PURPOSEMachine learning (ML) algorithms that incorporate routinely collected patient-reported outcomes (PROs) alongside electronic health record (EHR) variables may improve prediction of short-term mortality and facilitate earlier supportive and palliative care for patients with cancer.METHODSWe trained and validated two-phase ML algorithms that incorporated standard PRO assessments alongside approximately 200 routinely collected EHR variables, among patients with medical oncology encounters at a tertiary academic oncology and a community oncology practice.RESULTSAmong 12,350 patients, 5,870 (47.5%) completed PRO assessments. Compared with EHR- and PRO-only algorithms, the EHR + PRO model improved predictive performance in both tertiary oncology (EHR + PROvEHRvPRO: area under the curve [AUC] 0.86 [0.85-0.87]v0.82 [0.81-0.83]v0.74 [0.74-0.74]) and community oncology (area under the curve 0.89 [0.88-0.90]v0.86 [0.85-0.88]v0.77 [0.76-0.79]) practices.CONCLUSIONRoutinely collected PROs contain added prognostic information not captured by an EHR-based ML mortality risk algorithm. Augmenting an EHR-based algorithm with PROs resulted in a more accurate and clinically relevant model, which can facilitate earlier and targeted supportive care for patients with cancer.