Background Natural language processing applied to clinical text or aimed at a clinical
outcome has been thriving in recent years. This paper offers the first broad overview of …

Abstract Biomedical Entity Linking (BEL) is the task of mapping of spans of text within
biomedical documents to normalized, unique identifiers within an ontology. This is an …

Objective This paper aims to propose knowledge-aware embedding, a critical tool for
medical term normalization. Methods We develop CODER (Cross-lingual knowledge …

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 …

Introduction Clinical data warehouses are often oriented toward integration and exploration
of coded data. However narrative reports are of crucial importance for translational research …

Obtaining text datasets with semantic annotations is an effortful process, yet crucial for
supervised training in natural language processing (NLP). In general, developing and …

The abundance and unstructured nature of biomedical texts, be it clinical or research
content, impose significant challenges for the effective and efficient use of information and …

We present a statistical model, GERNERMED++, for German medical natural language
processing trained for named entity recognition (NER) as an open, publicly available model …

Here we describe a new clinical corpus rich in nested entities and a series of neural models
to identify them. The corpus comprises de-identified referrals from the waiting list in Chilean …

Abstract Background Semantic annotators and Natural Language Processing (NLP)
methods for Named Entity Recognition and Linking (NER+ L) require plenty of training and …