Predictive modeling of corticosteroids sensitivity in sepsis using a supervised learning approach

Dealing with Sepsis poses a critical challenge in healthcare and necessitates rapid and well-adapted treatment responses. Corticosteroids have been used as a treatment but individual-level effects vary widely. This study aims at improving treatment efficacy by leveraging machine learning techniques to predict patients' sensitivity to corticosteroids. We use two comprehensive datasets of sepsis patients and follow the methodology proposed by Hellali (2024) to evaluate four distinct model configurations. These configurations employ Logistic Regression and Random Forest algorithms, both with and without class balancing using the Synthetic Minority Over-sampling Technique for Nominal and Continuous (SMOTE-NC) data augmentation to address mixed data types. Our findings consistently demonstrate that Random Forest models, particularly when paired with appropriate class balancing techniques, outperform other model configurations in predicting corticosteroid sensitivity using both datasets individually and combined. Notably, incorporating SMOTE-NC significantly enhances model performance, underscoring the importance of appropriately addressing imbalanced datasets in predictive modeling.