- Field-Weighted Citation Impact
- 3.55
Funders
- Dr. Susan Love Foundation for Breast Cancer Research
- National Institutes of Health
- National Cancer Institute