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

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, we compared these models in terms of feature selection, prediction horizons, and performance when applied to tabular data and ECG signal features. To enhance the predictive capability of ML for NOAF in patients with sepsis, we drew inspiration from multimodal models developed for other diseases, such as Alzheimer's disease, and proposed integrating tabular data and ECG signal data within a multimodal framework. Results: This study systematically analyzed the application of ML and DL in AF prediction. After screening, 12 studies (6 ML, 6 DL) were included. ML models, based on electronic medical records (EMR) or ECG features, achieved prediction windows ranging from minutes to hours with AUCs of 0.74-0.90. DL models processing raw ECG signals extended prediction windows to days, achieving AUCs of 0.74-0.96, with performance improving with larger datasets. A Transformer-based multimodal model (integrating clinical data and ECG) was proposed to enhance AF prediction in sepsis patients, though further validation is needed for cross-modal data fusion feasibility. Conclusions: Transitioning from unimodal predictive models to multimodal frameworks that combine tabular clinical data and raw ECG signals is feasible within the current deep-learning framework. This approach has the potential to significantly improve the early prediction capabilities of NOAF in sepsis patients.