AI Medical Compendium Topic

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Knowledge

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"When they say weed causes depression, but it's your fav antidepressant": Knowledge-aware attention framework for relationship extraction.

PloS one
With the increasing legalization of medical and recreational use of cannabis, more research is needed to understand the association between depression and consumer behavior related to cannabis consumption. Big social media data has potential to provi...

Extracting Biomedical Entity Relations using Biological Interaction Knowledge.

Interdisciplinary sciences, computational life sciences
Discovering relations of cross-type biomedical entities is crucial for biology research. A large amount of potential or indirect connected biological relations is hidden in millions of biomedical literatures and biological databases. The previous rul...

How Digital Technologies Modify The Work Characteristics: A Preliminary Study.

The Spanish journal of psychology
New technologies with unprecedented agentic capabilities (i.e., action selection, protocol development) are now introduced in organizations such as Big Data, 3D printing or artificial intelligence. Because they are endowed with novel capabilities tha...

Artificial intelligence methods for a Bayesian epistemology-powered evidence evaluation.

Journal of evaluation in clinical practice
RATIONALE, AIMS AND OBJECTIVES: The diversity of types of evidence (eg, case reports, animal studies and observational studies) makes the assessment of a drug's safety profile into a formidable challenge. While frequentist uncertain inference struggl...

Insights into mobile health application market via a content analysis of marketplace data with machine learning.

PloS one
BACKGROUND: Despite the benefits offered by an abundance of health applications promoted on app marketplaces (e.g., Google Play Store), the wide adoption of mobile health and e-health apps is yet to come.

Global and Local Knowledge-Aware Attention Network for Action Recognition.

IEEE transactions on neural networks and learning systems
Convolutional neural networks (CNNs) have shown an effective way to learn spatiotemporal representation for action recognition in videos. However, most traditional action recognition algorithms do not employ the attention mechanism to focus on essent...

Learning Student Networks via Feature Embedding.

IEEE transactions on neural networks and learning systems
Deep convolutional neural networks have been widely used in numerous applications, but their demanding storage and computational resource requirements prevent their applications on mobile devices. Knowledge distillation aims to optimize a portable st...

C-Norm: a neural approach to few-shot entity normalization.

BMC bioinformatics
BACKGROUND: Entity normalization is an important information extraction task which has gained renewed attention in the last decade, particularly in the biomedical and life science domains. In these domains, and more generally in all specialized domai...

DAPath: Distance-aware knowledge graph reasoning based on deep reinforcement learning.

Neural networks : the official journal of the International Neural Network Society
Knowledge graph reasoning aims to find reasoning paths for relations over incomplete knowledge graphs (KG). Prior works may not take into account that the rewards for each position (vertex in the graph) may be different. We propose the distance-aware...

How to Extract More Information With Less Burden: Fundus Image Classification and Retinal Disease Localization With Ophthalmologist Intervention.

IEEE journal of biomedical and health informatics
Image classification using convolutional neural networks (CNNs) outperforms other state-of-the-art methods. Moreover, attention can be visualized as a heatmap to improve the explainability of results of a CNN. We designed a framework that can generat...