Background and objectives Artificial intelligence (AI) has branched out to various
applications in healthcare, such as health services management, predictive medicine …

Feature attributions based on the Shapley value are popular for explaining machine
learning models. However, their estimation is complex from both theoretical and …

Abstract Background and Objective Artificial intelligence (AI) has several uses in the
healthcare industry, some of which include healthcare management, medical forecasting …

Medical time series are sequential data collected over time that measures health-related
signals, such as electroencephalography (EEG), electrocardiography (ECG), and intensive …

With the power of parallel processing, large datasets, and fast computational resources,
deep neural networks (DNNs) have outperformed highly trained and experienced human …

Anticipation of clinical decompensation is essential for effective emergency and critical care.
In this study, we develop a multimodal machine learning approach to predict the onset of …

Abstract Integrating Artificial Intelligence (AI) into healthcare has shown tremendous promise
in several domains, such as prediction, decision-making, and diagnosis. Nevertheless, the …

Electromyography (EMG) proves invaluable myoelectric manifestation in identifying
neuromuscular alterations resulting from ischemic strokes, serving as a potential marker for …

Background and Objective The aim of this narrative review is to analyze whether or not
artificial intelligence (AI) and its subsets are implemented in current clinical anesthetic …

Medical data refers to health-related information associated with regular patient care or as
part of a clinical trial program. There are many categories of such data, such as clinical …