Machine learning-driven decision support for antibiotic optimization in typhoid fever based on patient profiles.

introductionTyphoid fever remains a major Global public health concern, with treatment outcomes strongly influenced by antimicrobial resistance (AMR) and inter-patient variability. Determining the most appropriate antibiotic for an individual patient remains clinically challenging. Machine learning-based clinical decision support systems (CDSS) offer a promising avenue for improving diagnostic precision and guiding antibiotic selection using routinely collected clinical data.

methodsWe developed a machine learning-based decision-support framework using XGBoost models to predict (i) treatment outcome (binary), (ii) suspected typhoid classification, and (iii) a resistance-proxy score from clinical and engineered features. Model performance was evaluated using AUROC for classification tasks and R

resultsThe treatment outcome classifier demonstrated strong generalization performance, achieving a test AUROC of 0.962 ± 0.010 and an overall accuracy of 90%. The suspected typhoid classifier achieved an AUROC of 0.902 ± 0.005 with an overall classification accuracy of 82%. The resistance-proxy regression model showed moderate predictive capacity (R

conclusionThis study demonstrates the feasibility of using machine learning to simulate antibiotic selection in typhoid treatment using patient-level clinical profiles. It presents a machine learning-based decision-support framework for antibiotic optimization under uncertainty, with explicit relevance to antimicrobial resistance management in resource-limited settings. To our knowledge, this is among the first studies to integrate explainable machine learning with counterfactual drug simulation for antibiotic optimization in typhoid fever.