AIMC Topic: Piperacillin, Tazobactam Drug Combination

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A clinical data-driven machine learning approach for predicting the effectiveness of piperacillin-tazobactam in treating lower respiratory tract infections.

BMC pulmonary medicine Mar 17, 2025
BACKGROUND: In hospitalized patients, inadequate antibiotic dosage leading to bacterial resistance and increased antimicrobial use intensity due to overexposure to antibiotics are common problems. In the present study, we constructed a machine learni...
ROC Curve Logistic Models China Bayes Theorem Respiratory Tract Infections Treatment Outcome Support Vector Machine Aged Adult Male Middle Aged Retrospective Studies Female Anti-Bacterial Agents Humans Machine Learning Piperacillin, Tazobactam Drug Combination
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Employing artificial intelligence for optimising antibiotic dosages in sepsis on intensive care unit: a study protocol for a prospective observational study (KI.SEP).

BMJ open Dec 12, 2024
INTRODUCTION: In sepsis treatment, achieving and maintaining effective antibiotic therapy is crucial. However, optimal antibiotic dosing faces challenges due to significant variability among patients with sepsis. Therapeutic drug monitoring (TDM), th...
Anti-Bacterial Agents Prospective Studies Humans Intensive Care Units Female Machine Learning Germany Artificial Intelligence Research Design Drug Monitoring Piperacillin, Tazobactam Drug Combination Sepsis Meropenem Observational Studies as Topic
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Simplifying Piperacillin/Tazobactam Dosing: Pharmacodynamics of Utilizing Only 4.5 or 3.375 g Doses for Patients With Normal and Impaired Renal Function.

Journal of pharmacy practice Dec 21, 2016
OBJECTIVES: To evaluate the pharmacodynamic exposure of piperacillin/tazobactam across the renal function range using 4.5 or 3.375 g dosing regimens.
Renal Insufficiency Monte Carlo Method Enterobacteriaceae Piperacillin, Tazobactam Drug Combination Kidney Penicillanic Acid Dose-Response Relationship, Drug Piperacillin Infusions, Intravenous Pseudomonas aeruginosa Humans Anti-Bacterial Agents Microbial Sensitivity Tests
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Machine Learning Model to Guide Empirical Antimicrobial Therapy in Febrile Neutropenic Patients With Hematologic Malignancies.

Anticancer research Jun 1, 2025
BACKGROUND/AIM: Optimal antimicrobial selection for patients with febrile neutropenia (FN) may differ depending on the underlying mechanisms. We aimed to develop a model for predicting the severity of bacteremia in patients with FN and hematologic ma...
Hematologic Neoplasms Male Anti-Bacterial Agents Aged Humans Anti-Infective Agents Adult Retrospective Studies Machine Learning Piperacillin, Tazobactam Drug Combination Febrile Neutropenia Female Middle Aged Bacteremia
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