biostudies-literatureGuan CThe S＆T Program of Hebei349https://www.ebi.ac.uk/biostudies/studies/S-EPMC11523862biostudies-literatureUnknown28(1)<h4>Background</h4>New-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).<h4>Methods</h4>The 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 SHCritical care (London, England)Interpretable machine learning model for new-onset atrial fibrillation prediction in critically ill patients: a multi-center study.PMC11523862No.22377728DYang XLiu LGong AYin CLu JGuan CGeng LZhao YXiao BfalseInterpretable machine learning model for new-onset atrial fibrillation prediction in critically ill patients: a multi-center study.<h4>Background</h4>New-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).<h4>Methods</h4>The 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 SH2024-01-01T00:00:00Z2024 Oct2026-07-15T17:47:29.549Z2025-04-06T15:29:55.644ZS-EPMC115238623947301310.1186/s13054-024-05138-0