Multi task learning based early prediction model for antibiotic resistance using multi institutional cohort data

Antibiotic resistance poses a significant global health challenge, with its rapid emergence driven by inappropriate antibiotic use. This study aimed to develop and compare machine learning models to predict resistance to nine antibiotic classes in hospitalized patients, addressing the high-cost labeling challenge of medical data. We conducted a retrospective study using electronic medical records from three Korean tertiary care institutions (n = 59,551). Single-task learning models (Logistic Regression, XGBoost, LightGBM, CatBoost, Multi-Layer Perceptron) and multi-task learning models (Hard Parameter Sharing, Soft Parameter Sharing) were developed. Model performance was evaluated using Area Under the ROC Curve (AUC), Precision-Recall Curve (PRC), and calibration slope. In external validation, the hard parameter sharing model achieved the highest AUC and PRC for five of nine antibiotic classes, with average values of 79.63 and 80.26, respectively. Shapley Additive Explanations analysis revealed previous antibiotic resistance as the most important predictor. Subgroup analyses showed hard parameter sharing model performed best with available previous culture test results, while soft parameter sharing excelled when they were missing. Our multi-task learning approach achieved generalized, stable, and superior performance in predicting antibiotic resistance compared with traditional models, providing a novel solution for handling partial targets in medical data.