Self-attention with temporal prior: can we learn more from the arrow of time?

Many diverse phenomena in nature often inherently encode both short- and long-term temporal dependencies, which especially result from the direction of the flow of time. In this respect, we discovered experimental evidence suggesting that of these events are higher for closer time stamps. However, to be able for attention-based models to learn these regularities in short-term dependencies, it requires large amounts of data, which are often infeasible.

Self-Supervised Learning with Electrocardiogram Delineation for Arrhythmia Detection.

Electrocardiogram (ECG) signals convey immense information that, when properly processed, can be used to diagnose various health conditions including arrhythmia and heart failure. Deep learning algorithms have been successfully applied to medical diagnosis, but existing methods heavily rely on abundant high-quality annotations which are expensive. Self-supervised learning (SSL) circumvents this annotation cost by pre-training deep neural networks (DNNs) on auxiliary tasks that do not require manual annotation.

Automated Detection of Acute Myocardial Infarction Using Asynchronous Electrocardiogram Signals-Preview of Implementing Artificial Intelligence With Multichannel Electrocardiographs Obtained From Smartwatches: Retrospective Study.

Background: When using a smartwatch to obtain electrocardiogram (ECG) signals from multiple leads, the device has to be placed on different parts of the body sequentially. The ECG signals measured from different leads are asynchronous. Artificial intelligence (AI) models for asynchronous ECG signals have barely been explored.

Artificial Intelligence Algorithm for Screening Heart Failure with Reduced Ejection Fraction Using Electrocardiography.

Although heart failure with reduced ejection fraction (HFrEF) is a common clinical syndrome and can be modified by the administration of appropriate medical therapy, there is no adequate tool available to perform reliable, economical, early-stage screening. To meet this need, we developed an interpretable artificial intelligence (AI) algorithm for HFrEF screening using electrocardiography (ECG) and validated its performance. This retrospective cohort study included two hospitals.

Graph Convolutional Networks-Based Noisy Data Imputation in Electronic Health Record.

Objectives: A deep learning-based early warning system is proposed to predict sepsis prior to its onset.

Design: A novel algorithm was devised to detect sepsis 6 hours prior to its onset based on electronic medical records.

Setting: Retrospective cohorts from three separate hospitals are used in this study.