Unraveling the impact of therapeutic drug monitoring via machine learning for patients with sepsis.

ABSTRACT: Clinical studies investigating the benefits of beta-lactam therapeutic drug monitoring (TDM) among critically ill patients are hindered by small patient groups, variability between studies, patient heterogeneity, and inadequate use of TDM. Accordingly, definitive conclusions regarding the efficacy of TDM remain elusive. To address these challenges, we propose an innovative approach that leverages data-driven methods to unveil the concealed connections between therapy effectiveness and patient data, collected through a randomized controlled trial (DRKS00011159; 10th October 2016). Our findings reveal that machine learning algorithms can successfully identify informative features that distinguish between healthy and sick states. These hold promise as potential markers for disease classificat

SUBMITTER: Ates HC 

PROVIDER: S-EPMC11384951 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature