AIMC Topic: Unified Medical Language System

Clear Filters Showing 21 to 30 of 151 articles

Aspect-based sentiment analysis with graph convolution over syntactic dependencies.

Artificial intelligence in medicine
Aspect-based sentiment analysis is a natural language processing task whose aim is to automatically classify the sentiment associated with a specific aspect of a written text. In this study, we propose a novel model for aspect-based sentiment analysi...

Improved biomedical word embeddings in the transformer era.

Journal of biomedical informatics
BACKGROUND: Recent natural language processing (NLP) research is dominated by neural network methods that employ word embeddings as basic building blocks. Pre-training with neural methods that capture local and global distributional properties (e.g.,...

A deep database of medical abbreviations and acronyms for natural language processing.

Scientific data
The recognition, disambiguation, and expansion of medical abbreviations and acronyms is of upmost importance to prevent medically-dangerous misinterpretation in natural language processing. To support recognition, disambiguation, and expansion, we pr...

An Examination of the Statistical Laws of Semantic Change in Clinical Notes.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
Natural language is continually changing. Given the prevalence of unstructured, free-text clinical notes in the healthcare domain, understanding the aspects of this change is of critical importance to clinical Natural Language Processing (NLP) system...

Collecting specialty-related medical terms: Development and evaluation of a resource for Spanish.

BMC medical informatics and decision making
BACKGROUND: Controlled vocabularies are fundamental resources for information extraction from clinical texts using natural language processing (NLP). Standard language resources available in the healthcare domain such as the UMLS metathesaurus or SNO...

Multi-domain clinical natural language processing with MedCAT: The Medical Concept Annotation Toolkit.

Artificial intelligence in medicine
Electronic health records (EHR) contain large volumes of unstructured text, requiring the application of information extraction (IE) technologies to enable clinical analysis. We present the open source Medical Concept Annotation Toolkit (MedCAT) that...

The quest for better clinical word vectors: Ontology based and lexical vector augmentation versus clinical contextual embeddings.

Computers in biology and medicine
BACKGROUND: Word vectors or word embeddings are n-dimensional representations of words and form the backbone of Natural Language Processing of textual data. This research experiments with algorithms that augment word vectors with lexical constraints ...

A clinical trials corpus annotated with UMLS entities to enhance the access to evidence-based medicine.

BMC medical informatics and decision making
BACKGROUND: The large volume of medical literature makes it difficult for healthcare professionals to keep abreast of the latest studies that support Evidence-Based Medicine. Natural language processing enhances the access to relevant information, an...

A pre-training and self-training approach for biomedical named entity recognition.

PloS one
Named entity recognition (NER) is a key component of many scientific literature mining tasks, such as information retrieval, information extraction, and question answering; however, many modern approaches require large amounts of labeled training dat...

Automatic MeSH Indexing: Revisiting the Subheading Attachment Problem.

AMIA ... Annual Symposium proceedings. AMIA Symposium
This year less than 200 National Library of Medicine indexers expect to index 1 million articles, and this would not be possible without the assistance of the Medical Text Indexer (MTI) system. MTI is an automated indexing system that provides MeSH m...