We investigate the current extent of term reuse and overlap among biomedical ontologies.
We use the corpus of biomedical ontologies stored in the BioPortal repository, and analyze …

Biomedical terminologies play a vital role in managing biomedical data. Missing IS-A
relations in a biomedical terminology could be detrimental to its downstream usages. In this …

Background Named entity recognition is critical for biomedical text mining, where it is not
unusual to find entities labeled by a wide range of different terms. Nowadays, ontologies are …

Objective and background: The exponential growth of the unstructured data available in
biomedical literature, and Electronic Health Record (EHR), requires powerful novel …

The ShARe/CLEF eHealth Evaluation Lab (SHEL) organized a challenge on natural
language processing (NLP) and information retrieval (IR) in the medical domain in 2013 …

Crucial information about the practice of healthcare is recorded only in free-form text, which
creates an enormous opportunity for high-impact NLP. However, annotated healthcare …

Modern medical vocabularies can contain up to hundreds of thousands of concepts. In any
particular use-case only a small fraction of these will be needed. In this paper we first define …

While clinical text NLP systems have become very effective in recognizing named entities in
clinical text and mapping them to standardized terminologies in the normalization process …

Motivation Methods for concept recognition (CR) in clinical texts have largely been tested on
abstracts or articles from the medical literature. However, texts from electronic health records …

The biomedical literature presents a uniquely challenging text mining problem. Sentences
are long and complex, the subject matter is highly specialized with a distinct vocabulary, and …