AI Medical Compendium Topic

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A deep learning approach to automatic detection of early glaucoma from visual fields.

PloS one
PURPOSE: To investigate the suitability of multi-scale spatial information in 30o visual fields (VF), computed from a Convolutional Neural Network (CNN) classifier, for early-glaucoma vs. control discrimination.

A novel attention-based hybrid CNN-RNN architecture for sEMG-based gesture recognition.

PloS one
The surface electromyography (sEMG)-based gesture recognition with deep learning approach plays an increasingly important role in human-computer interaction. Existing deep learning architectures are mainly based on Convolutional Neural Network (CNN) ...

Sense classification of shallow discourse relations with focused RNNs.

PloS one
Understanding the sense of discourse relations between segments of text is essential to truly comprehend any natural language text. Several automated approaches have been suggested, but all rely on external resources, linguistic feature engineering, ...

The PInSoRo dataset: Supporting the data-driven study of child-child and child-robot social dynamics.

PloS one
The study of the fine-grained social dynamics between children is a methodological challenge, yet a good understanding of how social interaction between children unfolds is important not only to Developmental and Social Psychology, but recently has b...

Machine Learning for Drug-Target Interaction Prediction.

Molecules (Basel, Switzerland)
Identifying drug-target interactions will greatly narrow down the scope of search of candidate medications, and thus can serve as the vital first step in drug discovery. Considering that in vitro experiments are extremely costly and time-consuming, h...

Automated retinopathy of prematurity screening using deep neural networks.

EBioMedicine
BACKGROUND: Retinopathy of prematurity (ROP) is the leading cause of childhood blindness worldwide. Automated ROP detection system is urgent and it appears to be a safe, reliable, and cost-effective complement to human experts.

De novo profile generation based on sequence context specificity with the long short-term memory network.

BMC bioinformatics
BACKGROUND: Long short-term memory (LSTM) is one of the most attractive deep learning methods to learn time series or contexts of input data. Increasing studies, including biological sequence analyses in bioinformatics, utilize this architecture. Ami...

Semi-Supervised Recurrent Neural Network for Adverse Drug Reaction mention extraction.

BMC bioinformatics
BACKGROUND: Social media is a useful platform to share health-related information due to its vast reach. This makes it a good candidate for public-health monitoring tasks, specifically for pharmacovigilance. We study the problem of extraction of Adve...

Managing the 1920s' Chilean educational crisis: A historical view combined with machine learning.

PloS one
In the first decades of the 20th century, political actors diagnosed the incubation of a crisis in the Chilean schooling process. Low rates of enrollment, literacy, and attendance, inefficiency in the use of resources, poverty, and a reduced number o...

Deep learning in pharmacogenomics: from gene regulation to patient stratification.

Pharmacogenomics
This Perspective provides examples of current and future applications of deep learning in pharmacogenomics, including: identification of novel regulatory variants located in noncoding domains of the genome and their function as applied to pharmacoepi...