AIMC Topic: Machine Learning

Clear Filters Showing 24391 to 24400 of 34417 articles

Machine vision analysis on abnormal respiratory conditions of mice inhaling particles containing cadmium.

Ecotoxicology and environmental safety
Inhalable environmental toxicants can induce pulmonary malfunction resulting abnormal respiratory conditions. The traditional methods currently available to detect the respiratory condition of animals rely on differential pressure transducers and sig...

Similarity-based machine learning support vector machine predictor of drug-drug interactions with improved accuracies.

Journal of clinical pharmacy and therapeutics
WHAT IS KNOWN AND OBJECTIVE: Drug-drug interactions (DDI) are frequent causes of adverse clinical drug reactions. Efforts have been directed at the early stage to achieve accurate identification of DDI for drug safety assessments, including the devel...

Deep learning in spiking neural networks.

Neural networks : the official journal of the International Neural Network Society
In recent years, deep learning has revolutionized the field of machine learning, for computer vision in particular. In this approach, a deep (multilayer) artificial neural network (ANN) is trained, most often in a supervised manner using backpropagat...

Spatiotemporal continuous estimates of PM concentrations in China, 2000-2016: A machine learning method with inputs from satellites, chemical transport model, and ground observations.

Environment international
Ambient exposure to fine particulate matter (PM) is known to harm public health in China. Satellite remote sensing measurements of aerosol optical depth (AOD) were statistically associated with in-situ observations after 2013 to predict PM concentrat...

Classification of needle-EMG resting potentials by machine learning.

Muscle & nerve
INTRODUCTION: The diagnostic importance of audio signal characteristics in needle electromyography (EMG) is well established. Given the recent advent of audio-sound identification by artificial intelligence, we hypothesized that the extraction of cha...

Practical Model Selection for Prospective Virtual Screening.

Journal of chemical information and modeling
Virtual (computational) high-throughput screening provides a strategy for prioritizing compounds for experimental screens, but the choice of virtual screening algorithm depends on the data set and evaluation strategy. We consider a wide range of liga...

Combining multimodal imaging and treatment features improves machine learning-based prognostic assessment in patients with glioblastoma multiforme.

Cancer medicine
BACKGROUND: For Glioblastoma (GBM), various prognostic nomograms have been proposed. This study aims to evaluate machine learning models to predict patients' overall survival (OS) and progression-free survival (PFS) on the basis of clinical, patholog...

A Dual Mode Adaptive Basal-Bolus Advisor Based on Reinforcement Learning.

IEEE journal of biomedical and health informatics
Self-monitoring of blood glucose (SMBG) and continuous glucose monitoring (CGM) are commonly used by type 1 diabetes (T1D) patients to measure glucose concentrations. The proposed adaptive basal-bolus algorithm (ABBA) supports inputs from either SMBG...

Quantitative ultrasound and machine learning for assessment of steatohepatitis in a rat model.

European radiology
OBJECTIVES: To develop a machine learning model based on quantitative ultrasound (QUS) parameters to improve classification of steatohepatitis with shear wave elastography in rats by using histopathology scoring as the reference standard.

On medical application of neural networks trained with various types of data.

Bioscience trends
Neural networks have garnered attention over the past few years. A neural network is a typical model of machine learning that is used to identify visual patterns. Neural networks are used to solve a wide variety of problems, including image recogniti...