尽管人们对用于分析医疗保健中的真实世界数据 (RWD) 的机器学习 (ML) 算法很感兴趣，但使用 ML 来预测事件发生时间数据（临床实践中的常见场景）的探索较少。机器学习模型能够通过算法从大型、复杂的数据集中进行学习，并在预测事件发生时间数据方面具有优势。我们回顾了 ML 在医疗保健中使用 RWD 进行生存分析的最新应用。从数据库建立到 2023 年 3 月，对 PUBMED 和 EMBASE 进行了搜索，以确定使用 RWD 预测事件发生时间结果的 ML 模型的同行评审英语研究。两位评审员提取了有关数据源、患者群体、生存结果、ML 算法和曲线下面积 (AUC) 的信息。在 257 次引用中，纳入了 28 篇出版物。随机生存森林 (N = 16, 57%) 和神经网络 (N = 11, 39%) 是最流行的 ML 算法。这些 ML 模型的 AUC 存在差异（中位数 0.789，范围 0.6-0.950）。 ML 算法主要被考虑用于预测肿瘤学中的总体生存率（N = 12, 43%）。 ML 生存模型通常用于预测肿瘤学中的疾病预后或临床事件 (N = 27, 96%)，而较少用于预测治疗结果 (N = 1, 4%)。 ML 算法、随机生存森林和神经网络网络，主要用于 RWD 来预测生存结果，例如疾病预后或肿瘤学中的临床事件。本次综述表明，应用这些机器学习算法来为临床实践中的治疗决策提供信息仍有更多机会。还需要更多的方法论工作来确保 ML 模型在生存结果中的实用性和适用性。© 2023。作者。

Despite the interest in machine learning (ML) algorithms for analyzing real-world data (RWD) in healthcare, the use of ML in predicting time-to-event data, a common scenario in clinical practice, is less explored. ML models are capable of algorithmically learning from large, complex datasets and can offer advantages in predicting time-to-event data. We reviewed the recent applications of ML for survival analysis using RWD in healthcare.PUBMED and EMBASE were searched from database inception through March 2023 to identify peer-reviewed English-language studies of ML models for predicting time-to-event outcomes using the RWD. Two reviewers extracted information on the data source, patient population, survival outcome, ML algorithms, and the Area Under the Curve (AUC).Of 257 citations, 28 publications were included. Random survival forests (N = 16, 57%) and neural networks (N = 11, 39%) were the most popular ML algorithms. There was variability across AUC for these ML models (median 0.789, range 0.6-0.950). ML algorithms were predominately considered for predicting overall survival in oncology (N = 12, 43%). ML survival models were often used to predict disease prognosis or clinical events (N = 27, 96%) in the oncology, while less were used for treatment outcomes (N = 1, 4%).The ML algorithms, random survival forests and neural networks, are mainly used for RWD to predict survival outcomes such as disease prognosis or clinical events in the oncology. This review shows that more opportunities remain to apply these ML algorithms to inform treatment decision-making in clinical practice. More methodological work is also needed to ensure the utility and applicability of ML models in survival outcomes.© 2023. The Author(s).