ArticleFrontiers in artificial intelligence2025
Machine learning-guided clinical pharmacist interventions improve treatment outcomes in tuberculosis patients: a precision medicine approach.
Article in Frontiers in artificial intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: The heterogeneity in tuberculosis (TB) treatment responses necessitates a precision medicine approach. This study employed machine learning techniques to identify patient subtypes and optimize clinical pharmacist interventions. Methods: We conducted a prospective cohort study involving 467 TB patients (218 in the intervention group receiving machine learning-guided pharmacist care and 249 in the control group receiving standard care). Primary outcomes included time to sputum conversion (smear, culture, TB-RNA) and duration of hospitalization; secondary outcomes encompassed adverse event rates (hepatotoxicity, renal impairment, etc.), cost-effectiveness, and biomarker dynamics. Patient stratification was performed using unsupervised learning (k-means/PCA) on clinical and laboratory parameters. Treatment outcomes were assessed via Kaplan-Meier survival analysis and Cox proportional hazards modeling, with prespecified subgroup analyses by risk clusters. Results: Machine learning identified 2 distinct patient subtypes (inflammatory vs. immunologic profiles). The intervention group showed significantly shorter hospital stays (primary outcome: median 49.0 vs. 57.0 days; log-rank Conclusion: Machine learning-guided pharmacist interventions improved TB treatment outcomes and reduced costs. Future multicenter studies should validate subtype-specific benefits. Clinical trial registration: https://www.chictr.org.cn/ identifier ChiCTR2300074328.
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