An Unsupervised Machine Learning Approach to Evaluating the Association of Symptom Clusters With Adverse Outcomes Among Older Adults With Advanced Cancer: A Secondary Analysis of a Randomized Clinical Trial.

ABSTRACT:

Importance

Older adults with advanced cancer who have high pretreatment symptom severity often experience adverse events during cancer treatments. Unsupervised machine learning may help stratify patients into different risk groups.

Objective

To evaluate whether clusters identified from baseline patient-reported symptom severity were associated with adverse outcomes.

Design, setting, and participants

This secondary analysis of the Geriatric Assessment Intervention for Reducing Toxicity in Older Patients With Advanced Cancer (GAP70+) Trial (2014-2019) included patients who completed the National Cancer Institute Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) before starting a new cancer treatment regimen and received

SUBMITTER: Xu H 

PROVIDER: S-EPMC10034574 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature