AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Linear Models

Showing 411 to 420 of 559 articles

Clear Filters

hMuLab: A Biomedical Hybrid MUlti-LABel Classifier Based on Multiple Linear Regression.

IEEE/ACM transactions on computational biology and bioinformatics
Many biomedical classification problems are multi-label by nature, e.g., a gene involved in a variety of functions and a patient with multiple diseases. The majority of existing classification algorithms assumes each sample with only one class label,...

Coexistence and local μ-stability of multiple equilibrium points for memristive neural networks with nonmonotonic piecewise linear activation functions and unbounded time-varying delays.

Neural networks : the official journal of the International Neural Network Society
In this paper, the coexistence and dynamical behaviors of multiple equilibrium points are discussed for a class of memristive neural networks (MNNs) with unbounded time-varying delays and nonmonotonic piecewise linear activation functions. By means o...

Combining two open source tools for neural computation (BioPatRec and Netlab) improves movement classification for prosthetic control.

BMC research notes
BACKGROUND: Controlling a myoelectric prosthesis for upper limbs is increasingly challenging for the user as more electrodes and joints become available. Motion classification based on pattern recognition with a multi-electrode array allows multiple ...

Automatic Liver Segmentation from CT Images Using Single-Block Linear Detection.

BioMed research international
Automatic liver segmentation not only plays an important role in the analysis of liver disease, but also reduces the cost and humanity's impact in segmentation. In addition, liver segmentation is a very challenging task due to countless anatomical va...

Development of Health Parameter Model for Risk Prediction of CVD Using SVM.

Computational and mathematical methods in medicine
Current methods of cardiovascular risk assessment are performed using health factors which are often based on the Framingham study. However, these methods have significant limitations due to their poor sensitivity and specificity. We have compared th...

Making use of longitudinal information in pattern recognition.

Human brain mapping
Longitudinal designs are widely used in medical studies as a means of observing within-subject changes over time in groups of subjects, thereby aiming to improve sensitivity for detecting disease effects. Paralleling an increased use of such studies ...

In Silico Calculation of Infinite Dilution Activity Coefficients of Molecular Solutes in Ionic Liquids: Critical Review of Current Methods and New Models Based on Three Machine Learning Algorithms.

Journal of chemical information and modeling
The aim of the paper is to address all the disadvantages of currently available models for calculating infinite dilution activity coefficients (γ(∞)) of molecular solutes in ionic liquids (ILs)-a relevant property from the point of view of many appli...

Longitudinal clinical score prediction in Alzheimer's disease with soft-split sparse regression based random forest.

Neurobiology of aging
Alzheimer's disease (AD) is an irreversible neurodegenerative disease and affects a large population in the world. Cognitive scores at multiple time points can be reliably used to evaluate the progression of the disease clinically. In recent studies,...

Remote triage support algorithm based on fuzzy logic.

Journal of the Royal Army Medical Corps
OBJECTIVES: This paper presents a remote triage support algorithm as a part of a complex military telemedicine system which provides continuous monitoring of soldiers' vital sign data gathered on-site using unobtrusive set of sensors.

Motor Imagery Classification Based on Bilinear Sub-Manifold Learning of Symmetric Positive-Definite Matrices.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
In motor imagery brain-computer interfaces (BCIs), the symmetric positive-definite (SPD) covariance matrices of electroencephalogram (EEG) signals carry important discriminative information. In this paper, we intend to classify motor imagery EEG sign...