Research graph
References from Interpretable admission-stage machine learning for early Sepsis-3 identification in the emergency department. Local targets link to admitted publications; unresolved targets remain external evidence.
Global, regional, and national sepsis incidence and mortality, 1990-2017: analysis for the global burden of disease study
10.1016/s0140-6736(19)32989-7 · 2020 · External reference
Time to treatment and mortality during mandated emergency care for sepsis
10.1056/nejmoa1703058 · 2017 · External reference
Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021
10.1007/s00134-021-06506-y · 2021 · External reference
The third international consensus definitions for sepsis and septic shock (sepsis-3)
10.1001/jama.2016.0287 · 2016 · External reference
Assessment of clinical criteria for sepsis: for the third international consensus definitions for sepsis and septic shock (sepsis-3)
10.1001/jama.2016.0288 · 2016 · External reference
Sepsis biomarkers: a review
10.1186/cc8872 · 2010 · External reference
Procalcitonin as a diagnostic marker for sepsis: a systematic review and meta-analysis
10.1016/s1473-3099(12)70323-7 · 2013 · External reference
Diagnostic accuracy of procalcitonin, neutrophil-lymphocyte count ratio, C-reactive protein, and lactate in patients with suspected bacterial sepsis
10.1371/journal.pone.0181704 · 2017 · External reference
Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy
10.1007/s00134-019-05872-y · 2020 · External reference
Development and evaluation of a machine learning model for the early identification of patients at risk for sepsis
10.1016/j.annemergmed.2018.11.036 · 2019 · External reference
Machine learning model to identify sepsis patients in the emergency department: algorithm development and validation
10.3390/jpm11111055 · 2021 · External reference
Creating an automated trigger for sepsis clinical decision support at emergency department triage using machine learning
10.1371/journal.pone.0174708 · 2017 · External reference
Sepsis prediction at emergency department triage using natural language processing: retrospective cohort study
10.2196/49784 · 2024 · External reference
Intelligible models for healthcare: predicting pneumonia risk and hospital 30-day readmission
2015 · External reference
A unified approach to interpreting model predictions
2017 · External reference
Assessing the performance of prediction models: a framework for traditional and novel measures
10.1097/ede.0b013e3181c30fb2 · 2010 · External reference
Calibration: the Achilles heel of predictive analytics
10.1186/s12916-019-1466-7 · 2019 · External reference
Decision curve analysis: a novel method for evaluating prediction models
10.1177/0272989x06295361 · 2006 · External reference
Tripod + AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods
10.1136/bmj-2023-078378 · 2024 · External reference
Accuracy is not enough: explainable boosting machine model and identification of candidate biomarkers for real-time sepsis risk assessment in the emergency department
10.1186/s12873-025-01402-w · 2025 · External reference
Regression shrinkage and selection via the lasso
10.1111/j.2517-6161.1996.tb02080.x · 1996 · External reference
Unresolved reference
External reference
Interpretable machine learning for predicting sepsis risk in emergency triage patients
10.1038/s41598-025-85121-z · 2025 · External reference
An interpretable machine learning model for accurate prediction of sepsis in the ICU
10.1097/ccm.0000000000002936 · 2018 · External reference
An explainable artificial intelligence predictor for early detection of sepsis
10.1097/ccm.0000000000004550 · 2020 · External reference
An interpretable machine learning model for real-time sepsis prediction based on basic physiological indicators
10.26355/eurrev_202305_32439 · 2023 · External reference
Early prediction of sepsis in the ICU using machine learning: a systematic review
10.3389/fmed.2021.607952 · 2021 · External reference
Progress in sepsis prediction models: from traditional scoring systems to multimodal intelligence and clinical translation
10.3389/fmed.2026.1732164 · 2026 · External reference
Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement
10.7326/m14-0697 · 2015 · External reference
Diagnostic accuracy of procalcitonin, neutrophil-lymphocyte count ratio, C-reactive protein, and lactate in patients with suspected bacterial sepsis
10.1371/journal.pone.0181704 · 2017 · External reference
The third international consensus definitions for sepsis and septic shock (sepsis-3)
10.1001/jama.2016.0287 · ExternalCitation · doi-reference
Assessment of clinical criteria for sepsis: for the third international consensus definitions for sepsis and septic shock (sepsis-3)
10.1001/jama.2016.0288 · ExternalCitation · doi-reference
Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy
10.1007/s00134-019-05872-y · ExternalCitation · doi-reference
Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021
10.1007/s00134-021-06506-y · ExternalCitation · doi-reference
Development and evaluation of a machine learning model for the early identification of patients at risk for sepsis
10.1016/j.annemergmed.2018.11.036 · ExternalCitation · doi-reference
Global, regional, and national sepsis incidence and mortality, 1990-2017: analysis for the global burden of disease study
10.1016/s0140-6736(19)32989-7 · ExternalCitation · doi-reference
Procalcitonin as a diagnostic marker for sepsis: a systematic review and meta-analysis
10.1016/s1473-3099(12)70323-7 · ExternalCitation · doi-reference
Interpretable machine learning for predicting sepsis risk in emergency triage patients
10.1038/s41598-025-85121-z · ExternalCitation · doi-reference
Time to treatment and mortality during mandated emergency care for sepsis
10.1056/nejmoa1703058 · ExternalCitation · doi-reference
An interpretable machine learning model for accurate prediction of sepsis in the ICU
10.1097/ccm.0000000000002936 · ExternalCitation · doi-reference
An explainable artificial intelligence predictor for early detection of sepsis
10.1097/ccm.0000000000004550 · ExternalCitation · doi-reference
Assessing the performance of prediction models: a framework for traditional and novel measures
10.1097/ede.0b013e3181c30fb2 · ExternalCitation · doi-reference
Regression shrinkage and selection via the lasso
10.1111/j.2517-6161.1996.tb02080.x · ExternalCitation · doi-reference
Tripod + AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods
10.1136/bmj-2023-078378 · ExternalCitation · doi-reference
Decision curve analysis: a novel method for evaluating prediction models
10.1177/0272989x06295361 · ExternalCitation · doi-reference
Sepsis biomarkers: a review
10.1186/cc8872 · ExternalCitation · doi-reference
Accuracy is not enough: explainable boosting machine model and identification of candidate biomarkers for real-time sepsis risk assessment in the emergency department
10.1186/s12873-025-01402-w · ExternalCitation · doi-reference
Calibration: the Achilles heel of predictive analytics
10.1186/s12916-019-1466-7 · ExternalCitation · doi-reference
Creating an automated trigger for sepsis clinical decision support at emergency department triage using machine learning
10.1371/journal.pone.0174708 · ExternalCitation · doi-reference
Diagnostic accuracy of procalcitonin, neutrophil-lymphocyte count ratio, C-reactive protein, and lactate in patients with suspected bacterial sepsis
10.1371/journal.pone.0181704 · ExternalCitation · doi-reference
Sepsis prediction at emergency department triage using natural language processing: retrospective cohort study
10.2196/49784 · ExternalCitation · doi-reference
An interpretable machine learning model for real-time sepsis prediction based on basic physiological indicators
10.26355/eurrev_202305_32439 · ExternalCitation · doi-reference
Early prediction of sepsis in the ICU using machine learning: a systematic review
10.3389/fmed.2021.607952 · ExternalCitation · doi-reference
Progress in sepsis prediction models: from traditional scoring systems to multimodal intelligence and clinical translation
10.3389/fmed.2026.1732164 · ExternalCitation · doi-reference
Machine learning model to identify sepsis patients in the emergency department: algorithm development and validation
10.3390/jpm11111055 · ExternalCitation · doi-reference
Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement
10.7326/m14-0697 · ExternalCitation · doi-reference
