Identifying Condition-Action Statements in Medical Guidelines Using Domain-Independent Features
This work addresses the need for automated text understanding in medical guidelines, but it is incremental as it builds on prior methods with new datasets and baselines.
The paper tackled the problem of extracting condition-action pairs from medical guidelines by releasing two new annotated datasets and establishing machine learning baselines, achieving results that highlight limitations and potential extensions for text mining in this domain.
This paper advances the state of the art in text understanding of medical guidelines by releasing two new annotated clinical guidelines datasets, and establishing baselines for using machine learning to extract condition-action pairs. In contrast to prior work that relies on manually created rules, we report experiment with several supervised machine learning techniques to classify sentences as to whether they express conditions and actions. We show the limitations and possible extensions of this work on text mining of medical guidelines.