Neural networks : the official journal of the International Neural Network Society
Feb 5, 2021
Despite the popularism of Bayesian neural networks (BNNs) in recent years, its use is somewhat limited in complex and big data situations due to the computational cost associated with full posterior evaluations. Variational Bayes (VB) provides a usef...
Neural networks : the official journal of the International Neural Network Society
Feb 5, 2021
Latent Dirichlet allocation (LDA) obtains essential information from data by using Bayesian inference. It is applied to knowledge discovery via dimension reducing and clustering in many fields. However, its generalization error had not been yet clari...
Previous research has established the variability of examiners in reaching suitability determinations for friction ridge comparisons. Attempts to create predictive models to assist in this determination have been made, but have been largely confined ...
Machine learning has greatly facilitated the analysis of medical data, while the internal operations usually remain intransparent. To better comprehend these opaque procedures, a convolutional neural network for optical coherence tomography image seg...
Patients with severe COVID-19 have overwhelmed healthcare systems worldwide. We hypothesized that machine learning (ML) models could be used to predict risks at different stages of management and thereby provide insights into drivers and prognostic m...
BACKGROUND: Clinical risk scores and machine learning models based on routine laboratory values could assist in automated early identification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) patients at risk for severe clinical outcom...
Risk assessment of in-hospital mortality of patients at the time of hospitalization is necessary for determining the scale of required medical resources for the patient depending on the patient's severity. Because recent machine learning application ...
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Feb 5, 2021
Human motion prediction, which aims at predicting future human skeletons given the past ones, is a typical sequence-to-sequence problem. Therefore, extensive efforts have been devoted to exploring different RNN-based encoder-decoder architectures. Ho...
INTRODUCTION: This study was designed to develop and evaluate machine learning algorithms for predicting seizure due to acute tramadol poisoning, identifying high-risk patients and facilitating appropriate clinical decision-making.
Pattern classification aims to establish a new approach in personalized treatment. The scope is to tailor treatment on individual characteristics during all phases of care including prevention, diagnosis, treatment, and clinical outcome. In psychotic...
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