AIMC Topic: Humans

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An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction.

Lancet (London, England)
BACKGROUND: Atrial fibrillation is frequently asymptomatic and thus underdetected but is associated with stroke, heart failure, and death. Existing screening methods require prolonged monitoring and are limited by cost and low yield. We aimed to deve...

Clinical effects of robot-assisted gait training and treadmill training for Parkinson's disease. A randomized controlled trial.

Annals of physical and rehabilitation medicine
BACKGROUND: Although gait disorders strongly contribute to perceived disability in people with Parkinson's disease, clinical trials have failed to identify which task-oriented gait training method can provide the best benefit. Freezing of gait remain...

Development of a biological signal-based evaluator for robot-assisted upper-limb rehabilitation: a pilot study.

Australasian physical & engineering sciences in medicine
Bio-signal based assessment for upper-limb functions is an attractive technology for rehabilitation. In this work, an upper-limb function evaluator is developed based on biological signals, which could be used for selecting different robotic training...

Deep Splicing Code: Classifying Alternative Splicing Events Using Deep Learning.

Genes
Alternative splicing (AS) is the process of combining different parts of the pre-mRNA to produce diverse transcripts and eventually different protein products from a single gene. In computational biology field, researchers try to understand AS behavi...

Medical Image Segmentation Algorithm Based on Feedback Mechanism CNN.

Contrast media & molecular imaging
With the development of computer vision and image segmentation technology, medical image segmentation and recognition technology has become an important part of computer-aided diagnosis. The traditional image segmentation method relies on artificial ...

Leveraging Contextual Sentences for Text Classification by Using a Neural Attention Model.

Computational intelligence and neuroscience
We explored several approaches to incorporate context information in the deep learning framework for text classification, including designing different attention mechanisms based on different neural network and extracting some additional features fro...

Probabilistic associative learning suffices for learning the temporal structure of multiple sequences.

PloS one
From memorizing a musical tune to navigating a well known route, many of our underlying behaviors have a strong temporal component. While the mechanisms behind the sequential nature of the underlying brain activity are likely multifarious and multi-s...

Detecting drug-drug interactions using artificial neural networks and classic graph similarity measures.

PloS one
Drug-drug interactions are preventable causes of medical injuries and often result in doctor and emergency room visits. Computational techniques can be used to predict potential drug-drug interactions. We approach the drug-drug interaction prediction...