ArticleJournal of the American Medical Informatics Association : JAMIA
Medical decision support using machine learning for early detection of late-onset neonatal sepsis.
Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 77 papers, 2 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
77 citing papers in PubMed, 2 syntheses or guidelines pooled it, 194 citations in OpenAlex.
- Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review.F1000Research · 2019Pooled it
- Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2017Pooled it
- Temporal Changes in Inflammatory Markers and Laboratory Parameters in Relation to Risk of Neonatal Sepsis.Journal of clinical medicine · 2026Article
- Machine Learning-Based Prediction of Culture-Confirmed Neonatal Sepsis in a Tertiary Neonatal Intensive Care Unit: Retrospective Cohort Study.JMIR medical informatics · 2026Article
- Machine learning models for predicting neonatal bacterial infections: a retrospective cohort study.European journal of pediatrics · 2026Article
- Antimicrobial stewardship in UK special care baby units: gaps in research and future directions.JAC-antimicrobial resistance · 2026Review
- Aligning neonatal sepsis triage diagnostics with WHO target product profiles in low- and middle-income countries.BMJ global health · 2026Review
- On the usage of artificial intelligence for identifying main attributes and predicting neonatal sepsis.Scientific reports · 2026Article
- Growth Trajectories of Medication Adherence in Pediatric Organ Transplantation and Their Relationship to Posttransplant Health Outcomes.Transplantation direct · 2026Article
- Automated sepsis prediction from unstructured electronic health records using natural language processing: a retrospective cohort study.BMJ health & care informatics · 2025Article
- AI models in clinical neonatology: a review of modeling approaches and a consensus proposal for standardized reporting of model performance.Pediatric research · 2025Review
- Present and future of microbiome-targeting therapeutics.The Journal of clinical investigation · 2025Review
- Reformulating patient stratification for targeting interventions by accounting for severity of downstream outcomes resulting from disease onset: a case study in sepsis.Journal of the American Medical Informatics Association : JAMIA · 2025Observational
- Leveraging Machine Learning for Pediatric Appendicitis Diagnosis: A Retrospective Study Integrating Clinical, Laboratory, and Imaging Data.Health science reports · 2025Article
- A machine learning approach to predict mortality and neonatal persistent pulmonary hypertension in newborns with congenital diaphragmatic hernia. A retrospective observational cohort study.European journal of pediatrics · 2025Observational
- Review
- AI Algorithms for Modeling the Risk, Progression, and Treatment of Sepsis, Including Early-Onset Sepsis-A Systematic Review.Journal of clinical medicine · 2024Review
- Implementing Explainable Machine Learning Models for Practical Prediction of Early Neonatal Hypoglycemia.Diagnostics (Basel, Switzerland) · 2024Article
- Machine Learning: A Potential Therapeutic Tool to Facilitate Neonatal Therapeutic Decision Making.Paediatric drugs · 2024Review
- Artificial Intelligence-Based Prediction of Contrast Medium Doses for Computed Tomography Angiography Using Optimized Clinical Parameter Sets.Methods of information in medicine · 2024Article
17 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 2 countries.
Funding
Abstract
objectiveThe objective was to develop non-invasive predictive models for late-onset neonatal sepsis from off-the-shelf medical data and electronic medical records (EMR).
designThe data used in this study are from 299 infants admitted to the neonatal intensive care unit in the Monroe Carell Jr. Children's Hospital at Vanderbilt and evaluated for late-onset sepsis. Gold standard diagnostic labels (sepsis negative, culture positive sepsis, culture negative/clinical sepsis) were assigned based on all the laboratory, clinical and microbiology data available in EMR. Only data that were available up to 12 h after phlebotomy for blood culture testing were used to build predictive models using machine learning (ML) algorithms. MEASUREMENT: We compared sensitivity, specificity, positive predictive value and negative predictive value of sepsis treatment of physicians with the predictions of models generated by ML algorithms.
resultsThe treatment sensitivity of all the nine ML algorithms and specificity of eight out of the nine ML algorithms tested exceeded that of the physician when culture-negative sepsis was included. When culture-negative sepsis was excluded both sensitivity and specificity exceeded that of the physician for all the ML algorithms. The top three predictive variables were the hematocrit or packed cell volume, chorioamnionitis and respiratory rate.
conclusionsPredictive models developed from off-the-shelf and EMR data using ML algorithms exceeded the treatment sensitivity and treatment specificity of clinicians. A prospective study is warranted to assess the clinical utility of the ML algorithms in improving the accuracy of antibiotic use in the management of neonatal sepsis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.