In an increasingly data-driven world, artificial intelligence is expected to be a key tool for converting big data into tangible benefits and the healthcare domain is no exception to this. Machine learning aims to identify complex patterns in multi-d...
PURPOSE: The purpose of this study is to develop a machine learning algorithm to predict future intubation among patients diagnosed or suspected with COVID-19.
OBJECTIVE: To investigate the applicability of supervised machine learning (SML) to classify health-related webpages as 'reliable' or 'unreliable' in an automated way.
Neural networks : the official journal of the International Neural Network Society
Nov 10, 2020
Mixed sample augmentation (MSA) has witnessed great success in the research area of semi-supervised learning (SSL) and is performed by mixing two training samples as an augmentation strategy to effectively smooth the training space. Following the ins...
Journal of chemical information and modeling
Nov 8, 2020
This work considers strategies to develop accurate and reliable graph neural networks (GNNs) for molecular property predictions. Prediction performance of GNNs is highly sensitive to the change in various parameters due to the inherent challenges in ...
In recent years, wearable sensors have become common, with possible applications in biomechanical monitoring, sports and fitness training, rehabilitation, assistive devices, or human-computer interaction. Our goal was to achieve accurate kinematics e...
Time series gene expression data is widely used to study different dynamic biological processes. Although gene expression datasets share many of the characteristics of time series data from other domains, most of the analyses in this field do not ful...
Screening agrochemicals and pharmaceuticals for potential liver toxicity is required for regulatory approval and is an expensive and time-consuming process. The identification and utilization of early exposure gene signatures and robust predictive mo...
Deep learning analysis of images and text unfolds new horizons in medicine. However, analysis of transcriptomic data, the cause of biological and pathological changes, is hampered by structural complexity distinctive from images and text. Here we con...
Journal of chemical information and modeling
Nov 3, 2020
Semi-supervised learning has proved its efficacy in utilizing extensive unlabeled data to alleviate the use of a large amount of supervised data and improve model performance. Despite its tremendous potential, semi-supervised learning has yet to be i...