建议对预期寿命<12 个月的癌症患者进行姑息治疗。机器学习 (ML) 技术可以帮助预测癌症患者的生存结果，并可能有助于区分谁从姑息治疗支持中受益最多。我们的目的是探讨几个客观和主观自我报告变量的重要性。通过电子心理肿瘤学和姑息治疗自我评估筛查收集主观变量。我们使用这些变量来预测 1 年死亡率。2020 年 4 月 1 日至 2021 年 3 月 31 日期间，共有 265 名晚期癌症患者完成了患者报告的结果工具。我们记录了从电子健康记录中收集的客观和主观变量、自我报告的主观变量以及所有临床变量的组合。我们使用逻辑回归 (LR)、20 倍交叉验证、决策树和随机森林来预测 1 年死亡率。我们分析了接受者操作特征 (ROC) 曲线 - AUC、精确回忆曲线 - AUC (PR-AUC) - 以及 ML 模型的特征重要性。临床非患者变量在预测中的表现（LR 达到 0.81 [ROC- AUC] 和 0.72 [F1 评分]）比患者主观报告变量（LR 达到 0.55 [ROC-AUC] 和 0.52 [F1 评分]）更具预测性。比衡量主观负担的主观患者报告变量更具预测性。这些发现表明主观负担不能可靠地用于预测生存。需要进一步的研究来阐明使用机器学习自我报告的患者负担和死亡率预测的作用。

Palliative care is recommended for patients with cancer with a life expectancy of <12 months. Machine learning (ML) techniques can help in predicting survival outcomes among patients with cancer and may help distinguish who benefits the most from palliative care support. We aim to explore the importance of several objective and subjective self-reported variables. Subjective variables were collected through electronic psycho-oncologic and palliative care self-assessment screenings. We used these variables to predict 1-year mortality.Between April 1, 2020, and March 31, 2021, a total of 265 patients with advanced cancer completed a patient-reported outcome tool. We documented objective and subjective variables collected from electronic health records, self-reported subjective variables, and all clinical variables combined. We used logistic regression (LR), 20-fold cross-validation, decision trees, and random forests to predict 1-year mortality. We analyzed the receiver operating characteristic (ROC) curve-AUC, the precision-recall curve-AUC (PR-AUC)-and the feature importance of the ML models.The performance of clinical nonpatient variables in predictions (LR reaches 0.81 [ROC-AUC] and 0.72 [F1 score]) are much more predictive than that of subjective patient-reported variables (LR reaches 0.55 [ROC-AUC] and 0.52 [F1 score]).The results show that objective variables used in this study are much more predictive than subjective patient-reported variables, which measure subjective burden. These findings indicate that subjective burden cannot be reliably used to predict survival. Further research is needed to clarify the role of self-reported patient burden and mortality prediction using ML.