基于机器学习的预测,使用癌症患者的主观和客观参数预测1年生存率
Maria Rosa Salvador Comino1, Paul Youssef2,3, Anna Heinzelmann1
1Department of Palliative Medicine, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Essen, Germany.
JCO clinical cancer informatics
|August 28, 2024
概括
客观的临床数据比主观的患者报告的变量更准确地预测1年癌症死亡率. 机器学习模型显示,客观变量在息护理环境中的生存结果预测中优越.
科学领域:
- 在瘤学瘤学.
- 抚慰性护理是一种缓解性护理.
- 机器学习 机器学习
背景情况:
- 息护理对于寿命有限的癌症患者至关重要.
- 机器学习 (ML) 可以提高瘤学中的生存结果预测.
- 识别那些从息治疗中获益最多的患者至关重要.
研究的目的:
- 评估客观和主观自我报告变量的预测力,以评估1年癌症死亡率.
- 探索电子健康记录和患者自我评估中的变量的重要性.
- 为了比较不同机器学习模型在预测死亡率方面的有效性.
主要方法:
- 利用了265名晚期癌症患者 (2020年4月至2021年3月) 的数据.
- 收集客观的临床数据和主观的患者报告的结果.
- 使用后勤回归,决策树和随机森林,具有20倍的交叉验证.
- 使用ROC-AUC和PR-AUC指标分析了性能.
主要成果:
- 客观临床变量显示出优异的预测性能 (LR:0.81 ROC-AUC,0.72 F1得分).
- 主观患者报告的变量显示出较低的预测准确性 (LR:0.55 ROC-AUC,0.52 F1得分).
- 机器学习模型强调了客观数据的重要性.
结论:
- 客观变量对1年死亡率的预测能力明显高于患者报告的主观变量.
- 在本研究中测量的主观负担不是癌症患者生存的可靠预测指标.
- 需要进一步的研究来完善使用患者报告的数据来预测死亡率的ML模型.
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