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. The performance measures of the model, evaluated with an independent dataset, outperformed physician's diagnosis using the same features based on traditional scoring systems, with a 93.3% sensitivity, an 80.0% specificity, a 94.4% AUROC, and a regression coefficient of 0.974 between actual and simulated results. The model also performed well-relative to the state-of-the-art methods using similar maternal/neonatal variables. The top 10 factors estimating sepsis were maternal age, cervicovaginitis and neonatal: fever, apneas, platelet counts, gender, bradypnea, band cells, catheter use, and birth weight.

Prediction modelling in the early detection of neonatal sepsis

“…Majority (five studies) of the included articles were performed in United State of America [24,26,29,30], while the remaining each were from Israel, China, Mexico, United Kingdom and Canada [22,23,27,28,31]. The highest sample size of all the included studies was 4794, while the minimal is 36, which are illustrated in Fig.…”

“…Neonatal sepsis detection was the primary outcome of considerable number of studies (five studies) [27][28][29][30][31]. Earlyonset and late-onset sepsis detection were encountered as the main outcome in three and two different studies, respectively [22][23][24][25][26].…”

Artificial Intelligence for Clinical Decision Support in Sepsis

“…Traditional scoring systems help distinguish patients with sepsis from those with non-sepsis, but they did not consider the particularity of each patient. There is a neonatal sepsis model based on the training and verification of artificial neural network (ANN) algorithms, mainly for the diagnosis of early-onset and late-onset neonatal sepsis ( 23 ). The results show that compared with doctors based on the traditional scoring system, the performance of the model is superior by using the same features.…”

Stratified Management for Bacterial Infections in Late Preterm and Term Neonates: Current Strategies and Future Opportunities Toward Precision Medicine

“…Improvements in computational power of recent decades allow for analysis of massive amounts of data, and leveraging this power is projected to fundamentally alter medical practice ( 147 ). Machine learning models to improve precision in neonatal sepsis management are being developed, but it will take adaptation of the medical electronic infrastructure, evaluation cycles, and scientific research to allow the promises of true clinical impact to become reality ( 147 – 149 ).…”