Knowledge-guided Text Structuring in Clinical Trials
This work addresses the need for better computer-based eligibility query formulation and electronic patient screening in clinical trials, representing an incremental improvement over previous methods that focused on simpler text.
The paper tackled the problem of extracting complex information from various free-text sections of clinical trial records, such as eligibility criteria and results, by proposing a knowledge-guided text structuring framework. The method achieved high precision and recall in experiments, demonstrating its effectiveness and efficiency.
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.