AIMC Topic: Neural Networks, Computer

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Extracting psychiatric stressors for suicide from social media using deep learning.

BMC medical informatics and decision making
BACKGROUND: Suicide has been one of the leading causes of deaths in the United States. One major cause of suicide is psychiatric stressors. The detection of psychiatric stressors in an at risk population will facilitate the early prevention of suicid...

Chemical-induced disease extraction via recurrent piecewise convolutional neural networks.

BMC medical informatics and decision making
BACKGROUND: Extracting relationships between chemicals and diseases from unstructured literature have attracted plenty of attention since the relationships are very useful for a large number of biomedical applications such as drug repositioning and p...

Towards improving diagnosis of skin diseases by combining deep neural network and human knowledge.

BMC medical informatics and decision making
BACKGROUND: The emergence of the deep convolutional neural network (CNN) greatly improves the quality of computer-aided supporting systems. However, due to the challenges of generating reliable and timely results, clinical adoption of computer-aided ...

Shoulder physiotherapy exercise recognition: machine learning the inertial signals from a smartwatch.

Physiological measurement
OBJECTIVE: Participation in a physical therapy program is considered one of the greatest predictors of successful conservative management of common shoulder disorders. However, adherence to these protocols is often poor and typically worse for unsupe...

A Neuromuscular Interface for Robotic Devices Control.

Computational and mathematical methods in medicine
A neuromuscular interface (NI) that can be employed to operate external robotic devices (RD), including commercial ones, was proposed. Multichannel electromyographic (EMG) signal is used in the control loop. Control signal can also be supplemented wi...

A machine-learning framework for automatic reference-free quality assessment in MRI.

Magnetic resonance imaging
Magnetic resonance (MR) imaging offers a wide variety of imaging techniques. A large amount of data is created per examination which needs to be checked for sufficient quality in order to derive a meaningful diagnosis. This is a manual process and th...

A new mechanical approach to handle generalized Hopfield neural networks.

Neural networks : the official journal of the International Neural Network Society
We propose a modification of the cost function of the Hopfield model whose salient features shine in its Taylor expansion and result in more than pairwise interactions with alternate signs, suggesting a unified framework for handling both with deep l...

Generative adversarial networks for reconstructing natural images from brain activity.

NeuroImage
We explore a method for reconstructing visual stimuli from brain activity. Using large databases of natural images we trained a deep convolutional generative adversarial network capable of generating gray scale photos, similar to stimuli presented du...

Deep neural models for extracting entities and relationships in the new RDD corpus relating disabilities and rare diseases.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: There is a huge amount of rare diseases, many of which have associated important disabilities. It is paramount to know in advance the evolution of the disease in order to limit and prevent the appearance of disabilities and ...

Effect of Cd and Pb Pollutions on Physiological Growth: Wavelet Neural Network (WNN) as a New Approach on Age Determination of Coenobita scaevola.

Bulletin of environmental contamination and toxicology
Environmental pollution of aquatic ecosystems leads to an interference in several fundamental biochemical and physiological functions. In this study the interference of Cd and Pb pollutions on the physiological growth and subsequently on the age dete...