  Clinical trial records are variable resources or the analysis of patients and
diseases. Information extraction from free text such as eligibility criteria
and summary of results and conclusions in clinical trials would better support
computer-based eligibility query formulation and electronic patient screening.
Previous research has focused on extracting information from eligibility
criteria, with usually a single pair of medical entity and attribute, but
seldom considering other kinds of free text with multiple entities, attributes
and relations that are more complex for parsing. In this paper, we propose a
knowledge-guided text structuring framework with an automatically generated
knowledge base as training corpus and word dependency relations as context
information to transfer free text into formal, computer-interpretable
representations. Experimental results show that our method can achieve overall
high precision and recall, demonstrating the effectiveness and efficiency of
the proposed method.
