Abstract: Recently, rule-based classification on multivariate time series (MTS) data has gained lots of attention, which could improve the interpretability of classification. However, state-of-the-art ...

aDepartment of Medicine, University of California San Francisco, San Francisco, CA, USA bDepartment of Medicine, University of California Los Angeles, Los Angeles, CA, USA cDepartment of Medicine, ...

Abstract: This article examines some of the most relevant algorithms for association rule mining in a medical context, within the framework of unsupervised Federated Learning (FL) in a simulated ...

An association rule learning algorithm was used to analyze the characteristics of co-occurring health care needs among Chinese residents, while a generalized linear model was used to examine the ...

Despite the poor outcomes related to the presence of pulmonary hypertension, it often goes undiagnosed in part because of low suspicion and screening tools not being easily accessible such as ...

Space complexity of machine learning algorithms is the amount of memory or storage an algorithm requires for its successful execution. This becomes one of the important metrics of concern since it ...

bDepartment of Cardiology, University Hospital Basel, University of Basel, Basel, Switzerland cDepartment of Cardiac Surgery, University Hospital Basel, University of Basel, Basel, Switzerland ...

The rule introduces requirements to ensure algorithms don’t contribute to health disparities or decrease health equity, ensure that clinical decision support tools include access to supporting ...

Spiking neural network (SNN) is considered to be the brain-like model that best conforms to the biological mechanism of the brain. Due to the non-differentiability of the spike, the training method of ...

Group A Streptococcus is a globally significant human pathogen. The extensive variability of the GAS genome, virulence phenotypes and clinical outcomes, render it an excellent candidate for the ...