A clinical data-driven machine learning approach for predicting the effectiveness of piperacillin-tazobactam in treating lower respiratory tract infections

In this study, we successfully developed and validated an EL model that effectively predicts the clinical effectiveness of TZP (4:1) in treating bacterial LRTIs. The model achieved a balanced performance across key evaluation metrics, demonstrating the model's potential utility in clinical decision-making. The web-based application makes this model readily accessible to clinicians, potentially helping optimize antibiotic dosing decisions and reduce both inadequate treatment and overexposure. While promising, future studies with larger datasets and prospective validation are needed to further improve the model's performance and validate its clinical utility. This work represents a step forward in using machine learning to support antimicrobial stewardship and personalized antibiotic therapy.