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BMC Medical Informatics and Decision Making
Published in: BMC Medical Informatics and Decision Making 1/2019

Open Access 01-12-2019 | Research article

QAnalysis: a question-answer driven analytic tool on knowledge graphs for leveraging electronic medical records for clinical research

Authors: Tong Ruan, Yueqi Huang, Xuli Liu, Yuhang Xia, Ju Gao

Published in: BMC Medical Informatics and Decision Making | Issue 1/2019

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Abstract

Background

While doctors should analyze a large amount of electronic medical record (EMR) data to conduct clinical research, the analyzing process requires information technology (IT) skills, which is difficult for most doctors in China.

Methods

In this paper, we build a novel tool QAnalysis, where doctors enter their analytic requirements in their natural language and then the tool returns charts and tables to the doctors. For a given question from a user, we first segment the sentence, and then we use grammar parser to analyze the structure of the sentence. After linking the segmentations to concepts and predicates in knowledge graphs, we convert the question into a set of triples connected with different kinds of operators. These triples are converted to queries in Cypher, the query language for Neo4j. Finally, the query is executed on Neo4j, and the results shown in terms of tables and charts are returned to the user.

Results

The tool supports top 50 questions we gathered from two hospital departments with the Delphi method. We also gathered 161 questions from clinical research papers with statistical requirements on EMR data. Experimental results show that our tool can directly cover 78.20% of these statistical questions and the precision is as high as 96.36%. Such extension is easy to achieve with the help of knowledge-graph technology we have adopted. The recorded demo can be accessed from https://​github.​com/​NLP-BigDataLab/​QAnalysis-project.

Conclusion

Our tool shows great flexibility in processing different kinds of statistic questions, which provides a convenient way for doctors to get statistical results directly in natural language.
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Metadata
Title
QAnalysis: a question-answer driven analytic tool on knowledge graphs for leveraging electronic medical records for clinical research
Authors
Tong Ruan
Yueqi Huang
Xuli Liu
Yuhang Xia
Ju Gao
Publication date
01-12-2019
Publisher
BioMed Central
Published in
BMC Medical Informatics and Decision Making / Issue 1/2019
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/s12911-019-0798-8

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