Prediction Models for Sepsis-Associated Thrombocytopenia Risk in Intensive Care Units Based on a Machine Learning Algorithm.

ABSTRACT: Sepsis-associated thrombocytopenia (SAT) is a common complication in the intensive care unit (ICU), which significantly increases the mortality rate and leads to poor prognosis of diseases. Machine learning (ML) is widely used in disease prediction in critically ill patients. Here, we aimed to establish prediction models for platelet decrease and severe platelet decrease in ICU patients with sepsis based on four common ML algorithms and identify the best prediction model. The research subjects were 1,455 ICU sepsis patients admitted to Dongyang People's Hospital affiliated with Wenzhou Medical University from January 1, 2015, to October 31, 2019. Basic clinical demographic information, biochemical indicators, and clinical outcomes were recorded. The prediction models were based on four ML

SUBMITTER: Jiang X 

PROVIDER: S-EPMC8829034 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature