Individualized treatment effect prediction for Mechanical Ventilation

Using Causal Multi-task Gaussian Process to estimate the individualized treatment effect of a low vs high PEEP regime on ICU patients

Bachelor thesis (2024)

Authors

K.T. Mc Alpine Electrical Engineering, Mathematics and Computer Science

Contributors

J.H. Krijthe Pattern Recognition and Bioinformatics - EEMCS (mentor)
R.K.A. Karlsson Pattern Recognition and Bioinformatics - EEMCS (mentor)
J.M. Smit Pattern Recognition and Bioinformatics - EEMCS (mentor)
J.A. Baaijens Pattern Recognition and Bioinformatics - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
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Published Date

25-06-2024

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

This research investigates the use of Causal Multi-task Gaussian Process (CMGP) for estimating the individualized treatment effect (ITE) of low versus high Positive End-Expiratory Pressure (PEEP) regimes on ICU patients requiring mechanical ventilation. The study addresses the complexities of determining ITE due to the inability to observe counterfactual outcomes and the confounding bias in observational studies. By employing Conditional Average Treatment Effect (CATE) estimators, such as S-Learner, T-Learner, and CMGP, the research evaluates the impact of different PEEP settings on patient survival across varied patient characteristics. The precision of these estimators is assessed using simulated data, real-world observational data from the MIMIC-IV dataset, and an external RCT dataset. The findings of this study are inconclusive, highlighting the need for further research to refine these methods and explore larger, more balanced datasets.