使用机器学习开发和评估一种多变量预测模型,用于预先发病的肺癌的整体存活率,使用机器学习
Huiping Dai1, Guang Li2, Cheng Zhang3
1Department of Cardiothoracic Surgery, The Affiliated Hospital of Hangzhou Normal University, 310015, Zhejiang, China.
概括
综合性肺切除改善了晚期肺癌 (APC) 患者的生存率,与化疗或放射治疗不同. 一个新的机器学习模型预测了APC的生存率,有助于治疗决策.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- 关于全面肺切除 (CPR),化疗或放射治疗对晚期肺癌 (APC) 患者的益处的证据有限.
- 现有的APC预后模型不足以指导治疗选择.
研究的目的:
- 开发和评估一种多变量机器学习模型,用于预测APC患者的整体存活率.
- 创建一个基于网络的预后工具,以帮助APC治疗的临床决策.
主要方法:
- 利用SEER数据库来获取APC患者的临床数据.
- 员工倾向得分匹配以减轻追溯偏见.
- 开发了91个机器学习模型来预测APC的生存率,并选择了表现最佳的模型.
主要成果:
- 综合性肺切除 (CPR) 与非CPR相比,与显著改善的整体存活率有关.
- 化疗和放射治疗在生存结果上没有显著差异.
- 最好的机器学习模型实现了0.785的C指数和0.850的5年AUC,包括13个临床变量.
结论:
- 开发的预后模型为APC患者提供了个性化的生存预测.
- 该工具通过提供个性化的生存估计来支持基于证据的治疗决策.
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