Natural language processing (NLP) is a form of machine learning which enables the
processing and analysis of free text. When used with medical notes, it can aid in the …

The last decade has seen an exponential growth in the quantity of clinical data collected
nationwide, triggering an increase in opportunities to reuse the data for biomedical research …

Objective We implement 2 different multitask learning (MTL) techniques, hard parameter
sharing and cross-stitch, to train a word-level convolutional neural network (CNN) …

Physicians write clinical notes with abbreviations and shorthand that are difficult to decipher.
Abbreviations can be clinical jargon (writing “HIT” for “heparin induced thrombocytopenia”) …

Background Natural language processing (NLP) based clinical decision support systems
(CDSSs) have demonstrated the ability to extract vital information from patient electronic …

This study examined the use of neural word embeddings for clinical abbreviation
disambiguation, a special case of word sense disambiguation (WSD). We investigated three …

Unstructured data in the electronic health records contain essential patient information.
Natural language processing (NLP), teaching a computer to read, allows us to tap into these …

Objective: The goal of this study was to develop a practical framework for recognizing and
disambiguating clinical abbreviations, thereby improving current clinical natural language …

The recognition, disambiguation, and expansion of medical abbreviations and acronyms is
of upmost importance to prevent medically-dangerous misinterpretation in natural language …

Providing access to relevant biomedical literature in a clinical setting has the potential to
bridge a critical gap in evidence-based medicine. Here, our goal is specifically to provide …