ArticleJournal of clinical medicine2020
Development and Validation of a Quick Sepsis-Related Organ Failure Assessment-Based Machine-Learning Model for Mortality Prediction in Patients with Suspected Infection in the Emergency Department.
Article in Journal of clinical medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 3 of them syntheses that pooled it.
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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.
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Who cites it
17 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Evaluating Artificial Intelligence for Sepsis Prediction in Emergency Departments: A Systematic Review and Meta Analysis.Journal of medical systems · 2026Pooled it
- Sepsis mortality prediction using machine learning and deep learning - a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Machine learning techniques for mortality prediction in emergency departments: a systematic review.BMJ open · 2021Pooled it
- Mortality prediction by quick Sequential Organ Failure Assessment, National Early Warning Score 2, Shock Index, and Modified Shock Index in suspected sepsis: A retrospective study.The Journal of international medical research · 2026Observational
- The role of artificial intelligence in sepsis in the Emergency Department: a narrative review.Annals of translational medicine · 2025Review
- Investigating computational models for diagnosis and prognosis of sepsis based on clinical parameters: Opportunities, challenges, and future research directions.Journal of intensive medicine · 2024Review
- A comparative study of explainable ensemble learning and logistic regression for predicting in-hospital mortality in the emergency department.Scientific reports · 2024Article
- Article
- A machine learning approach using endpoint adjudication committee labels for the identification of sepsis predictors at the emergency department.BMC emergency medicine · 2022Article
- Application of Machine Learning Approaches to Predict Postnatal Growth Failure in Very Low Birth Weight Infants.Yonsei medical journal · 2022Article
- Predicting Sepsis Mortality in a Population-Based National Database: Machine Learning Approach.Journal of medical Internet research · 2022Article
- Micro- and nanosensors for detecting blood pathogens and biomarkers at different points of sepsis care.Mikrochimica acta · 2022Review
- Evaluating machine learning models for sepsis prediction: A systematic review of methodologies.iScience · 2022Article
- Interpretable Machine Learning to Optimize Early In-Hospital Mortality Prediction for Elderly Patients with Sepsis: A Discovery Study.Computational and mathematical methods in medicine · 2022Article
- Can Prehospital Data Improve Early Identification of Sepsis in Emergency Department? An Integrative Review of Machine Learning Approaches.Applied clinical informatics · 2022Review
- Development and validation of a difficult laryngoscopy prediction model using machine learning of neck circumference and thyromental height.BMC anesthesiology · 2021Article
- COVID-19: What Should the General Practitioner Know?Clinical interventions in aging · 2021Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
The quick sepsis-related organ failure assessment (qSOFA) score has been introduced to predict the likelihood of organ dysfunction in patients with suspected infection. We hypothesized that machine-learning models using qSOFA variables for predicting three-day mortality would provide better accuracy than the qSOFA score in the emergency department (ED). Between January 2016 and December 2018, the medical records of patients aged over 18 years with suspected infection were retrospectively obtained from four EDs in Korea. Data from three hospitals (
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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.