AIMC Topic: PubMed

Clear Filters Showing 141 to 150 of 157 articles

Forty-two Million Ways to Describe Pain: Topic Modeling of 200,000 PubMed Pain-Related Abstracts Using Natural Language Processing and Deep Learning-Based Text Generation.

Pain medicine (Malden, Mass.)
OBJECTIVE: Recent efforts to update the definitions and taxonomic structure of concepts related to pain have revealed opportunities to better quantify topics of existing pain research subject areas.

Leveraging PubMed to Create a Specialty-Based Sense Inventory for Spanish Acronym Resolution.

Studies in health technology and informatics
Acronyms frequently occur in clinical text, which makes their identification, disambiguation and resolution an important task in clinical natural language processing. This paper contributes to acronym resolution in Spanish through the creation of a s...

Advancing PICO element detection in biomedical text via deep neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: In evidence-based medicine, defining a clinical question in terms of the specific patient problem aids the physicians to efficiently identify appropriate resources and search for the best available evidence for medical treatment. In order...

Can artificial intelligence replace manual search for systematic literature? Review on cutaneous manifestations in primary Sjögren's syndrome.

Rheumatology (Oxford, England)
OBJECTIVES: Manual systematic literature reviews are becoming increasingly challenging due to the sharp rise in publications. The primary objective of this literature review was to compare manual and computer software using artificial intelligence re...

Pretraining to Recognize PICO Elements from Randomized Controlled Trial Literature.

Studies in health technology and informatics
PICO (Population/problem, Intervention, Comparison, and Outcome) is widely adopted for formulating clinical questions to retrieve evidence from the literature. It plays a crucial role in Evidence-Based Medicine (EBM). This paper contributes a scalabl...

The MeSH-Gram Neural Network Model: Extending Word Embedding Vectors with MeSH Concepts for Semantic Similarity.

Studies in health technology and informatics
Eliciting semantic similarity between concepts remains a challenging task. Recent approaches founded on embedding vectors have gained in popularity as they have risen to efficiently capture semantic relationships. The underlying idea is that two word...

Automatic Human-like Mining and Constructing Reliable Genetic Association Database with Deep Reinforcement Learning.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
The increasing amount of scientific literature in biological and biomedical science research has created a challenge in continuous and reliable curation of the latest knowledge discovered, and automatic biomedical text-mining has been one of the answ...

Gene multifunctionality scoring using gene ontology.

Journal of bioinformatics and computational biology
Multifunctional genes are important genes because of their essential roles in human cells. Studying and analyzing multifunctional genes can help understand disease mechanisms and drug discovery. We propose a computational method for scoring gene mult...

Exploiting and assessing multi-source data for supervised biomedical named entity recognition.

Bioinformatics (Oxford, England)
MOTIVATION: Recognition of biomedical entities from scientific text is a critical component of natural language processing and automated information extraction platforms. Modern named entity recognition approaches rely heavily on supervised machine l...

Automatic recognition of self-acknowledged limitations in clinical research literature.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: To automatically recognize self-acknowledged limitations in clinical research publications to support efforts in improving research transparency.