# Personalised dynamic super learning: an application in predicting hemodiafiltration convection volumes

arXiv: https://arxiv.org/abs/2310.08479 · HTML: https://mlsanity.com/paper/2310.08479/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 88 references that are in the map.
- First posted on arXiv 12 Oct 2023.

## Builds on (most cited first, 6 of 6)

- [All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously](https://mlsanity.com/paper/1801.01489/) (2018)
- [Tunability: Importance of Hyperparameters of Machine Learning Algorithms](https://mlsanity.com/paper/1802.09596/) (2018)
- [A review of predictive uncertainty estimation with machine learning](https://mlsanity.com/paper/2209.08307/) (2022)
- [Smoothing parameter and model selection for general smooth models](https://mlsanity.com/paper/1511.03864/) (2015)
- [Practical considerations for specifying a super learner](https://mlsanity.com/paper/2204.06139/) (2022)
- [Individual dynamic prediction of clinical endpoint from large dimensional longitudinal biomarker history: a landmark approach](https://mlsanity.com/paper/2102.01466/) (2021)

## Abstract (arXiv)

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.