评估机器学习技术预测AHOD0031试验中接受霍奇金淋巴瘤治疗的儿童的结局：来自儿童肿瘤组的报告
An evaluation of machine learning techniques to predict the outcome of children treated for Hodgkin-Lymphoma on the AHOD0031 trial: A report from the Children's Oncology Group
摘要：
在这篇文章中，我们分析了一个包含霍奇金淋巴瘤（HL）儿童参加临床试验的信息的数据集。收集接受的治疗和生存状态以及其他协变量，如人口统计学和临床测量。我们的主要任务是探索机器学习（ML）算法在生存分析背景下的潜力，以改善Cox比例风险（CoxPH）模型。我们讨论了我们想要改进的CoxPH模型的弱点，然后我们介绍了多种算法，从成熟的算法到最先进的模型，解决了这些问题。然后，我们根据一致性指数和brier评分比较每个模型。最后，根据我们的经验，我们为希望从人工智能的最新进展中受益的从业者提出了一系列建议。
英文摘要：
In this manuscript we analyze a data set containing information on children with Hodgkin Lymphoma (HL) enrolled on a clinical trial. Treatments received and survival status were collected together with other covariates such as demographics and clinical measurements. Our main task is to explore the potential of machine learning (ML) algorithms in a survival analysis context in order to improve over the Cox Proportional Hazard (CoxPH) model. We discuss the weaknesses of the CoxPH model we would like to improve upon and then we introduce multiple algorithms, from well-established ones to state-of-the-art models, that solve these issues. We then compare every model according to the concordance index and the brier score. Finally, we produce a series of recommendations, based on our experience, for practitioners that would like to benefit from the recent advances in artificial intelligence.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
