AIMC Topic: Machine Learning

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Human-Machine Interface: Multiclass Classification by Machine Learning on 1D EOG Signals for the Control of an Omnidirectional Robot.

Sensors (Basel, Switzerland)
People with severe disabilities require assistance to perform their routine activities; a Human-Machine Interface (HMI) will allow them to activate devices that respond according to their needs. In this work, an HMI based on electrooculography (EOG) ...

Machine learning-based approach for disease severity classification of carpal tunnel syndrome.

Scientific reports
Identifying the severity of carpal tunnel syndrome (CTS) is essential to providing appropriate therapeutic interventions. We developed and validated machine-learning (ML) models for classifying CTS severity. Here, 1037 CTS hands with 11 variables eac...

Exploring the diagnostic effectiveness for myocardial ischaemia based on CCTA myocardial texture features.

BMC cardiovascular disorders
BACKGROUND: To explore the characteristics of myocardial textures on coronary computed tomography angiography (CCTA) images in patients with coronary atherosclerotic heart disease, a classification model was established, and the diagnostic effectiven...

Predicting mortality among patients with liver cirrhosis in electronic health records with machine learning.

PloS one
OBJECTIVE: Liver cirrhosis is a leading cause of death and effects millions of people in the United States. Early mortality prediction among patients with cirrhosis might give healthcare providers more opportunity to effectively treat the condition. ...

Sociodemographic risk factors of under-five stunting in Bangladesh: Assessing the role of interactions using a machine learning method.

PloS one
This paper aims to demonstrate the importance of studying interactions among various sociodemographic risk factors of childhood stunting in Bangladesh with the help of an interpretable machine learning method. Data used for the analyses are extracted...

Prediction of N chemical shifts by machine learning.

Magnetic resonance in chemistry : MRC
We demonstrate the potential for machine learning systems to predict three-dimensional (3D)-relevant NMR properties beyond traditional H- and C-based data, with comparable accuracy to density functional theory (DFT) (but orders of magnitude faster)...

Machine Learning for the Study of Plankton and Marine Snow from Images.

Annual review of marine science
Quantitative imaging instruments produce a large number of images of plankton and marine snow, acquired in a controlled manner, from which the visual characteristics of individual objects and their in situ concentrations can be computed. To exploit t...

From computer-aided drug discovery to computer-driven drug discovery.

Drug discovery today. Technologies
Computational chemistry and structure-based design have traditionally been viewed as a subset of tools that could aid acceleration of the drug discovery process, but were not commonly regarded as a driving force in small molecule drug discovery. In t...

Redefining β-blocker response in heart failure patients with sinus rhythm and atrial fibrillation: a machine learning cluster analysis.

Lancet (London, England)
BACKGROUND: Mortality remains unacceptably high in patients with heart failure and reduced left ventricular ejection fraction (LVEF) despite advances in therapeutics. We hypothesised that a novel artificial intelligence approach could better assess m...

Machine learning models of ischemia/hemorrhage in moyamoya disease and analysis of its risk factors.

Clinical neurology and neurosurgery
OBJECT: This study aimed to determine the risk factors of ischemic/hemorrhagic stroke in patients suffering moyamoya disease (MMD), as well as to compare the effects of six analysis methods.