Clinical applications of machine learning in the survival prediction and classification of sepsis: coagulation and heparin usage matter.

ABSTRACT:

Background

Sepsis is a life-threatening syndrome eliciting highly heterogeneous host responses. Current prognostic evaluation methods used in clinical practice are characterized by an inadequate effectiveness in predicting sepsis mortality. Rapid identification of patients with high mortality risk is urgently needed. The phenotyping of patients will assistant invaluably in tailoring treatments.

Methods

Machine learning and deep learning technology are used to characterize the patients' phenotype and determine the sepsis severity. The database used in this study is MIMIC-III and MIMIC-IV ('Medical information Mart for intensive care') which is a large, public, and freely available database. The K-means clustering is used to classify the sepsis phenotype. Convolutional neural

SUBMITTER: Guo F 

PROVIDER: S-EPMC9187899 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature