AIMC Topic: Machine Learning

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Shape Completion Using Deep Boltzmann Machine.

Computational intelligence and neuroscience
Shape completion is an important task in the field of image processing. An alternative method is to capture the shape information and finish the completion by a generative model, such as Deep Boltzmann Machine. With its powerful ability to deal with ...

Using machine learning to identify air pollution exposure profiles associated with early cognitive skills among U.S. children.

Environmental pollution (Barking, Essex : 1987)
Data-driven machine learning methods present an opportunity to simultaneously assess the impact of multiple air pollutants on health outcomes. The goal of this study was to apply a two-stage, data-driven approach to identify associations between air ...

Application of machine learning in prediction of hydrotrope-enhanced solubilisation of indomethacin.

International journal of pharmaceutics
Systematic in-vitro studies have been conducted to determine the ability of a range of 10 potential hydrotropes to improve the apparent aqueous solubility of the poorly water soluble drug, indomethacin. Solubilisation of the drug in the presence of t...

The impact of machine learning techniques in the study of bipolar disorder: A systematic review.

Neuroscience and biobehavioral reviews
Machine learning techniques provide new methods to predict diagnosis and clinical outcomes at an individual level. We aim to review the existing literature on the use of machine learning techniques in the assessment of subjects with bipolar disorder....

EEG machine learning for accurate detection of cholinergic intervention and Alzheimer's disease.

Scientific reports
Monitoring effects of disease or therapeutic intervention on brain function is increasingly important for clinical trials, albeit hampered by inter-individual variability and subtle effects. Here, we apply complementary biomarker algorithms to electr...

Fiber tractography using machine learning.

NeuroImage
We present a fiber tractography approach based on a random forest classification and voting process, guiding each step of the streamline progression by directly processing raw diffusion-weighted signal intensities. For comparison to the state-of-the-...

Feature selection before EEG classification supports the diagnosis of Alzheimer's disease.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: In many decision support systems, some input features can be marginal or irrelevant to the diagnosis, while others can be redundant among each other. Thus, feature selection (FS) algorithms are often considered to find relevant/non-redunda...

Machine Learning Algorithms for Risk Prediction of Severe Hand-Foot-Mouth Disease in Children.

Scientific reports
The identification of indicators for severe HFMD is critical for early prevention and control of the disease. With this goal in mind, 185 severe and 345 mild HFMD cases were assessed. Patient demographics, clinical features, MRI findings, and laborat...

Deep Learning for Fully-Automated Localization and Segmentation of Rectal Cancer on Multiparametric MR.

Scientific reports
Multiparametric Magnetic Resonance Imaging (MRI) can provide detailed information of the physical characteristics of rectum tumours. Several investigations suggest that volumetric analyses on anatomical and functional MRI contain clinically valuable ...

Machine Learning Consensus Scoring Improves Performance Across Targets in Structure-Based Virtual Screening.

Journal of chemical information and modeling
In structure-based virtual screening, compound ranking through a consensus of scores from a variety of docking programs or scoring functions, rather than ranking by scores from a single program, provides better predictive performance and reduces targ...