Machine Learning Model to Identify Sepsis Patients in the Emergency Department: Algorithm Development and Validation.

ABSTRACT: Accurate stratification of sepsis can effectively guide the triage of patient care and shared decision making in the emergency department (ED). However, previous research on sepsis identification models focused mainly on ICU patients, and discrepancies in model performance between the development and external validation datasets are rarely evaluated. The aim of our study was to develop and externally validate a machine learning model to stratify sepsis patients in the ED. We retrospectively collected clinical data from two geographically separate institutes that provided a different level of care at different time periods. The Sepsis-3 criteria were used as the reference standard in both datasets for identifying true sepsis cases. An eXtreme Gradient Boosting (XGBoost) algorithm was develo

SUBMITTER: Lin PC 

PROVIDER: S-EPMC8623760 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature