A Data-Driven Approach to Support Clinical Renal Replacement Therapy

TL;DR

This study develops an interpretable machine learning model using tabular data to predict membrane fouling in CRRT, outperforming RNNs and enabling clinical decision support through counterfactual analysis.

Contribution

It introduces a data-driven, interpretable approach with feature selection and counterfactual analysis for predicting membrane fouling in CRRT patients.

Findings

Achieved 77.6% sensitivity and 96.3% specificity.

Tabular models outperformed LSTM neural networks.

Counterfactual analysis identified minimal input changes to reverse predictions.

Abstract

This study investigates a data-driven machine learning approach to predict membrane fouling in critically ill patients undergoing Continuous Renal Replacement Therapy (CRRT). Using time-series data from an ICU, 16 clinically selected features were identified to train predictive models. To ensure interpretability and enable reliable counterfactual analysis, the researchers adopted a tabular data approach rather than modeling temporal dependencies directly. Given the imbalance between fouling and non-fouling cases, the ADASYN oversampling technique was applied to improve minority class representation. Random Forest, XGBoost, and LightGBM models were tested, achieving balanced performance with 77.6% sensitivity and 96.3% specificity at a 10% rebalancing rate. Results remained robust across different forecasting horizons. Notably, the tabular approach outperformed LSTM recurrent neural networks, suggesting that explicit temporal modeling was not necessary for strong predictive performance. Feature selection further reduced the model to five key variables, improving simplicity and interpretability with minimal loss of accuracy. A Shapley value-based counterfactual analysis was applied to the best-performing model, successfully identifying minimal input changes capable of reversing fouling predictions. Overall, the findings support the viability of interpretable machine learning models for predicting membrane fouling during CRRT. The integration of prediction and counterfactual analysis offers practical clinical value, potentially guiding therapeutic adjustments to reduce fouling risk and improve patient management.