Machine learning (ML) techniques can help predict survival among cancer patients and might help with a timely integration in palliative care. We aim to explore the importance of subjective variables self-reported and collected via electronic patient reported outcome measure (ePROM) for survival prediction. A total of 256 advanced cancer patients met the eligible criteria. We analyzed objective variables collected from electronic health records, subjective variables collected via ePROM and all clinical variables combined. We used logistic regression (LR), decision trees, and random forests to predict 1-year mortality. Receiver operating characteristic (ROC) curve - area under the curve (AUC) and the ML models feature importance were analyzed. The performance of all variables for predictions (LR reaches 0.80 [ROC AUC] and 0.72 [F1 Score]) does not improve over the performance of only clinical non-patient reported outcome (non-PRO) variables (LR reaches 0.81 [ROC AUC] and 0.72 [F1 Score]). Our study indicates that patient-reported outcome (PRO) variables, which measure subjective burden, cannot be reliably used to predict survival. Further research in this area is needed to clarify the role of self-reported patient's burden and mortality prediction via ML.