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 ...

Version 8.0 has been released. Get it here or with Docker. This release adds the capability to use pre-trained scikit-learn, Keras or REST API based models with Qlik. More on this here. Qlik's ...

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 ...

In the 1980s, Andrew Barto and Rich Sutton were considered eccentric devotees to an elegant but ultimately doomed idea—having machines learn, as humans and animals do, from experience. Decades on, ...

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 ...

This is Part 1 of Embedded Bias, a series revealing how race-based clinical algorithms pervade medicine and why it's so difficult to change them. Pediatrician Alexandra Epee-Bounya had had enough. In ...

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 ...

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 ...

Deep learning has been highly successful in recent years and has led to dramatic improvements in multiple domains. Deep-learning algorithms often generalize quite well in practice, namely, given ...