ArticleFrontiers in pediatrics2020
Neonatal Sepsis Diagnosis Decision-Making Based on Artificial Neural Networks.
Article in Frontiers in pediatrics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- On the usage of artificial intelligence for identifying main attributes and predicting neonatal sepsis.Scientific reports · 2026Article
- A machine learning model for prediction of early-onset neonatal sepsis in low-income and middle-income countries: development and validation study.BMJ paediatrics open · 2026Article
- Study of a Kawasaki disease diagnostic prediction model based on the LightGBM machine learning algorithm.Frontiers in artificial intelligence · 2026Article
- Clinical prediction models to diagnose neonatal sepsis in low-income and middle-income countries: a scoping review.BMJ global health · 2025Article
- Deep Genomics: Deep Learning-Based Analysis of Genome-Sequenced Data for Identification of Gene Alterations.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Predictive gene expression signature diagnoses neonatal sepsis before clinical presentation.EBioMedicine · 2024Article
- Artificial Intelligence in Pediatrics: Learning to Walk Together.Turkish archives of pediatrics · 2024Article
- A case control study of maternal and neonatal risk factors associated with neonatal sepsis.Journal of public health research · 2023Article
- Utilizing big data from electronic health records in pediatric clinical care.Pediatric research · 2023Review
- The Role of C-reactive Protein Estimation in Determining the Duration of Antibiotic Therapy in Neonatal Sepsis.Cureus · 2022Article
- Predictive Scores for Late-Onset Neonatal Sepsis as an Early Diagnostic and Antimicrobial Stewardship Tool: What Have We Done So Far?Antibiotics (Basel, Switzerland) · 2022Review
- Accessing Artificial Intelligence for Fetus Health Status Using Hybrid Deep Learning Algorithm (AlexNet-SVM) on Cardiotocographic Data.Sensors (Basel, Switzerland) · 2022Article
- Vitamin D Deficiency, Excessive Gestational Weight Gain, and Oxidative Stress Predict Small for Gestational Age Newborns Using an Artificial Neural Network Model.Antioxidants (Basel, Switzerland) · 2022Article
- Prediction modelling in the early detection of neonatal sepsis.World journal of pediatrics : WJP · 2022Review
- Development and validation of a sepsis diagnostic scoring model for neonates with suspected sepsis.Frontiers in pediatrics · 2022Article
- Diagnosis of neonatal sepsis: the past, present and future.Pediatric research · 2022Review
- Point-of-Care (POC) Urinary L-Type Fatty Acid-Binding Protein (u-LFABP) Use in Critically Ill, Very Preterm Neonates.International journal of nephrology · 2022Article
- Knowledge gaps in late-onset neonatal sepsis in preterm neonates: a roadmap for future research.Pediatric research · 2022Review
- Applying artificial neural network for early detection of sepsis with intentionally preserved highly missing real-world data for simulating clinical situation.BMC medical informatics and decision making · 2021Article
- Stratified Management for Bacterial Infections in Late Preterm and Term Neonates: Current Strategies and Future Opportunities Toward Precision Medicine.Frontiers in pediatrics · 2021Review
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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