A Data-Driven Approach to Support Clinical Renal Replacement Therapy

Objective. The study aimed to develop and evaluate the viability of a data-driven approach to predict membrane fouling in critically ill patients undergoing CRRT using machine learning algorithms. Moreover, Counterfactual Analysis is used to detect counterfactuals, i.e. the minimal modifications to the input of the machine learning model required to revert a membrane fouling prediction. Design. The study utilizes time series from Careggi University Hospital ICU. A subset of 16 specific features was recognized, following the recommendations of clinicians, as the most relevant indicators to train machine learning models for predicting membrane fouling dynamics. To keep the approach simple, interpretable, and amenable to detect reliable counterfactuals, the study mainly focuses on a tabular data approach, not involving the time series' interdependence inherent within each treatment. Since the number of membrane fouling cases is considerably smaller than the overall number of treatments, the ADASYN oversampling method was utilized as preprocessing step for a more equitable representation of the minority classes. A Shapley values based Counterfactual Analysis is applied to the best prediction model, in order to detect counterfactuals whose quality is measured through a proper score. Results. The specific methods adopted include Random Forest, XGBoost and LightGBM. For all these methods a rebalancing rate of 10% with respect to the majority class showed the most balanced performance with a sensitivity of 0.776 and a specificity of 0.963. The performance obtained by all methods proved to be robust with respect to different length forecasting horizons. The tabular data approach was shown not to be a limitation as it outperformed the Long Short-Term Memory Recurrent Neural Networks which intrinsically take into account temporal relationships. It is shown that reducing the features to 5 via a feature selection method, yields more simple and interpretable models, without compromising too much the accuracy. The adopted Counterfactual Analysis method is able to detect counterfactuals which appear promising according to the considered quality score. Conclusions. The experimental study provides promising results concerning the adoption of data-driven machine learning methods to predict membrane fouling events during CRRT. The practical implications for clinicians and nurses managing CRRT are significant; additionally, the interpretability afforded by using a reduced set of features enhances the understanding of how the model reaches its conclusions without sacrificing too much predictive power. The predictions of the model and the associated Counterfactual Analysis can inform therapeutic adjustments leading to more effective patient care while minimizing the risk of membrane fouling.