论文标题
对机器学习技术的评估，以预测AHOD0031试验中霍奇金 - 淋巴瘤治疗的儿童的结果：儿童肿瘤学组的报告
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
论文作者
论文摘要
在本手稿中，我们分析了一个数据集，其中包含参加临床试验的霍奇金淋巴瘤（HL）儿童（HL）的信息。收到的治疗和生存状况与其他协变量（例如人口统计学和临床测量）一起收集。我们的主要任务是在生存分析环境中探索机器学习（ML）算法的潜力，以改善COX比例危害（COXPH）模型。我们讨论了我们要改进的Coxph模型的弱点，然后我们引入了多种算法，从建立的算法到解决这些问题的最先进模型。然后，我们根据协和指数和Brier分数比较每个模型。最后，我们根据我们的经验提出了一系列建议，该建议希望从人工智能的最新进展中受益。
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.
