AIMC Topic: Natural Language Processing

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A corpus of potentially contradictory research claims from cardiovascular research abstracts.

Journal of biomedical semantics
BACKGROUND: Research literature in biomedicine and related fields contains a huge number of claims, such as the effectiveness of treatments. These claims are not always consistent and may even contradict each other. Being able to identify contradicto...

Temporal bone radiology report classification using open source machine learning and natural langue processing libraries.

BMC medical informatics and decision making
BACKGROUND: Radiology reports are a rich resource for biomedical research. Prior to utilization, trained experts must manually review reports to identify discrete outcomes. The Audiological and Genetic Database (AudGenDB) is a public, de-identified r...

An ensemble method for extracting adverse drug events from social media.

Artificial intelligence in medicine
OBJECTIVE: Because adverse drug events (ADEs) are a serious health problem and a leading cause of death, it is of vital importance to identify them correctly and in a timely manner. With the development of Web 2.0, social media has become a large dat...

BELTracker: evidence sentence retrieval for BEL statements.

Database : the journal of biological databases and curation
Biological expression language (BEL) is one of the main formal representation models of biological networks. The primary source of information for curating biological networks in BEL representation has been literature. It remains a challenge to ident...

BelSmile: a biomedical semantic role labeling approach for extracting biological expression language from text.

Database : the journal of biological databases and curation
Biological expression language (BEL) is one of the most popular languages to represent the causal and correlative relationships among biological events. Automatically extracting and representing biomedical events using BEL can help biologists quickly...

Filtering large-scale event collections using a combination of supervised and unsupervised learning for event trigger classification.

Journal of biomedical semantics
BACKGROUND: Biomedical event extraction is one of the key tasks in biomedical text mining, supporting various applications such as database curation and hypothesis generation. Several systems, some of which have been applied at a large scale, have be...

Extracting a stroke phenotype risk factor from Veteran Health Administration clinical reports: an information content analysis.

Journal of biomedical semantics
BACKGROUND: In the United States, 795,000 people suffer strokes each year; 10-15 % of these strokes can be attributed to stenosis caused by plaque in the carotid artery, a major stroke phenotype risk factor. Studies comparing treatments for the manag...

miRiaD: A Text Mining Tool for Detecting Associations of microRNAs with Diseases.

Journal of biomedical semantics
BACKGROUND: MicroRNAs are increasingly being appreciated as critical players in human diseases, and questions concerning the role of microRNAs arise in many areas of biomedical research. There are several manually curated databases of microRNA-diseas...