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QAnalysis: a question-answer driven analytic tool on knowledge graphs for leveraging electronic medical records for clinical research
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, wher...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6444506/ https://www.ncbi.nlm.nih.gov/pubmed/30935389 http://dx.doi.org/10.1186/s12911-019-0798-8 |
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author | Ruan, Tong Huang, Yueqi Liu, Xuli Xia, Yuhang Gao, Ju |
author_facet | Ruan, Tong Huang, Yueqi Liu, Xuli Xia, Yuhang Gao, Ju |
author_sort | Ruan, Tong |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6444506 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-64445062019-04-11 QAnalysis: a question-answer driven analytic tool on knowledge graphs for leveraging electronic medical records for clinical research Ruan, Tong Huang, Yueqi Liu, Xuli Xia, Yuhang Gao, Ju BMC Med Inform Decis Mak Research Article 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. BioMed Central 2019-04-01 /pmc/articles/PMC6444506/ /pubmed/30935389 http://dx.doi.org/10.1186/s12911-019-0798-8 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Ruan, Tong Huang, Yueqi Liu, Xuli Xia, Yuhang Gao, Ju QAnalysis: a question-answer driven analytic tool on knowledge graphs for leveraging electronic medical records for clinical research |
title | QAnalysis: a question-answer driven analytic tool on knowledge graphs for leveraging electronic medical records for clinical research |
title_full | QAnalysis: a question-answer driven analytic tool on knowledge graphs for leveraging electronic medical records for clinical research |
title_fullStr | QAnalysis: a question-answer driven analytic tool on knowledge graphs for leveraging electronic medical records for clinical research |
title_full_unstemmed | QAnalysis: a question-answer driven analytic tool on knowledge graphs for leveraging electronic medical records for clinical research |
title_short | QAnalysis: a question-answer driven analytic tool on knowledge graphs for leveraging electronic medical records for clinical research |
title_sort | qanalysis: a question-answer driven analytic tool on knowledge graphs for leveraging electronic medical records for clinical research |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6444506/ https://www.ncbi.nlm.nih.gov/pubmed/30935389 http://dx.doi.org/10.1186/s12911-019-0798-8 |
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