ArticleJAMIA open2022
Performance effectiveness of vital parameter combinations for early warning of sepsis-an exhaustive study using machine learning.
Article in JAMIA open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.
- Early detection of sepsis using machine learning algorithms: a systematic review and network meta-analysis.Frontiers in medicine · 2024Pooled it
- Explainable deep learning for early sepsis detection from ICU time-series data using XAI techniques.Scientific reports · 2026Article
- Diagnostic accuracy of PCT, IL-6, and MR-ProADM for early identification of sepsis in the emergency department.Revista espanola de quimioterapia : publicacion oficial de la Sociedad Espanola de Quimioterapia · 2026Observational
- Early sepsis prediction using a hybrid LSTM-GAT model: a study on the PhysioNet 2019 dataset.BMJ health & care informatics · 2026Article
- Machine Learning Model for Sepsis Prediction in Prolonged and Chronic Critical Illness: Development and Validation Using Retrospective Real-World ICU Data.Journal of clinical medicine · 2026Article
- Pediatric sepsis prediction: Human in the loop framework.PLOS digital health · 2025Article
- Streamlined machine learning model for early sepsis risk prediction in burn patients.NPJ digital medicine · 2025Article
- Monitoring Vital Signs With Continuous Monitoring After Major Gastrointestinal Surgical Procedures: The Patient, Nurse and Physician Perspective.Journal of evaluation in clinical practice · 2025Article
- An Easy and Quick Risk-Stratified Early Forewarning Model for Septic Shock in the Intensive Care Unit: Development, Validation, and Interpretation Study.Journal of medical Internet research · 2025Article
- Machine learning-based prediction of LDL cholesterol: performance evaluation and validation.PeerJ · 2025Article
- A scoping review of machine learning for sepsis prediction- feature engineering strategies and model performance: a step towards explainability.Critical care (London, England) · 2024Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
Objective: To carry out exhaustive data-driven computations for the performance of noninvasive vital signs heart rate (HR), respiratory rate (RR), peripheral oxygen saturation (SpO Materials and methods: By extracting features interpretable by clinicians, we applied Gradient Boosted Decision Tree machine learning on a dataset of 2630 patients to build 240 models. Validation was performed on a geographically distinct dataset. Relative to onset, predictions were clocked as per 16 pairs of monitoring intervals and prediction times, and the outcomes were ranked. Results: The combination of HR and Temp was found to be a minimal feature set yielding maximal predictability with area under receiver operating curve 0.94, sensitivity of 0.85, and specificity of 0.90. Whereas HR and RR each directly enhance prediction, the effects of SpO Conclusion: It can be concluded that using intensive care unit data even 2 vital signs are adequate to predict sepsis upto 6 h in advance with promising accuracy comparable to standard scoring methods and other sepsis predictive tools reported in literature. Vital-SEP can be used for fast-track prediction especially in limited resource hospital settings where laboratory based hematologic or biochemical assays may be unavailable, inaccurate, or entail clinically inordinate delays. A prospective study is essential to determine the clinical impact of the proposed sepsis prediction model and evaluate other outcomes such as mortality and duration of hospital stay.
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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.