在医疗领域，机器学习在预测生存时间方面受到了广泛关注。本文通过对利用机器学习和数据挖掘技术预测肺癌生存的临床数据的研究进行综述。系统文献回顾检索了MEDLINE、Scopus和Google Scholar数据库，遵循报道指南并使用COVIDENCE系统。本文纳入了2000年至2023年发表的通过机器学习预测肺癌生存的研究。风险偏倚评估采用了预测模型风险偏倚评估工具。共纳入了30项研究，其中13项（43.3%）使用了监测、流行病学和结果数据库。12项（40%）研究涉及缺失数据处理，主要通过数据转换和转换来解决。19项（63.3%）研究使用了特征选择算法，其中年龄、性别和N分期是最常选择的特征。随机森林是主要的机器学习模型，在17项（56.6%）研究中使用。尽管肺癌生存预测研究的数量有限，但基于临床数据的机器学习模型的使用自2012年以来逐渐增长。考虑到多样化患者队列和数据预处理非常重要。值得注意的是，大多数研究并未考虑缺失数据、归一化、缩放或标准化数据，可能引入偏倚。因此，有必要进行一项综合研究，使用临床数据对肺癌生存进行预测，解决这些挑战。版权所有©2023年作者。由Elsevier出版公司发表。保留所有权利。

Machine learning has gained popularity in predicting survival time in the medical field. This review examines studies utilizing machine learning and data-mining techniques to predict lung cancer survival using clinical data. A systematic literature review searched MEDLINE, Scopus, and Google Scholar databases, following reporting guidelines and using the COVIDENCE system. Studies published from 2000 to 2023 employing machine learning for lung cancer survival prediction were included. Risk of bias assessment used the prediction model risk of bias assessment tool. Thirty studies were reviewed, with 13 (43.3%) using the surveillance, epidemiology, and end results database. Missing data handling was addressed in 12 (40%) studies, primarily through data transformation and conversion. Feature selection algorithms were used in 19 (63.3%) studies, with age, sex, and N stage being the most chosen features. Random forest was the predominant machine learning model, used in 17 (56.6%) studies. While the number of lung cancer survival prediction studies is limited, the use of machine learning models based on clinical data has grown since 2012. Consideration of diverse patient cohorts and data pre-processing are crucial. Notably, most studies did not account for missing data, normalization, scaling, or standardized data, potentially introducing bias. Therefore, a comprehensive study on lung cancer survival prediction using clinical data is needed, addressing these challenges.Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.