ArticleCritical care (London, England)2024
Interpretable machine learning model for new-onset atrial fibrillation prediction in critically ill patients: a multi-center study.
Article in Critical care (London, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 92 papers, 1 of them a synthesis that pooled it.
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Who cites it
92 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Clinical subphenotypes in septic patients with new-onset atrial fibrillation: validation and parsimonious classifier model development.BMC medical informatics and decision making · 2026Article
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- Development of a machine learning-based prediction model for hypothyroidism-associated delirium in elderly hypothyroid patients in the intensive care unit.BMC geriatrics · 2026Article
- ScaHybNet: a scalogram-based hybrid ensemble network for ECG arrhythmia classification.Scientific reports · 2026Article
- Interpretable machine learning models for bladder cancer overall survival prediction development and external validation via SEER database and Chinese cohort analysis.Discover oncology · 2026Article
- Systematic evaluation of machine learning models for clinical risk prediction on real-world hospital datasets.iScience · 2026Article
- Adverse Pregnancy Outcomes in Women With Immune Abnormalities: Machine Learning Model Development and Validation Using First-Trimester Sonographic Features.JMIR medical informatics · 2026Article
- Interpretable machine learning for toluene degradation in biological trickling filters: prediction, parameter optimization.Bioprocess and biosystems engineering · 2026Article
- Explainable machine learning differentiates necrotizing fasciitis and osteomyelitis via routine blood biomarkers.NPJ digital medicine · 2026Article
- Development and external validation of a predictive model for in-hospital mortality in patients with liver cirrhosis and sepsis.Scientific reports · 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
32 more citing papers are in PubMed but not listed here.
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Abstract
backgroundNew-onset atrial fibrillation (NOAF) is the most common arrhythmia in critically ill patients admitted to intensive care and is associated with poor prognosis and disease burden. Identifying high-risk individuals early is crucial. This study aims to create and validate a NOAF prediction model for critically ill patients using machine learning (ML).
methodsThe data came from two non-overlapping datasets from the Medical Information Mart for Intensive Care (MIMIC), with MIMIC-IV used for training and subset of MIMIC-III used as external validation. LASSO regression was used for feature selection. Eight ML algorithms were employed to construct the prediction model. Model performance was evaluated based on identification, calibration, and clinical application. The SHapley Additive exPlanations (SHAP) method was used for visualizing model characteristics and individual case predictions.
resultsAmong 16,528 MIMIC-IV patients, 1520 (9.2%) developed AF post-ICU admission. A model with 23 variables was built, with XGBoost performing best, achieving an AUC of 0.891 (0.873-0.888) in validation and 0.769 (0.756-0.782) in external validation. Key predictors included age, mechanical ventilation, urine output, sepsis, blood urea nitrogen, percutaneous arterial oxygen saturation, continuous renal replacement therapy and weight. A risk probability greater than 0.6 was defined as high risk. A friendly user interface had been developed for clinician use.
conclusionWe developed a ML model to predict the risk of NOAF in critically ill patients without cardiac surgery and validated its potential as a clinically reliable tool. SHAP improves the interpretability of the model, enables clinicians to better understand the causes of NOAF, helps clinicians to prevent it in advance and improves patient outcomes.
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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.