Risk assessment and prediction of nosocomial infections based on surveillance data using machine learning methods.

ABSTRACT:

Background

Nosocomial infections with heavy disease burden are becoming a major threat to the health care system around the world. Through long-term, systematic, continuous data collection and analysis, Nosocomial infection surveillance (NIS) systems are constructed in each hospital; while these data are only used as real-time surveillance but fail to realize the prediction and early warning function. Study is to screen effective predictors from the routine NIS data, through integrating the multiple risk factors and Machine learning (ML) methods, and eventually realize the trend prediction and risk threshold of Incidence of Nosocomial infection (INI).

Methods

We selected two representative hospitals in southern and northern China, and collected NIS data from 2014 to 2021. Th

SUBMITTER: Chen Y 

PROVIDER: S-EPMC11223322 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature