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Determining the most appropriate antibiotic for an individual patient remains clinically challenging. Machine learning\u2013based clinical decision support systems (CDSS) offer a promising avenue for improving diagnostic precision and guiding antibiotic selection using routinely collected clinical data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We 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\n                      <jats:sup>2<\/jats:sup>\n                      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.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The treatment outcome classifier demonstrated strong generalization performance, achieving a test AUROC of 0.962\u2009\u00b1\u20090.010 and an overall accuracy of 90%. The suspected typhoid classifier achieved an AUROC of 0.902\u2009\u00b1\u20090.005 with an overall classification accuracy of 82%. The resistance-proxy regression model showed moderate predictive capacity (R\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u20090.588\u2009\u00b1\u20090.011). 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\u2019s 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\u2019s primary recommendation (72.7%) and lowest when no alignment was observed (32.6%).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12911-026-03528-8","type":"journal-article","created":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T05:43:49Z","timestamp":1778564629000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Machine learning-driven decision support for antibiotic optimization in typhoid fever based on patient profiles"],"prefix":"10.1186","volume":"26","author":[{"given":"Charles","family":"Ssemuyiga","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elminah","family":"Saru","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yusuf Abbas","family":"Aleshinloye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,12]]},"reference":[{"key":"3528_CR1","doi-asserted-by":"crossref","unstructured":"Buzil\u0103 ER, Dorneanu OS, Trofin F, Sima CM, Iancu LS. 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