A diagnostic model for sepsis using an integrated machine learning framework approach and its therapeutic drug discovery.

ABSTRACT:

Background

Sepsis remains a life-threatening condition in intensive care units (ICU) with high morbidity and mortality rates. Some biomarkers commonly used in clinic do not have the characteristics of rapid and specific growth and rapid decline after effective treatment. Machine learning has shown great potential in early diagnosis, subtype analysis, accurate treatment and prognosis evaluation of sepsis.

Methods

Gene expression matrices from GSE13904 and GSE26440 were combined into a training model after quality control and standardization. Then, the intersection genes were obtained by crossing the screened differentially expressed genes (DEGs) and the module genes with the strongest correlation obtained by WGCNA analysis. 113 combined machine learning algorithms to build a

SUBMITTER: Zhang W 

PROVIDER: S-EPMC11827343 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature