基于机器学习的癌症患者 1 年生存期主客观参数预测法

IF 3.3 Q2 ONCOLOGY
Maria Rosa Salvador Comino, Paul Youssef, Anna Heinzelmann, Florian Bernhardt, Christin Seifert, Mitra Tewes
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引用次数: 0

摘要

目的:建议对预期寿命不长的癌症患者进行姑息治疗 材料与方法:在 2020 年 4 月 1 日至 2021 年 3 月 31 日期间,共有 265 名晚期癌症患者填写了患者报告结果工具。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。我们使用逻辑回归(LR)、20 倍交叉验证、决策树和随机森林预测 1 年死亡率。我们分析了接收者操作特征曲线(ROC)-AUC、精确度-召回曲线-AUC(PR-AUC)以及ML模型的特征重要性:结果:临床非患者变量的预测性能(LR 达到 0.81 [ROC-AUC] 和 0.72 [F1 分数])远高于患者主观报告变量的预测性能(LR 达到 0.55 [ROC-AUC] 和 0.52 [F1 分数]):结果表明,本研究中使用的客观变量比患者报告的主观变量(衡量主观负担的变量)更具预测性。这些结果表明,主观负担不能可靠地用于预测生存率。还需要进一步研究,以明确患者自我报告的负担和使用 ML 预测死亡率的作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning-Based Prediction of 1-Year Survival Using Subjective and Objective Parameters in Patients With Cancer.

Purpose: 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.

Materials and methods: 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.

Results: 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]).

Conclusion: 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.

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来源期刊
CiteScore
6.20
自引率
4.80%
发文量
190
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