{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T06:46:57Z","timestamp":1766040417283,"version":"3.48.0"},"reference-count":21,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T00:00:00Z","timestamp":1766016000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We conducted a prospective cohort study involving 467\u202fTB 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\u2013Meier survival analysis and Cox proportional hazards modeling, with prespecified subgroup analyses by risk clusters.\n                      <jats:italic>Post hoc<\/jats:italic>\n                      analyses (e.g., correlation heatmaps of biomarkers) were explicitly labeled as exploratory. Cost-effectiveness was evaluated using incremental cost per quality-adjusted hospital day saved (ICER).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      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\u202fdays; log-rank\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.040). Adverse event rates were lower in the intervention group (26.1% vs. 27.7%). Cost analysis demonstrated potential savings of 5,000 CNY per patient in the intervention group. Limitations: Single-center design and modest sample size may limit generalizability. Unmeasured confounders (e.g., socioeconomic factors) could influence outcomes.\n                      <jats:italic>Post hoc<\/jats:italic>\n                      biomarker correlations require validation in independent cohorts.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Machine learning-guided pharmacist interventions improved TB treatment outcomes and reduced costs. Future multicenter studies should validate subtype-specific benefits.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Clinical trial registration<\/jats:title>\n                    <jats:p>\n                      <jats:ext-link>https:\/\/www.chictr.org.cn\/<\/jats:ext-link>\n                      identifier ChiCTR2300074328.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/frai.2025.1679837","type":"journal-article","created":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T06:44:22Z","timestamp":1766040262000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning-guided clinical pharmacist interventions improve treatment outcomes in tuberculosis patients: a precision medicine approach"],"prefix":"10.3389","volume":"8","author":[{"given":"Dang","family":"Yi","sequence":"first","affiliation":[]},{"given":"Huanqing","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Qian","family":"Lei","sequence":"additional","affiliation":[]},{"given":"Tingting","family":"Li","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2025,12,18]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"S5","DOI":"10.1016\/j.ijid.2020.01.041","article-title":"Precision and personalized medicine and anti-TB treatment: is TDM feasible for programmatic use?","volume":"92","author":"Alffenaar","year":"2020","journal-title":"Int. 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