Neonatal Sepsis Diagnosis Decision-Making Based on Artificial Neural Networks.

ABSTRACT: Neonatal sepsis remains difficult to diagnose due to its non-specific signs and symptoms. Traditional scoring systems help to discriminate between septic or not patients, but they do not consider every single patient particularity. Thus, the purpose of this study was to develop an early- and late-onset neonatal sepsis diagnosis model, based on clinical maternal and neonatal data from electronic records, at the time of clinical suspicion. A predictive model was obtained by training and validating an artificial Neural Networks (ANN) algorithm with a balanced dataset consisting of preterm and term non-septic or septic neonates (early- and late-onset), with negative and positive culture results, respectively, using 25 maternal and neonatal features. The outcome of the model was sepsis or not.

SUBMITTER: Helguera-Repetto AC 

PROVIDER: S-EPMC7518045 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature