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.

Mechanical ventilation with positive end-expiratory pressure (PEEP) is a critical intervention for patients in intensive care units (ICUs) with acute respiratory failure. Identifying the optimal PEEP level is challenging due to conflicting evidence from studies comparing low and high PEEP regimes. This research explores machine learning methods for estimating individualized treatment effects (ITE) in ICU patients on different PEEP levels using the observational MIMIC-IV dataset. Various conditional average treatment effect (CATE) estimators, including S-, T-, and DR-learners, are applied to control for confounders and identify PEEP effects on patient subgroups. This research aims to compare the performance of the aforementioned CATE estimators, with a focus on the doubly-robust (DR) learner, and determine which one is best suited for causal inference in this context. The DR-learner offers increased resilience to model errors since it integrates two models. Simulations using mean squared error (MSE) show the DR-learner performs well with confounded data and differing linear response functions between control and treatment groups. However, when looking at the performance on the MIMIC-IV dataset, the predictions are unstable, failing to reliably identify the optimal PEEP for increasing patient survival. This trend is also observed in a randomized controlled trial (RCT) dataset, with the area under the Qini curve (AUQC) close to zero, indicating difficulties in identifying the effects of PEEP settings. Despite promising simulation results, real-world application shows limitations in these machine learning methods for optimal PEEP identification.

In the intensive care unit (ICU), optimizing mechanical ventilation settings, particularly the positive end-expiratory pressure (PEEP), is crucial for patient survival. This paper investigates the application of neural network-based machine learning methods to personalize PEEP settings in the ICU, aiming to improve patient survival outcomes. The research focuses on two specific algorithms, TARNet and CFR, evaluating their ability to estimate individualized treatment effects of lower versus higher PEEP regimes. The study is structured into three phases: controlled simulations, application to the MIMIC-IV dataset, and validation using a randomized control trial dataset. TARNet and CFR showed potential for estimating the individualized treatment effects but required large datasets for optimal performance. In the case where limited data is available, these models are upstaged by simpler learners, such as the S- and T-learners. The study concludes that while neural network-based methods hold promise for personalizing ICU treatment, their efficacy is heavily influenced by data availability and quality.

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.

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.

Mechanical ventilation is a vital supportive measure for patients with acute respiratory distress syndrome (ARDS) in the intensive care unit. An important setting in the ventilator is the positive end-expiratory pressure (PEEP), which can reduce lung stress but may also cause harmful side effects. This research investigates the personalization of PEEP settings based on patient characteristics using three meta-learning algorithms (S-, T-, and X-learner) to estimate the conditional average treatment effect. Additionally, the hypothesis that the X-learner performs particularly well under a significant imbalance in patient numbers between treatment groups is tested. Results show that the X-learner slightly outperforms the S- and T-learners in terms of mean squared error under various unbalanced conditions in simulated data. However, the overall ability of these meta-learners to identify patients benefiting from high PEEP remains inconclusive. When using gradient boosted trees or random forest as base models, cumulative gain curves on MIMIC-IV data indicate potential overfitting. While the X-learner performs somewhat better on this data, the low area under the curve scores suggests a minimal distinction between high and low PEEP groups. External validation with data from a randomized control confirms that the models do not effectively distinguish between treatment groups. These findings suggest that further investigation with more complex models and real-world data is needed to validate the potential of meta-learning algorithms in personalizing PEEP settings for ARDS patients.

Individualizing mechanical ventilation treatment regimes remains a challenge in the intensive care unit (ICU). Reinforcement Learning (RL) offers the potential to improve patient outcomes and reduce mortality risk, by optimizing ventilation treatment regimes. We focus on the Offline RL setting, using Offline Policy Evaluation (OPE), specifically importance sampling (IS), to evaluate policies learned from observational data. Using a running example, we illustrate how a large difference between the learned policy and actual clinical behavior (behavior policy) limits the reliability of IS-based OPE. To assess this reliability, we use the Effective Sample Size (ESS) as a diagnostic. To achieve reliable evaluation, we apply policy shaping, by incorporating a divergence constraint in the policy learning objective, aiming to reduce the difference between the evaluation and behavior policy. We consider both a Kullback-Leibler (KL) divergence constraint and introduce a new constraint, the ESS divergence. Since effective OPE relies on an accurate estimate of the true behavior policy, we address how such an estimate is acquired. Various classifiers for estimating the behavior policy are systematically evaluated, focusing on both discrimination and calibration performance. Empirical results show the difficulty of learning policies that outperform existing clinical practices and generalize well to unseen patients. Although policy shaping improves the reliability of policy evaluations, no policies that consistently outperform clinician practice were found. The KL divergence constraint generalized better to unseen patients than the ESS divergence, which achieved large ESS without actually reducing the difference between the evaluation and behavior policy. We underscore the necessity of a cautious approach to applying RL in healthcare, and advocate that assessing OPE reliability and behavior policy calibration becomes standard practice, to ensure that only effective and reliable RL policies are considered for real-world clinical trials.