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

INTRODUCTION: Typhoid 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. METHODS: for regression, alongside probability calibration analysis using the Brier score. SHAP was used to interpret feature importance, generate patient-level explanations, and identify latent patient subgroups. A counterfactual drug-simulation experiment was further implemented to compare clinician-prescribed antibiotics with model-recommended alternatives. RESULTS: = 0.588 ± 0.011). SHAP analysis identified platelet count, age, hemoglobin, calcium, potassium, and severity score as dominant predictors across models and revealed biologically coherent patient subgroups through attribution-based clustering. Counterfactual drug simulations showed that the model's top recommendation matched the clinician-prescribed drug in 37.1% of cases and appeared as the second-rankedt option in 28.2% of cases. Treatment success was highest when prescriptions aligned with the model's primary recommendation (72.7%) and lowest when no alignment was observed (32.6%). CONCLUSION: This 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.