ArticleNPJ digital medicine2022
Discovery of signatures of fatal neonatal illness in vital signs using highly comparative time-series analysis.
Article in NPJ digital medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- The FAIRSCAPE AI-readiness Framework for Biomedical Research.bioRxiv : the preprint server for biology · 2026Article
- Continuous vital sign monitoring for predicting hospital length of stay: a feasibility study in chronic obstructive pulmonary disease and chronic heart failure patients.Scandinavian journal of trauma, resuscitation and emergency medicine · 2025Article
- Comparing machine learning techniques for neonatal mortality prediction: insights from a modeling competition.Pediatric research · 2025Article
- Heart rate patterns predicting cerebral palsy in preterm infants.Pediatric research · 2025Article
- Four-factor nomogram for early-onset sepsis in preterm neonates: Development and internal validation of a stewardship tool.PloS one · 2025Article
- Exploring Computational Techniques in Preprocessing Neonatal Physiological Signals for Detecting Adverse Outcomes: Scoping Review.Interactive journal of medical research · 2024Article
- Monitoring chaos at the cot-side.Pediatric research · 2024Article
- Highly comparative time series analysis of oxygen saturation and heart rate to predict respiratory outcomes in extremely preterm infants.Physiological measurement · 2024Observational
- Heart Rate and Pulse Oximetry Dynamics in the First Week after Birth in Neonatal Intensive Care Unit Patients and the Risk of Cerebral Palsy.American journal of perinatology · 2024Article
- The principles of whole-hospital predictive analytics monitoring for clinical medicine originated in the neonatal ICU.NPJ digital medicine · 2022Review
- Continuous ECG monitoring should be the heart of bedside AI-based predictive analytics monitoring for early detection of clinical deterioration.Journal of electrocardiologyReview
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
To seek new signatures of illness in heart rate and oxygen saturation vital signs from Neonatal Intensive Care Unit (NICU) patients, we implemented highly comparative time-series analysis to discover features of all-cause mortality in the next 7 days. We collected 0.5 Hz heart rate and oxygen saturation vital signs of infants in the University of Virginia NICU from 2009 to 2019. We applied 4998 algorithmic operations from 11 mathematical families to random daily 10 min segments from 5957 NICU infants, 205 of whom died. We clustered the results and selected a representative from each, and examined multivariable logistic regression models. 3555 operations were usable; 20 cluster medoids held more than 81% of the information, and a multivariable model had AUC 0.83. New algorithms outperformed others: moving threshold, successive increases, surprise, and random walk. We computed provenance of the computations and constructed a software library with links to the data. We conclude that highly comparative time-series analysis revealed new vital sign measures to identify NICU patients at the highest risk of death in the next week.
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Registered trials
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