AIMC Topic: Machine Learning

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Machine learning-based prediction of radiographic progression in patients with axial spondyloarthritis.

Clinical rheumatology
INTRODUCTION: Machine learning is applied to characterize the risk and predict outcomes in multi-dimensional data. The prediction of radiographic progression in axial spondyloarthritis (axSpA) remains limited. Hence, we tested the feasibility of supe...

A vision-based approach for fall detection using multiple cameras and convolutional neural networks: A case study using the UP-Fall detection dataset.

Computers in biology and medicine
The automatic recognition of human falls is currently an important topic of research for the computer vision and artificial intelligence communities. In image analysis, it is common to use a vision-based approach for fall detection and classification...

A comparison of machine learning classifiers for dementia with Lewy bodies using miRNA expression data.

BMC medical genomics
BACKGROUND: Dementia with Lewy bodies (DLB) is the second most common subtype of neurodegenerative dementia in humans following Alzheimer's disease (AD). Present clinical diagnosis of DLB has high specificity and low sensitivity and finding potential...

Heart sound classification using the SNMFNet classifier.

Physiological measurement
OBJECTIVE: Heart sound classification still suffers from the challenges involved in achieving high accuracy in the case of small samples. Dimension reduction attempts to extract low-dimensional features with more discriminability from high-dimensiona...

Multivoxel pattern analysis reveals dissociations between subjective fear and its physiological correlates.

Molecular psychiatry
In studies of anxiety and other affective disorders, objectively measured physiological responses have commonly been used as a proxy for measuring subjective experiences associated with pathology. However, this commonly adopted "biosignal" approach h...

Correlation of machine learning computed tomography-based fractional flow reserve with instantaneous wave free ratio to detect hemodynamically significant coronary stenosis.

Clinical research in cardiology : official journal of the German Cardiac Society
BACKGROUND: Fractional flow reserve based on coronary CT angiography (CT-FFR) is gaining importance for non-invasive hemodynamic assessment of coronary artery disease (CAD). We evaluated the on-site CT-FFR with a machine learning algorithm (CT-FFR) f...

Machine learning techniques to predict the effectiveness of music therapy: A randomized controlled trial.

Computer methods and programs in biomedicine
BACKGROUND: The literature shows the effectiveness of music listening, but which factors and what types of music produce therapeutic effects, as well as how music therapists can select music, remain unclear. Here, we present a study to establish the ...

Function2Form Bridge-Toward synthetic protein holistic performance prediction.

Proteins
Protein engineering and synthetic biology stand to benefit immensely from recent advances in silico tools for structural and functional analyses of proteins. In the context of designing novel proteins, current in silico tools inform the user on indiv...

Hash Transformation and Machine Learning-Based Decision-Making Classifier Improved the Accuracy Rate of Automated Parkinson's Disease Screening.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Digitalized hand-drawn pattern is a noninvasive and reproducible assistive manner to obtain hand actions and motions for evaluating functional tremors and upper-limb movement disorders. In this study, spirals and straight lines in polar coordinates a...