Obtaining continuously updated predictions is a major challenge for personalised medicine. Leveraging combinations of parametric regressions and machine learning approaches, the personalised online super learner (POSL) can achieve such dynamic and personalised predictions. We adapt POSL to predict a repeated continuous outcome dynamically and propose a new way to validate such personalised or dynamic prediction models. We illustrate its performance by predicting the convection volume of patients undergoing hemodiafiltration. POSL outperformed its candidate learners with respect to median absolute error, calibration-in-the-large, discrimination, and net benefit. We finally discuss the choices and challenges underlying the use of POSL.

翻译：获取持续更新的预测结果是个性化医疗面临的主要挑战。通过结合参数回归与机器学习方法，个性化在线超级学习器（POSL）能够实现此类动态且个性化的预测。我们将POSL应用于重复连续结局的动态预测，并提出一种验证此类个性化或动态预测模型的新方法。通过预测接受血液透析滤过治疗患者的对流体积，我们展示了其性能表现。POSL在中位绝对误差、整体校准度、区分度及净收益方面均优于其候选学习器。最后，我们讨论了使用POSL所涉及的选择与挑战。