AIMC Topic: Machine Learning

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Machine learning identifies a compact gene set for monitoring the circadian clock in human blood.

Genome medicine
BACKGROUND: The circadian clock and the daily rhythms it produces are crucial for human health, but are often disrupted by the modern environment. At the same time, circadian rhythms may influence the efficacy and toxicity of therapeutics and the met...

A doubly robust approach for cost-effectiveness estimation from observational data.

Statistical methods in medical research
Estimation of common cost-effectiveness measures, including the incremental cost-effectiveness ratio and the net monetary benefit, is complicated by the need to account for informative censoring and inherent skewness of the data. In addition, since t...

A novel hierarchical selective ensemble classifier with bioinformatics application.

Artificial intelligence in medicine
Selective ensemble learning is a technique that selects a subset of diverse and accurate basic models in order to generate stronger generalization ability. In this paper, we proposed a novel learning algorithm that is based on parallel optimization a...

Feedback for reinforcement learning based brain-machine interfaces using confidence metrics.

Journal of neural engineering
OBJECTIVE: For brain-machine interfaces (BMI) to be used in activities of daily living by paralyzed individuals, the BMI should be as autonomous as possible. One of the challenges is how the feedback is extracted and utilized in the BMI. Our long-ter...

Metric learning for Parkinsonian identification from IMU gait measurements.

Gait & posture
Diagnosis of people with mild Parkinson's symptoms is difficult. Nevertheless, variations in gait pattern can be utilised to this purpose, when measured via Inertial Measurement Units (IMUs). Human gait, however, possesses a high degree of variabilit...

Prognosticating Functional Outcome After Intracerebral Hemorrhage: The ICHOP Score.

World neurosurgery
BACKGROUND: The morbidity, mortality, and monetary cost associated with intracerebral hemorrhage (ICH) is devastatingly high. Several scoring systems have been proposed to prognosticate outcomes after ICH, although the original ICH Score is still the...

Analysis of live cell images: Methods, tools and opportunities.

Methods (San Diego, Calif.)
Advances in optical microscopy, biosensors and cell culturing technologies have transformed live cell imaging. Thanks to these advances live cell imaging plays an increasingly important role in basic biology research as well as at all stages of drug ...

Learning representation hierarchies by sharing visual features: a computational investigation of Persian character recognition with unsupervised deep learning.

Cognitive processing
In humans, efficient recognition of written symbols is thought to rely on a hierarchical processing system, where simple features are progressively combined into more abstract, high-level representations. Here, we present a computational model of Per...

Predictability and interpretability of hybrid link-level crash frequency models for urban arterials compared to cluster-based and general negative binomial regression models.

International journal of injury control and safety promotion
Machine learning (ML) techniques have higher prediction accuracy compared to conventional statistical methods for crash frequency modelling. However, their black-box nature limits the interpretability. The objective of this research is to combine bot...

Machine learning in the prediction of cardiac epicardial and mediastinal fat volumes.

Computers in biology and medicine
We propose a methodology to predict the cardiac epicardial and mediastinal fat volumes in computed tomography images using regression algorithms. The obtained results indicate that it is feasible to predict these fats with a high degree of correlatio...