Structured prediction models for RNN based sequence labeling in clinical text
This work addresses information extraction from Electronic Health Records for clinical applications, but it is incremental as it builds on existing LSTM-CRF methods.
The authors tackled the problem of extracting medical entities from clinical text by extending LSTM-CRF models with pairwise potentials and skip-chain CRF inference, resulting in improved exact phrase detection for entities like medication and side-effects.
Sequence labeling is a widely used method for named entity recognition and information extraction from unstructured natural language data. In clinical domain one major application of sequence labeling involves extraction of medical entities such as medication, indication, and side-effects from Electronic Health Record narratives. Sequence labeling in this domain, presents its own set of challenges and objectives. In this work we experimented with various CRF based structured learning models with Recurrent Neural Networks. We extend the previously studied LSTM-CRF models with explicit modeling of pairwise potentials. We also propose an approximate version of skip-chain CRF inference with RNN potentials. We use these methodologies for structured prediction in order to improve the exact phrase detection of various medical entities.