Title: Identifying Condition-Action Statements in Medical Guidelines Using Domain-Independent Features,
Abstract: 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.