ArticleProceedings of the ACM Conference on Health, Inference, and Learning2020
Multiple Instance Learning for Predicting Necrotizing Enterocolitis in Premature Infants Using Microbiome Data.
Article in Proceedings of the ACM Conference on Health, Inference, and Learning, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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
16 citing papers in PubMed, 16 citations in OpenAlex.
- Compact Gut Microbial Dysbiosis Signature Associated with Necrotizing Enterocolitis and Altered Early Microbiota Development.Pathogens (Basel, Switzerland) · 2026Article
- Biomarkers for Necrotising Enterocolitis-Are We There Yet?Children (Basel, Switzerland) · 2026Review
- Machine learning demonstrates normal fetal Doppler velocimetry associated with reduced risk of necrotizing enterocolitis among preterm infants with growth restriction.Journal of neonatal-perinatal medicine · 2026Article
- Early-life microbiome trajectories as biomarkers to predict health outcomes.Microbiome research reports · 2026Review
- [Recent advances in artificial intelligence for auxiliary diagnosis and management of neonatal necrotizing enterocolitis].Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2025Review
- Evaluation of radiomics as an assistant tool for radiologists in the diagnosis of necrotizing enterocolitis.Translational pediatrics · 2025Article
- Advances in Artificial Intelligence and Machine Learning for Precision Medicine in Necrotizing Enterocolitis and Neonatal Sepsis: A State-of-the-Art Review.Children (Basel, Switzerland) · 2025Review
- Using Vital Signs for the Early Prediction of Necrotizing Enterocolitis in Preterm Neonates with Machine Learning.Children (Basel, Switzerland) · 2024Article
- Artificial intelligence to classify acquired intestinal injury in preterm neonates-a new perspective.Pediatric research · 2024Article
- A digital twin of the infant microbiome to predict neurodevelopmental deficits.Science advances · 2024Article
- Role of the microbiome in pathophysiology of necrotising enterocolitis in preterm neonates.BMJ paediatrics open · 2023Review
- Biomarkers of necrotizing enterocolitis in the era of machine learning and omics.Seminars in perinatology · 2023Review
- State of the art review on machine learning and artificial intelligence in the study of neonatal necrotizing enterocolitis.Frontiers in pediatrics · 2023Review
- Multiomics, artificial intelligence, and precision medicine in perinatology.Pediatric research · 2023Review
- Proteomics Profiling of Stool Samples from Preterm Neonates with SWATH/DIA Mass Spectrometry for Predicting Necrotizing Enterocolitis.International journal of molecular sciences · 2022Article
- Interpretable prediction of necrotizing enterocolitis from machine learning analysis of premature infant stool microbiota.BMC bioinformatics · 2022Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Necrotizing enterocolitis (NEC) is a life-threatening intestinal disease that primarily affects preterm infants during their first weeks after birth. Mortality rates associated with NEC are 15-30%, and surviving infants are susceptible to multiple serious, long-term complications. The disease is sporadic and, with currently available tools, unpredictable. We are creating an early warning system that uses stool microbiome features, combined with clinical and demographic information, to identify infants at high risk of developing NEC. Our approach uses a multiple instance learning, neural network-based system that could be used to generate daily or weekly NEC predictions for premature infants. The approach was selected to effectively utilize sparse and weakly annotated datasets characteristic of stool microbiome analysis. Here we describe initial validation of our system, using clinical and microbiome data from a nested case-control study of 161 preterm infants. We show receiver-operator curve areas above 0.9, with 75% of dominant predictive samples for NEC-affected infants identified at least 24 hours prior to disease onset. Our results pave the way for development of a real-time early warning system for NEC using a limited set of basic clinical and demographic details combined with stool microbiome data.
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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.