ArticleJournal of translational medicine2025
Developing a rapid screening tool for high-risk ICU patients of sepsis: integrating electronic medical records with machine learning methods for mortality prediction in hospitalized patients-model establishment, internal and external validation, and visualization.
Article in Journal of translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prediction models for mortality in patients with sepsis: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- The prognostic value of CT-quantified visceral and subcutaneous fat areas in sepsis.World journal of emergency medicine · 2026Article
- Early prediction of sepsis in the ICU: a comparative analysis of multiple machine-learning algorithms using the MIMIC-III database.BMC medical informatics and decision making · 2026Article
- Development of an interpretable machine learning model for predicting new-onset atrial fibrillation in patients with sepsis-associated acute kidney injury: A retrospective cohort study.Science progress · 2026Article
- Artificial intelligence based predictive models for early sepsis detection in intensive care units: a scoping review.Frontiers in digital health · 2026Review
- Review
- Bug Wars: Artificial Intelligence Strikes Back in Sepsis Management.Diagnostics (Basel, Switzerland) · 2025Review
- Association between advanced lung cancer inflammation index and all-cause mortality in critically ill patients with sepsis: analysis of the MIMIC-IV database.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
objectivesTo develop a machine learning-based prediction model using clinical data from the first 24 h of ICU admission to enable rapid screening and early intervention for sepsis patients.
methodsThis multicenter retrospective cohort study analyzed electronic medical records of sepsis patients using machine learning methods. We evaluated model performance in predicting sepsis outcomes within the first 24 h of ICU admission across US and Chinese healthcare settings.
resultsFrom 31 clinical features, machine learning models demonstrated significantly better predictive performance than traditional approaches for sepsis outcomes. While linear regression achieved low test scores (0.25), machine learning methods reached scores of 0.78 and AUCs above 0.8 in testing. Importantly, these models maintained robust performance (scores 0.63-0.77) in external validation.
conclusionsThe application of machine learning-based prediction models for sepsis could significantly improve patient outcomes through early detection and timely intervention in the critical first 24 h of ICU admission, supporting clinical decision-making.
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Identifiers
What Socratic holds
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 Socratic graph.