Observational studyMedicine2019
Heart rate variability based machine learning models for risk prediction of suspected sepsis patients in the emergency department.
Observational study in Medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 4 of them syntheses that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
45 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Revolutionizing sepsis diagnosis using machine learning and deep learning models: a systematic literature review.BMC infectious diseases · 2025Pooled it
- Influence of artificial intelligence on the work design of emergency department clinicians a systematic literature review.BMC health services research · 2022Pooled it
- Performance of universal early warning scores in different patient subgroups and clinical settings: a systematic review.BMJ open · 2021Pooled it
- National Early Warning Score Does Not Accurately Predict Mortality for Patients With Infection Outside the Intensive Care Unit: A Systematic Review and Meta-Analysis.Frontiers in medicine · 2021Pooled it
- Machine learning for predicting the risk stratification of 1-5 cm gastric gastrointestinal stromal tumors based on CT.BMC medical imaging · 2023Trial
- A comparison of machine learning models versus clinical evaluation for mortality prediction in patients with sepsis.PloS one · 2021Trial
- Challenges in early detection and prognostication of sepsis: new approaches from the emergency department and intensive care unit.EClinicalMedicine · 2026Review
- Sepsis insult generates different vascular response phenotypes: an unsupervised time-series clustering preclinical study.Scientific reports · 2026Article
- Progress in sepsis prediction models: from traditional scoring systems to multimodal intelligence and clinical translation.Frontiers in medicine · 2026Review
- Article
- A Heart Rate Variability-Derived Decision Support Tool for Prognostication in Emergency Department Patients With Suspected Infection.BioMed research international · 2026Observational
- Analyzing Heart Rate Variability for COVID-19 ICU Mortality Prediction Using Continuous Signal Processing Techniques.Journal of clinical medicine · 2025Article
- Designing Health Care Provider-Centered Emergency Department Interventions: Participatory Design Study.JMIR formative research · 2025Article
- Artificial Intelligence in Emergency Medicine: A Primer for the Nonexpert.Journal of the American College of Emergency Physicians open · 2025Article
- The role of artificial intelligence in sepsis in the Emergency Department: a narrative review.Annals of translational medicine · 2025Review
- Genetic Algorithms for Feature Selection in the Classification of COVID-19 Patients.Bioengineering (Basel, Switzerland) · 2024Article
- Heart rate variability in acute pancreatitis: a narrative review.Translational gastroenterology and hepatology · 2024Review
- Predicting synkinesis caused by Bell's palsy or Ramsay Hunt syndrome using machine learning-based logistic regression.Laryngoscope investigative otolaryngology · 2023Article
- Article
- Dynamics of ventilatory pattern variability and Cardioventilatory Coupling during systemic inflammation in rats.Frontiers in network physiology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Early identification of high-risk septic patients in the emergency department (ED) may guide appropriate management and disposition, thereby improving outcomes. We compared the performance of machine learning models against conventional risk stratification tools, namely the Quick Sequential Organ Failure Assessment (qSOFA), National Early Warning Score (NEWS), Modified Early Warning Score (MEWS), and our previously described Singapore ED Sepsis (SEDS) model, in the prediction of 30-day in-hospital mortality (IHM) among suspected sepsis patients in the ED.Adult patients who presented to Singapore General Hospital (SGH) ED between September 2014 and April 2016, and who met ≥2 of the 4 Systemic Inflammatory Response Syndrome (SIRS) criteria were included. Patient demographics, vital signs and heart rate variability (HRV) measures obtained at triage were used as predictors. Baseline models were created using qSOFA, NEWS, MEWS, and SEDS scores. Candidate models were trained using k-nearest neighbors, random forest, adaptive boosting, gradient boosting and support vector machine. Models were evaluated on F1 score and area under the precision-recall curve (AUPRC).A total of 214 patients were included, of whom 40 (18.7%) met the outcome. Gradient boosting was the best model with a F1 score of 0.50 and AUPRC of 0.35, and performed better than all the baseline comparators (SEDS, F1 0.40, AUPRC 0.22; qSOFA, F1 0.32, AUPRC 0.21; NEWS, F1 0.38, AUPRC 0.28; MEWS, F1 0.30, AUPRC 0.25).A machine learning model can be used to improve prediction of 30-day IHM among suspected sepsis patients in the ED compared to traditional risk stratification tools.
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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.