Conclusions
SECONDARY
In this secondary analysis of a cluster trial of more than 700 older adults, unsupervised machine learning effectively identified patients with similar characteristics and stratified patients into clusters based on symptom severity. Our findings reinforce the importance of routine symptom assessment prior to treatment initiation as a best practice standard. PRO-CTCAEs assessed at treatment initiation can add information to the GA and performance status on patient vulnerability and inform potential treatment tolerability. Machine learning may be used to guide the development of risk stratification tools with the potential to assist clinicians in identifying older adults with a high risk of adverse outcomes.