A body of healthcare AI research connects data infrastructure, machine learning, and natural language processing across medical applications.

An automated machine-learning program developed by researchers from Edith Cowan University (ECU) in conjunction with the University of Manitoba has been able to identify potential cardiovascular ...

Housing tends to be a key part of household wealth, but despite its importance, it has been difficult to measure the value of a property. In a new article, researchers have studied the impact of a ...

The CMS Collaboration has shown, for the first time, that machine learning can be used to fully reconstruct particle collisions at the LHC. This new approach can reconstruct collisions more quickly ...

Are Machine Learning (ML) algorithms superior to traditional econometric models for GDP nowcasting in a time series setting? Based on our evaluation of all models from both classes ever used in ...

This model was trained and tested on a 70%/30% split (train/test result cohort), achieving an area under the receiver operator curve on the test set of 0.866 (95% CI, 0.857 to 0.875). Assigning a ...

The workflow encompasses patient datacollection and screening, univariate regression analysis for initial variable selection, systematic comparison of 91 machine learning models,selection and ...

A machine learning algorithm used gene expression profiles of patients with gout to predict flares. The PyTorch neural network performed best, with an area under the curve of 65%. The PyTorch model ...