Knowledge-guided Text Structuring in Clinical Trials

• URL: http://arxiv.org/abs/1912.12380v1
• Date: Sat, 28 Dec 2019 01:12:15 GMT
• Title: Knowledge-guided Text Structuring in Clinical Trials
• Authors: Yingcheng Sun, Kenneth Loparo
• Abstract summary: We propose a knowledge-guided text structuring framework with an automatically generated knowledge base. Experimental results show that our method can achieve overall high precision and recall.
• Score: 0.38073142980733
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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