AI Medical Compendium Topic:
Semantics

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Characterization of the Context of Drug Concepts in Research Protocols: An Empiric Study to Guide Ontology Development.

AMIA ... Annual Symposium proceedings. AMIA Symposium
We examined a large body of research study documents (protocols) to identify mentions of drug concepts and established base concepts and roles needed to characterize the semantics of these instances. We found these concepts in three general situation...

Ontology-Based Search of Genomic Metadata.

IEEE/ACM transactions on computational biology and bioinformatics
The Encyclopedia of DNA Elements (ENCODE) is a huge and still expanding public repository of more than 4,000 experiments and 25,000 data files, assembled by a large international consortium since 2007; unknown biological knowledge can be extracted fr...

A-DaGO-Fun: an adaptable Gene Ontology semantic similarity-based functional analysis tool.

Bioinformatics (Oxford, England)
SUMMARY: Gene Ontology (GO) semantic similarity measures are being used for biological knowledge discovery based on GO annotations by integrating biological information contained in the GO structure into data analyses. To empower users to quickly com...

Semantic biomedical resource discovery: a Natural Language Processing framework.

BMC medical informatics and decision making
BACKGROUND: A plethora of publicly available biomedical resources do currently exist and are constantly increasing at a fast rate. In parallel, specialized repositories are been developed, indexing numerous clinical and biomedical tools. The main dra...

Biologically Inspired Model for Visual Cognition Achieving Unsupervised Episodic and Semantic Feature Learning.

IEEE transactions on cybernetics
Recently, many biologically inspired visual computational models have been proposed. The design of these models follows the related biological mechanisms and structures, and these models provide new solutions for visual recognition tasks. In this pap...

An Unsupervised Graph Based Continuous Word Representation Method for Biomedical Text Mining.

IEEE/ACM transactions on computational biology and bioinformatics
In biomedical text mining tasks, distributed word representation has succeeded in capturing semantic regularities, but most of them are shallow-window based models, which are not sufficient for expressing the meaning of words. To represent words usin...

Extracting Biomedical Event with Dual Decomposition Integrating Word Embeddings.

IEEE/ACM transactions on computational biology and bioinformatics
Extracting biomedical event from literatures has attracted much attention recently. By now, most of the state-of-the-art systems have been based on pipelines which suffer from cascading errors, and the words encoded by one-hot are unable to represent...

FIR: An Effective Scheme for Extracting Useful Metadata from Social Media.

Journal of medical systems
Recently, the use of social media for health information exchange is expanding among patients, physicians, and other health care professionals. In medical areas, social media allows non-experts to access, interpret, and generate medical information f...

Mining heart disease risk factors in clinical text with named entity recognition and distributional semantic models.

Journal of biomedical informatics
We present the design, and analyze the performance of a multi-stage natural language processing system employing named entity recognition, Bayesian statistics, and rule logic to identify and characterize heart disease risk factor events in diabetic p...