  Clinical notes often describe important aspects of a patient's stay and are
therefore critical to medical research. Clinical concept extraction (CCE) of
named entities - such as problems, tests, and treatments - aids in forming an
understanding of notes and provides a foundation for many downstream clinical
decision-making tasks. Historically, this task has been posed as a standard
named entity recognition (NER) sequence tagging problem, and solved with
feature-based methods using handengineered domain knowledge. Recent advances,
however, have demonstrated the efficacy of LSTM-based models for NER tasks,
including CCE. This work presents CliNER 2.0, a simple-to-install, open-source
tool for extracting concepts from clinical text. CliNER 2.0 uses a word- and
character- level LSTM model, and achieves state-of-the-art performance. For
ease of use, the tool also includes pre-trained models available for public
use.
