ArticleEuropean journal of medical research2022
Early predicting 30-day mortality in sepsis in MIMIC-III by an artificial neural networks model.
Article in European journal of medical research, 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, 26 citations in OpenAlex.
- Prediction models for mortality in patients with sepsis: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- Artificial Intelligence Models for Mortality and Outcome Prediction in Intensive Care Unit Sepsis: A Systematic Review.Journal of personalized medicine · 2026Review
- Diagnostic Codes in AI Prediction Models and Label Leakage of Same-Admission Clinical Outcomes.JAMA network open · 2025Article
- Inflammatory burden index as a predictor of mortality in septic patients: a retrospective study using the MIMIC-IV database.BMC infectious diseases · 2025Article
- Early sepsis mortality prediction model based on interpretable machine learning approach: development and validation study.Internal and emergency medicine · 2025Article
- TCKAN: a novel integrated network model for predicting mortality risk in sepsis patients.Medical & biological engineering & computing · 2025Article
- Prediction of sepsis mortality in ICU patients using machine learning methods.BMC medical informatics and decision making · 2024Article
- Prediction of 30-day mortality for ICU patients with Sepsis-3.BMC medical informatics and decision making · 2024Article
- LncRNA HOTTIP as a diagnostic biomarker for acute respiratory distress syndrome in patients with sepsis and to predict the short-term clinical outcome: a case-control study.BMC anesthesiology · 2024Article
- Harnessing artificial intelligence in sepsis care: advances in early detection, personalized treatment, and real-time monitoring.Frontiers in medicine · 2024Review
- Machine learning-based prediction of in-ICU mortality in pneumonia patients.Scientific reports · 2023Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveEarly identifying sepsis patients who had higher risk of poor prognosis was extremely important. The aim of this study was to develop an artificial neural networks (ANN) model for early predicting clinical outcomes in sepsis.
methodsThis study was a retrospective design. Sepsis patients from the Medical Information Mart for Intensive Care-III (MIMIC-III) database were enrolled. A predictive model for predicting 30-day morality in sepsis was performed based on the ANN approach.
resultsA total of 2874 patients with sepsis were included and 30-day mortality was 29.8%. The study population was categorized into the training set (n = 1698) and validation set (n = 1176) based on the ratio of 6:4. 11 variables which showed significant differences between survivor group and nonsurvivor group in training set were selected for constructing the ANN model. In training set, the predictive performance based on the area under the receiver-operating characteristic curve (AUC) were 0.873 for ANN model, 0.720 for logistic regression, 0.629 for APACHEII score and 0.619 for SOFA score. In validation set, the AUCs of ANN, logistic regression, APAHCEII score, and SOFA score were 0.811, 0.752, 0.607, and 0.628, respectively.
conclusionAn ANN model for predicting 30-day mortality in sepsis was performed. Our predictive model can be beneficial for early detection of patients with higher risk of poor prognosis.
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