Findings from Imperial College London in Antibiotics Reported (Personalising intravenous to oral antibiotic switch decision making through fair interpretable machine learning).

Předmět:	MACHINE learning DECISION making ANTIBIOTICS TECHNOLOGICAL innovations DRUG therapy
Zdroj:	Drug Week; 1/30/2024, p511-511, 1p
Abstrakt:	A recent report from Imperial College London discusses the use of machine learning to personalize the decision-making process for switching patients from intravenous to oral antibiotic treatment. The researchers aimed to address the global threat of antimicrobial resistance and healthcare-associated infections by maximizing targeted oral therapy and reducing the use of indwelling vascular devices. The machine learning model developed achieved a mean AUROC of 0.80 and provided fair and interpretable predictions for when a patient could switch treatments. However, further evaluation of safety and efficacy is necessary before implementing this technology clinically. [Extracted from the article]
Databáze:	Complementary Index
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