Machine learning models for coagulation dysfunction risk in inpatients administered β-lactam antibiotics.

ABSTRACT: The β-Lactam antibiotics represent a widely used class of antibiotics, yet the latent and often overlooked risk of coagulation dysfunction associated with their use underscores the need for proactive assessment. Machine learning methodologies can offer valuable insights into evaluating the risk of coagulation dysfunction associated with β-lactam antibiotics. This study aims to identify the risk factors associated with coagulation dysfunction related to β-lactam antibiotics and to develop machine learning models for estimating the risk of coagulation dysfunction with real-world data. A retrospective study was performed using machine learning modeling analysis on electronic health record data, employing five distinct machine learning methods. The study focused on adult inpatients discharged

SUBMITTER: Hua Y 

PROVIDER: S-EPMC11628276 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature