机器学习在预测涉及真实数据的生存结果中的应用:一个范围审查
Yinan Huang1, Jieni Li2, Mai Li3
1Department of Pharmacy Administration, School of Pharmacy, University of Mississippi, University, MS, 38677, USA.
BMC medical research methodology
|November 14, 2023
概括
机器学习 (ML) 模型显示了使用真实世界数据 (RWD) 预测时间到事件数据的前景. 随机生存森林和神经网络是常见的,主要用于瘤学预后,突出了临床决策的机会.
科学领域:
- 医疗数据分析 医疗数据分析
- 机器学习在医学中的应用
- 对生存分析的分析.
背景情况:
- 机器学习 (ML) 越来越多地用于医疗保健中的真实数据 (RWD) 分析.
- 尽管ML具有临床相关性,但使用ML预测时间到事件数据的探索较少.
- 在生存预测中,ML为分析复杂的RWD提供了潜在的优势.
研究的目的:
- 审查最近的ML应用在医疗保健中使用RWD进行生存分析.
- 识别常见的ML算法及其在预测时间到事件结果中的性能.
- 探索ML在临床实践中对生存预测的有用性.
主要方法:
- 搜索了PubMed和EMBASE数据库 (开始至2023年3月) 的相关研究.
- 包括使用RWD进行时间到事件预测的ML模型的同行评审的英语研究.
- 提取了数据来源,患者种群,生存结果,ML算法和曲线下的面积 (AUC) 的数据.
主要成果:
- 从257个引用中,包括了28个出版物.
- 随机生存森林 (57%) 和神经网络 (39%) 是最常见的ML算法.
- ML模型显示了可变的性能 (中位数AUC为0.789),主要用于瘤学预后 (43%) 和临床事件 (96%).
结论:
- 随机生存森林和神经网络是对RWD应用的关键ML算法,用于预测生存结果,特别是在瘤学中.
- 有机会利用ML在临床实践中为治疗决策提供信息.
- 需要进一步的方法开发,以提高ML模型对生存结果的有用性和适用性.
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