A Predictive Model Based on Machine Learning for the Early Detection of Late-Onset Neonatal Sepsis: Development and Observational Study.

ABSTRACT:

Background

Neonatal sepsis is associated with most cases of mortalities and morbidities in the neonatal intensive care unit (NICU). Many studies have developed prediction models for the early diagnosis of bloodstream infections in newborns, but there are limitations to data collection and management because these models are based on high-resolution waveform data.

Objective

The aim of this study was to examine the feasibility of a prediction model by using noninvasive vital sign data and machine learning technology.

Methods

We used electronic medical record data in intensive care units published in the Medical Information Mart for Intensive Care III clinical database. The late-onset neonatal sepsis (LONS) prediction algorithm using our proposed forward feature selectio

SUBMITTER: Song W 

PROVIDER: S-EPMC7428919 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature