Abstract
Antibiotic resistance poses a critical challenge to healthcare systems worldwide. This study applies machine learning (ML) techniques to predict antibiotic sensitivity using region-specific data from healthcare facilities in Central Alabama. By leveraging advanced predictive models, we demonstrate the value of localized data in optimizing antibiotic selection and reducing misuse. Unlike generalized global approaches, our research highlights how context-specific analytics can drive actionable insights to support clinical decision-making. Our study contributes to healthcare analytics and information systems by showcasing scalable, data-driven frameworks that improve healthcare outcomes and operational efficiency.
Recommended Citation
Zhang, Yuan; Kabir, Md Moin; and Qian, Li, "LEVERAGING MACHINE LEARNING FOR DATA-DRIVEN ANTIBIOTIC STEWARDSHIP: LOCAL INSIGHTS FOR GLOBAL" (2025). SAIS 2025 Proceedings. 32.
https://aisel.aisnet.org/sais2025/32