ArticlePhysiological measurement2017
Multiscale network representation of physiological time series for early prediction of sepsis.
Article in Physiological measurement, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 4 of them syntheses that pooled it.
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Who cites it
23 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Early detection of sepsis using machine learning algorithms: a systematic review and network meta-analysis.Frontiers in medicine · 2024Pooled it
- Diagnostic performance of machine-learning algorithms for sepsis prediction: An updated meta-analysis.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024Pooled it
- Early Prediction of Sepsis in the ICU Using Machine Learning: A Systematic Review.Frontiers in medicine · 2021Pooled it
- Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy.Intensive care medicine · 2020Pooled it
- Artificial Intelligence Enabled Diagnostics Using Photoplethysmography (PPG): Beyond SpOLife (Basel, Switzerland) · 2026Review
- Machine learning model for daily prediction of pediatric sepsis using Phoenix criteria.Pediatric research · 2026Article
- Trends and management of acute respiratory failure in hospitalized patients: a multicenter retrospective study in South Korea.Acute and critical care · 2025Article
- Development and validation of a screening tool for sepsis without laboratory results in the emergency department: a machine learning study.EClinicalMedicine · 2025Article
- Application of physiological network mapping in the prediction of survival in critically ill patients with acute liver failure.Scientific reports · 2024Article
- Machine learning-based prediction of clinical outcomes after traumatic brain injury: Hidden information of early physiological time series.CNS neuroscience & therapeutics · 2024Article
- Impact of a deep learning sepsis prediction model on quality of care and survival.NPJ digital medicine · 2024Article
- Machine Learning-Based Early Prediction of Sepsis Using Electronic Health Records: A Systematic Review.Journal of clinical medicine · 2023Review
- Rett syndrome severity estimation with the BioStamp nPoint using interactions between heart rate variability and body movement.PloS one · 2023Article
- The mechanistic and prognostic implications of heart rate variability analysis in patients with cirrhosis.Physiological reports · 2022Review
- Permutation Entropy as a Universal Disorder Criterion: How Disorders at Different Scale Levels Are Manifestations of the Same Underlying Principle.Entropy (Basel, Switzerland) · 2021Article
- Artificial intelligence sepsis prediction algorithm learns to say "I don't know".NPJ digital medicine · 2021Article
- DeepAISE - An interpretable and recurrent neural survival model for early prediction of sepsis.Artificial intelligence in medicine · 2021Article
- The New Field of Network Physiology: Building the Human Physiolome.Frontiers in network physiology · 2021Article
- Validation of a machine learning algorithm for early severe sepsis prediction: a retrospective study predicting severe sepsis up to 48 h in advance using a diverse dataset from 461 US hospitals.BMC medical informatics and decision making · 2020Article
- AIDEx - An Open-source Platform for Real-Time Forecasting Sepsis and A Case Study on Taking ML Algorithms to Production.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Objective and Approach: Sepsis, a dysregulated immune-mediated host response to infection, is the leading cause of morbidity and mortality in critically ill patients. Indices of heart rate variability and complexity (such as entropy) have been proposed as surrogate markers of neuro-immune system dysregulation with diseases such as sepsis. However, these indices only provide an average, one dimensional description of complex neuro-physiological interactions. We propose a novel multiscale network construction and analysis method for multivariate physiological time series, and demonstrate its utility for early prediction of sepsis. MAIN
resultsWe show that features derived from a multiscale heart rate and blood pressure time series network provide approximately 20% improvement in the area under the receiver operating characteristic (AUROC) for four-hour advance prediction of sepsis over traditional indices of heart rate entropy ([Formula: see text] versus [Formula: see text]). Our results indicate that this improvement is attributable to both the improved network construction method proposed here, as well as the information embedded in the higher order interaction of heart rate and blood pressure time series dynamics. Our final model, which included the most commonly available clinical measurements in patients' electronic medical records and multiscale entropy features, as well as the proposed network-based features, achieved an AUROC of [Formula: see text]. SIGNIFICANCE: Prediction of the onset of sepsis prior to clinical recognition will allow for meaningful earlier interventions (e.g. antibiotic and fluid administration), which have the potential to decrease sepsis-related morbidity, mortality and healthcare costs.
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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.