@misc{boag2018cliner,
author = {Boag, Willie and Sergeeva, Elena and Kulshreshtha, Saurabh and Szolovits, Peter and Rumshisky, Anna and Naumann, Tristan},
title = {CliNER 2.0: Accessible and Accurate Clinical Concept Extraction},
year = {2018},
month = {March},
abstract = {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.},
url = {http://approjects.co.za/?big=en-us/research/publication/cliner-2-0-accessible-and-accurate-clinical-concept-extraction/},
}