CliNER 2.0: Accessible and Accurate Clinical Concept Extraction
This addresses the problem of extracting medical entities from clinical text for researchers and practitioners, but it is incremental as it builds on existing LSTM-based methods.
The authors tackled clinical concept extraction from clinical notes by developing CliNER 2.0, a tool using a word- and character-level LSTM model that achieves state-of-the-art performance, with pre-trained models provided for public use.
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.