Document collections resulting from searches in the biomedical literature, for instance, in PubMed, are often so large that some organization of the returned information is necessary. Clustering is an efficient tool for organizing search results. To ...
OBJECTIVE: Terminologies and terminological systems have assumed important roles in many medical information processing environments, giving rise to the "big knowledge" challenge when terminological content comprises tens of thousands to millions of ...
OBJECTIVE: Data in electronic health records (EHRs) is being increasingly leveraged for secondary uses, ranging from biomedical association studies to comparative effectiveness. To perform studies at scale and transfer knowledge from one institution ...
Identifying unknown drug interactions is of great benefit in the early detection of adverse drug reactions. Despite existence of several resources for drug-drug interaction (DDI) information, the wealth of such information is buried in a body of unst...
BACKGROUND: Semantic approaches such as concept-based information retrieval rely on a corpus in which resources are indexed by concepts belonging to a domain ontology. In order to keep such applications up-to-date, new entities need to be frequently ...
BACKGROUND: Controlled vocabularies such as the Unified Medical Language System (UMLS) and Medical Subject Headings (MeSH) are widely used for biomedical natural language processing (NLP) tasks. However, the standard terminology in such collections s...
BACKGROUND: The need to create controlled vocabularies such as ontologies for knowledge organization and access has been widely recognized in various domains. Despite the indispensable need of thorough domain knowledge in ontology construction, most ...
BACKGROUND: Gene Ontology (GO) has been used widely to study functional relationships between genes. The current semantic similarity measures rely only on GO annotations and GO structure. This limits the power of GO-based similarity because of the li...
BACKGROUND: Full syntactic parsing of clinical text as a part of clinical natural language processing (NLP) is critical for a wide range of applications. Several robust syntactic parsers are publicly available to produce linguistic representations fo...
OBJECTIVE: The main goal of this work is to measure how lexical regularities in biomedical ontology labels can be used for the automatic creation of formal relationships between classes, and to evaluate the results of applying our approach to the Gen...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.