AIMC Topic:
Supervised Machine Learning
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Perspectives on Machine Learning for Classification of Schizotypy Using fMRI Data.
Schizophrenia bulletin
Oct 15, 2018
Functional magnetic resonance imaging is capable of estimating functional activation and connectivity in the human brain, and lately there has been increased interest in the use of these functional modalities combined with machine learning for identi...
Supervised Machine-learning Predictive Analytics for Prediction of Postinduction Hypotension.
Anesthesiology
Oct 1, 2018
WHAT WE ALREADY KNOW ABOUT THIS TOPIC: WHAT THIS ARTICLE TELLS US THAT IS NEW: BACKGROUND:: Hypotension is a risk factor for adverse perioperative outcomes. Machine-learning methods allow large amounts of data for development of robust predictive ana...
Learning predictive models of drug side-effect relationships from distributed representations of literature-derived semantic predications.
Journal of the American Medical Informatics Association : JAMIA
Oct 1, 2018
OBJECTIVE: The aim of this work is to leverage relational information extracted from biomedical literature using a novel synthesis of unsupervised pretraining, representational composition, and supervised machine learning for drug safety monitoring.
Exploiting and assessing multi-source data for supervised biomedical named entity recognition.
Bioinformatics (Oxford, England)
Jul 15, 2018
MOTIVATION: Recognition of biomedical entities from scientific text is a critical component of natural language processing and automated information extraction platforms. Modern named entity recognition approaches rely heavily on supervised machine l...
Computational Principles of Supervised Learning in the Cerebellum.
Annual review of neuroscience
Jul 8, 2018
Supervised learning plays a key role in the operation of many biological and artificial neural networks. Analysis of the computations underlying supervised learning is facilitated by the relatively simple and uniform architecture of the cerebellum, a...
Investigating Upper Limb Movement Classification on Users with Tetraplegia as a Possible Neuroprosthesis Interface.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2018
Spinal cord injury (SCI), stroke and other nervous system conditions can result in partial or total paralysis of individual's limbs. Numerous technologies have been proposed to assist neurorehabilitation or movement restoration, e.g. robotics or neur...
Personalised meal eating behaviour analysis via semi-supervised learning.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2018
Automated monitoring and analysis of eating behaviour patterns, i.e., "how one eats", has recently received much attention by the research community, owing to the association of eating patterns with health-related problems and especially obesity and ...
A sEMG Classification Framework with Less Training Data.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2018
Supervised machine learning algorithms, such as Artificial Neural Network (ANN), have been applied to surface electromyograph (sEMG) to classify user's muscular states. This paper introduces a novel framework to design a binary sEMG classifier to dis...
Personalized Human Activity Recognition using Wearables: A Manifold Learning-based Knowledge Transfer.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2018
Human activity recognition (HAR) is an important component in health-care systems. For example, it can enable context-aware applications such as elderly care and patient monitoring. Relying on a set of training data, supervised machine learning algor...
Synthetic Sensor Data Generation for Health Applications: A Supervised Deep Learning Approach.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2018
Recent advancements in mobile devices, data analysis, and wearable sensors render the capability of in-place health monitoring. Supervised machine learning algorithms, the core intelligence of these systems, learn from labeled training data. However,...
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