Electronic patient‑reported outcomes and machine learning in predicting immune‑related adverse events of immune checkpoint inhibitor therapies

Immune-checkpoint inhibitors (ICIs) have introduced novel immune-related adverse events (irAEs), arising from various organ systems without strong timely dependency on therapy dosing. Early detection of irAEs could result in improved toxicity profile and quality of life. Symptom data collected by electronic (e) patient-reported outcomes (PRO) could be used as an input for machine learning (ML) based prediction models for the early detection of irAEs. The purpose of the study is to investigate whether a ML-based prediction model for irAEs of cancer patients receiving ICIs could be created based on ePRO symptom data coupled with clinical data.