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- W4387635872 abstract "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." @default.
- W4387635872 created "2023-10-14" @default.
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- W4387635872 date "2023-10-12" @default.
- W4387635872 modified "2023-10-15" @default.
- W4387635872 title "Personalised dynamic super learning: an application in predicting hemodiafiltration's convection volumes" @default.
- W4387635872 doi "https://doi.org/10.48550/arxiv.2310.08479" @default.
- W4387635872 hasPublicationYear "2023" @default.
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