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This paper advances the state of the art in text understanding of medicalguidelines by releasing two new annotated clinical guidelines datasets, andestablishing baselines for using machine learning to extract condition-actionpairs. In contrast to prior work that relies on manually created rules, wereport experiment with several supervised machine learning techniques toclassify sentences as to whether they express conditions and actions. We showthe limitations and possible extensions of this work on text mining of medicalguidelines.

Identifying Condition-Action Statements in Medical Guidelines Using Domain-Independent Features