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是个性化医学面临的主要挑战。通过结合参数化回归与机器学习方法，个性化在线超级学习器（POSL）能够实现这种动态和个性化预测。我们调整POSL以动态预测重复连续型结局，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患者对流体积的性能演示，POSL在中位绝对误差、整体校准度、区分度和净收益方面均优于其候选学习器。最后，我们探讨了使用POSL所涉及的选择与挑战。