Knowledge-guided Text Structuring in Clinical Trials

TL;DR

This paper introduces a knowledge-guided framework for structuring complex clinical trial texts into formal representations, enhancing information extraction for medical research and patient screening.

Contribution

It presents a novel method that uses an automatically generated knowledge base and dependency relations to improve parsing of complex clinical trial texts.

Findings

High precision and recall achieved in experiments

Effective transfer of free text into formal representations

Demonstrated efficiency of the proposed method

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.