AI Medical Compendium Topic:
Supervised Machine Learning

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Characterization of Nanoscale Organization of F-Actin in Morphologically Distinct Dendritic Spines Using Supervised Learning.

eNeuro
The cytoarchitecture of a neuron is very important in defining morphology and ultrastructure. Although there is a wealth of information on the molecular components that make and regulate these ultrastructures, there is a dearth of understanding of ho...

Characterizing Alzheimer's Disease With Image and Genetic Biomarkers Using Supervised Topic Models.

IEEE journal of biomedical and health informatics
Neuroimaging and genetic biomarkers have been widely studied from discriminative perspectives towards Alzheimer's disease (AD) classification, since neuroanatomical patterns and genetic variants are jointly critical indicators for AD diagnosis. Gener...

Convergent Temperature Representations in Artificial and Biological Neural Networks.

Neuron
Discoveries in biological neural networks (BNNs) shaped artificial neural networks (ANNs) and computational parallels between ANNs and BNNs have recently been discovered. However, it is unclear to what extent discoveries in ANNs can give insight into...

Comparison of machine learning algorithms for clinical event prediction (risk of coronary heart disease).

Journal of biomedical informatics
AIM: The aim of this study is to compare the utility of several supervised machine learning (ML) algorithms for predicting clinical events in terms of their internal validity and accuracy. The results, which were obtained using two statistical softwa...

Self-supervised learning for medical image analysis using image context restoration.

Medical image analysis
Machine learning, particularly deep learning has boosted medical image analysis over the past years. Training a good model based on deep learning requires large amount of labelled data. However, it is often difficult to obtain a sufficient number of ...

Chemical-induced disease relation extraction via attention-based distant supervision.

BMC bioinformatics
BACKGROUND: Automatically understanding chemical-disease relations (CDRs) is crucial in various areas of biomedical research and health care. Supervised machine learning provides a feasible solution to automatically extract relations between biomedic...

A Semisupervised Recurrent Convolutional Attention Model for Human Activity Recognition.

IEEE transactions on neural networks and learning systems
Recent years have witnessed the success of deep learning methods in human activity recognition (HAR). The longstanding shortage of labeled activity data inherently calls for a plethora of semisupervised learning methods, and one of the most challengi...

Disentangled representation learning in cardiac image analysis.

Medical image analysis
Typically, a medical image offers spatial information on the anatomy (and pathology) modulated by imaging specific characteristics. Many imaging modalities including Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) can be interpreted in ...

The Extended Supervised Learning Event (ESLE): Assessing Nontechnical Skills in Emergency Medicine Trainees in the Workplace.

Annals of emergency medicine
STUDY OBJECTIVE: The contribution of emergency medicine clinicians' nontechnical skills in providing safe, high-quality care in the emergency department (ED) is well known. In 2015, the UK Royal College of Emergency Medicine introduced explicit valid...

Weakly supervised classification of aortic valve malformations using unlabeled cardiac MRI sequences.

Nature communications
Biomedical repositories such as the UK Biobank provide increasing access to prospectively collected cardiac imaging, however these data are unlabeled, which creates barriers to their use in supervised machine learning. We develop a weakly supervised ...