ArticleFrontiers in medicine2021
Early Prediction of Mortality, Severity, and Length of Stay in the Intensive Care Unit of Sepsis Patients Based on Sepsis 3.0 by Machine Learning Models.
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Article in Frontiers in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 7 of them syntheses that pooled it.
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35citing papers in PubMed, 7 pooled it
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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.
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Who cites it
35 citing papers in PubMed, 7 syntheses or guidelines pooled it.
- Prediction models for mortality in patients with sepsis: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- Sepsis mortality prediction using machine learning and deep learning - a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Artificial Intelligence-Based Predictive Modeling for Early Detection of Sepsis in Hospitalized Patients: A Systematic Review and Meta-Analysis.Critical care explorations · 2025Pooled it
- Fluid balance and mortality in adult ICU patients with sepsis or septic shock: a systematic review and meta-analysis of observational studies.BMC anesthesiology · 2025Pooled it
- Global trends of big data analytics in health research: a bibliometric study.Frontiers in medicine · 2025Pooled it
- Machine learning for the prediction of sepsis-related death: a systematic review and meta-analysis.BMC medical informatics and decision making · 2023Pooled it
- Predicting sepsis onset in ICU using machine learning models: a systematic review and meta-analysis.BMC infectious diseases · 2023Pooled it
- Artificial Intelligence Models for Mortality and Outcome Prediction in Intensive Care Unit Sepsis: A Systematic Review.Journal of personalized medicine · 2026Review
- Machine Learning for Intensive Care Unit Length-of-Stay Prediction: A Simulation-Based Approach to Bed Capacity Management.Medical decision making : an international journal of the Society for Medical Decision Making · 2026Article
- Prediction of death in burn patients infected with antibiotic-resistant Staphylococcus aureus using machine learning based techniques.BMC infectious diseases · 2026Article
- Timing matters: a machine learning-driven comparison of community and hospital-onset sepsis.BMC medical informatics and decision making · 2026Article
- Association between clinical characteristics within 6 h of ICU admission and 30-day mortality risk in immunocompromised sepsis patients: development and validation of a machine learning model based on the MIMIC-IV database.Frontiers in digital health · 2026Article
- Progress in sepsis prediction models: from traditional scoring systems to multimodal intelligence and clinical translation.Frontiers in medicine · 2026Review
- Improving Sepsis Mortality Prediction With Machine Learning Using Full Region Synthetic Sampling Approach.Health science reports · 2025Article
- Intelligent Prediction Platform for Sepsis Risk Based on Real-Time Dynamic Temporal Features: Design Study.JMIR medical informatics · 2025Article
- Predicting ICU Mortality Among Septic Patients Using Machine Learning Technique.Journal of clinical medicine · 2025Article
- Development of an emergency department length-of-stay prediction model based on machine learning.World journal of emergency medicine · 2025Article
- Predictive model for daily risk alerts in sepsis patients in the ICU: visualization and clinical analysis of risk indicators.Precision clinical medicine · 2025Article
- Impact of trauma level designation on mortality in trauma patients with sepsis: an observational study across US trauma centers.Frontiers in medicine · 2025Article
- Early prediction of mortality upon intensive care unit admission.BMC medical informatics and decision making · 2024Article
Corrections and comments
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Authors and funding
13 authors.
Longxiang SuDepartment of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China.
Zheng XuDigital Health China Technologies Co., Ltd., Beijing, China.
Fengxiang ChangDigital Health China Technologies Co., Ltd., Beijing, China.
Yingying MaDigital Health China Technologies Co., Ltd., Beijing, China.
Shengjun LiuDepartment of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China.
Huizhen JiangDepartment of Information Center, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China.
Hao WangDepartment of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China.
Dongkai LiDepartment of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China.
Huan ChenDepartment of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China.
Xiang ZhouDepartment of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China.
Na HongDigital Health China Technologies Co., Ltd., Beijing, China.
Weiguo ZhuDepartment of Information Center, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China.
Yun LongDepartment of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China.
Funding
No grant is acknowledged in the PubMed record.
Abstract
PubMed holds no abstract for this paper.
Indexed as
machine learningoutcomepredictionsepsissequential (sepsis-related) organ failure assessment
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Registered trials
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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.