Prediction of new-onset atrial fibrillation in sepsis patients by machine learning: A systematic review.

ABSTRACT:

Aims

New-onset atrial fibrillation (NOAF) occurs in approximately 23% of patients with sepsis and is independently associated with increased mortality. Therefore, early prediction of NOAF has significant clinical value. However, current artificial intelligence (AI) models predominantly rely on tabular data. These unimodal AI models face limitations in predicting NOAF as they fail to fully utilize the predictive potential arising from the interplay of multimodal data.

Methods

We reviewed current Machine Learning (ML) and Deep Learning (DL) approaches for atrial fibrillation (AF) prediction. It summarizes the selected features in ML models for predicting AF in ICU patients, and the advantages of time-window selection in DL models using electrocardiogram (ECG) signals. Notably,

SUBMITTER: Ye S 

PROVIDER: S-EPMC12508553 | biostudies-literature | 2025 Jan-Dec

REPOSITORIES: biostudies-literature