Evidence map›Paper›PMID 28789686›Full record

ArticleBMC medical informatics and decision making2017

Developing a cardiovascular disease risk factor annotated corpus of Chinese electronic medical records.

Jia Su, Bin He, Yi Guan, Jingchi Jiang, Jinfeng Yang

Abstract read
In one paragraph

Article in BMC medical informatics and decision making, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Jia SuLanguage Technology Research Center, Harbin Institute of Technology, School of Computer Science and Technology, No. 92 West Dazhi Street, Harbin, Heilongjiang, China.
Bin HeLanguage Technology Research Center, Harbin Institute of Technology, School of Computer Science and Technology, No. 92 West Dazhi Street, Harbin, Heilongjiang, China.
Yi GuanLanguage Technology Research Center, Harbin Institute of Technology, School of Computer Science and Technology, No. 92 West Dazhi Street, Harbin, Heilongjiang, China. guanyi@hit.edu.cn.ORCID http://orcid.org/0000-0003-3618-6719
Jingchi JiangLanguage Technology Research Center, Harbin Institute of Technology, School of Computer Science and Technology, No. 92 West Dazhi Street, Harbin, Heilongjiang, China.
Jinfeng YangSchool of Software, Harbin University of Science and Technology, Harbin, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCardiovascular disease (CVD) has become the leading cause of death in China, and most of the cases can be prevented by controlling risk factors. The goal of this study was to build a corpus of CVD risk factor annotations based on Chinese electronic medical records (CEMRs). This corpus is intended to be used to develop a risk factor information extraction system that, in turn, can be applied as a foundation for the further study of the progress of risk factors and CVD.

resultsWe designed a light annotation task to capture CVD risk factors with indicators, temporal attributes and assertions that were explicitly or implicitly displayed in the records. The task included: 1) preparing data; 2) creating guidelines for capturing annotations (these were created with the help of clinicians); 3) proposing an annotation method including building the guidelines draft, training the annotators and updating the guidelines, and corpus construction. Meanwhile, we proposed some creative annotation guidelines: (1) the under-threshold medical examination values were annotated for our purpose of studying the progress of risk factors and CVD; (2) possible and negative risk factors were concerned for the same reason, and we created assertions for annotations; (3) we added four temporal attributes to CVD risk factors in CEMRs for constructing long term variations. Then, a risk factor annotated corpus based on de-identified discharge summaries and progress notes from 600 patients was developed. Built with the help of clinicians, this corpus has an inter-annotator agreement (IAA) F

conclusionTo the best of our knowledge, this is the first annotated corpus concerning CVD risk factors in CEMRs and the guidelines for capturing CVD risk factor annotations from CEMRs were proposed. The obtained document-level annotations can be applied in future studies to monitor risk factors and CVD over the long term.

Indexed as

Cardiovascular DiseasesElectronic Health RecordsInformation Storage and RetrievalNatural Language ProcessingChinaHumansRisk FactorsAnnotationCardiovascular disease risk factorsChinese electronic medical recordsCorpus constructionInformation extraction

Identifiers

PMID28789686
PMCPMC5549299

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.