ArticleScience translational medicine2023
Data-driven longitudinal characterization of neonatal health and morbidity.
Article in Science translational medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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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
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Who cites it
28 citing papers in PubMed, 33 citations in OpenAlex.
- Longitudinal alignments and syntheses of multimodal clinical data for personalized medicine with the PULSE framework.Nature computational science · 2026Article
- A lesson from the past and the future of artificial intelligence in medical decision-making.Journal of perinatology : official journal of the California Perinatal Association · 2026Review
- Trajectory Analysis of Pediatric Longitudinal Allergen Sensitization Patterns From Real World Data.Allergy, asthma & immunology research · 2026Article
- Neurodevelopmental Outcome in Very Low Birth Weight Preterm Infants: An Exploratory Multivariable Analysis Including Sonographic Brain Volume Trajectories-Data from the NeoNEVS Project.Children (Basel, Switzerland) · 2026Article
- [Research progress in artificial intelligence for the diagnosis and management of diseases in preterm infants].Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2026Review
- Machine learning-based prognostic modeling of early clinical outcomes in very low birth weight infants: insights from a nationwide cohort on delivery room resuscitation, prematurity-related complications, and mortality.BMC medical informatics and decision making · 2026Article
- Leveraging health records to identify diagnoses associated with recurrent pregnancy loss across two medical centers.iScience · 2026Article
- Development and validation of a pre-trained language model for neonatal morbidities: a retrospective, multicentre, prognostic study.The Lancet. Digital health · 2025Article
- Leveraging electronic health records from two hospital systems identifies male infertility-associated comorbidities across time.Communications medicine · 2025Article
- Towards a new taxonomy of preterm birth.Journal of perinatology : official journal of the California Perinatal Association · 2025Review
- Review
- AI-guided precision parenteral nutrition for neonatal intensive care units.Nature medicine · 2025Article
- Early prediction of mortality and morbidities in VLBW preterm neonates using machine learning.Pediatric research · 2025Article
- Mitigation of outcome conflation in predicting patient outcomes using electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Review
- A machine learning approach to leveraging electronic health records for enhanced omics analysis.Nature machine intelligence · 2025Article
- Cross-modal contrastive learning for unified placenta analysis using photographs.Patterns (New York, N.Y.) · 2024Article
- Artificial Intelligence in Perioperative Care: Opportunities and Challenges.Anesthesiology · 2024Article
- Large-scale proteomics in the first trimester of pregnancy predict psychopathology and temperament in preschool children: an exploratory study.Journal of child psychology and psychiatry, and allied disciplines · 2024Article
- Reassessing acquired neonatal intestinal diseases using unsupervised machine learning.Pediatric research · 2024Article
Corrections and comments
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Authors and funding
33 authors at 3 institutions in 1 country.
Funding
Abstract
Although prematurity is the single largest cause of death in children under 5 years of age, the current definition of prematurity, based on gestational age, lacks the precision needed for guiding care decisions. Here, we propose a longitudinal risk assessment for adverse neonatal outcomes in newborns based on a deep learning model that uses electronic health records (EHRs) to predict a wide range of outcomes over a period starting shortly before conception and ending months after birth. By linking the EHRs of the Lucile Packard Children's Hospital and the Stanford Healthcare Adult Hospital, we developed a cohort of 22,104 mother-newborn dyads delivered between 2014 and 2018. Maternal and newborn EHRs were extracted and used to train a multi-input multitask deep learning model, featuring a long short-term memory neural network, to predict 24 different neonatal outcomes. An additional cohort of 10,250 mother-newborn dyads delivered at the same Stanford Hospitals from 2019 to September 2020 was used to validate the model. Areas under the receiver operating characteristic curve at delivery exceeded 0.9 for 10 of the 24 neonatal outcomes considered and were between 0.8 and 0.9 for 7 additional outcomes. Moreover, comprehensive association analysis identified multiple known associations between various maternal and neonatal features and specific neonatal outcomes. This study used linked EHRs from more than 30,000 mother-newborn dyads and would serve as a resource for the investigation and prediction of neonatal outcomes. An interactive website is available for independent investigators to leverage this unique dataset: https://maternal-child-health-associations.shinyapps.io/shiny_app/.
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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.