Using forest-based models to personalise ventilation treatment in the ICU
Optimising positive end-expiratory pressure assignment based on the MIMIC-IV dataset
H.D. Nowak (TU Delft - Electrical Engineering, Mathematics and Computer Science)
J.H. Krijthe – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
R.K.A. Karlsson – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
J.M. Smit – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
Jasmijn A. Baaijens – Graduation committee member (TU Delft - Pattern Recognition and Bioinformatics)
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Abstract
Positive end-expiratory pressure (PEEP) is one of the components of mechanical ventilation treatment for patients with acute respiratory distress syndrome (ARDS). Correct PEEP level can reduce additional lung injuries sustained during the hospitalisation, significantly increasing patients' chances for survival. In this paper, we focus on estimating the difference in patient mortality when assigned high or low PEEP level. We look at three machine learning models specifically designed for such tasks: S-learner, T-learner and causal forest. Through a series of experiments, we determine their best use cases based on simulated data and measure their performance on a real-life dataset - MIMIC-IV. In our analysis, we find that after tuning the hyperparameters, the models can, to some degree, make valuable predictions and reveal heterogeneity in the treatment effect. However, when evaluated on a separate dataset, the models' performance drops significantly.