ABSTRACT 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.

中文翻译：

---

在 AHOD0031 试验中对机器学习技术进行评估以预测霍奇金淋巴瘤儿童的治疗结果

摘要 在这份手稿中，我们分析了一个包含参与临床试验的霍奇金淋巴瘤 (HL) 儿童信息的数据集。接受的治疗和生存状态与其他协变量如人口统计学和临床​​测量一起收集。我们的主要任务是探索机器学习 (ML) 算法在生存分析环境中的潜力，以改进 Cox Proportional Hazard (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，从完善的算法到最先进的模型，可以解决这些问题。然后我们根据一致性指数和 Brier 分数比较每个模型。最后，我们根据我们的经验提出一系列建议，