About: Multilingual chief complaint classification for syndromic surveillance: An experiment with Chinese chief complaints

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About: Multilingual chief complaint classification for syndromic surveillance: An experiment with Chinese chief complaints Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Multilingual chief complaint classification for syndromic surveillance: An experiment with Chinese chief complaints
Creator	Zeng, Daniel King, Chwan-Chuen Shih, Fuh-Yuan Chen, Hsinchun Hsiao, Jin-Yi Lu, Hsin-Min Wu, Tsung-Shu
topic	covid:0429e5b02e2579de8c97e5874a218a455d906780#this
Source	Elsevier; Medline; PMC
abstract	PURPOSE: Syndromic surveillance is aimed at early detection of disease outbreaks. An important data source for syndromic surveillance is free-text chief complaints (CCs), which may be recorded in different languages. For automated syndromic surveillance, CCs must be classified into predefined syndromic categories to facilitate subsequent data aggregation and analysis. Despite the fact that syndromic surveillance is largely an international effort, existing CC classification systems do not provide adequate support for processing CCs recorded in non-English languages. This paper reports a multilingual CC classification effort, focusing on CCs recorded in Chinese. METHODS: We propose a novel Chinese CC classification system leveraging a Chinese-English translation module and an existing English CC classification approach. A set of 470 Chinese key phrases was extracted from about one million Chinese CC records using statistical methods. Based on the extracted key phrases, the system translates Chinese text into English and classifies the translated CCs to syndromic categories using an existing English CC classification system. RESULTS: Compared to alternative approaches using a bilingual dictionary and a general-purpose machine translation system, our approach performs significantly better in terms of positive predictive value (PPV or precision), sensitivity (recall), specificity, and F measure (the harmonic mean of PPV and sensitivity), based on a computational experiment using real-world CC records. CONCLUSIONS: Our design provides satisfactory performance in classifying Chinese CCs into syndromic categories for public health surveillance. The overall design of our system also points out a potentially fruitful direction for multilingual CC systems that need to handle languages beyond English and Chinese.
has issue date	2008-10-05(xsd:dateTime)
bibo:doi	10.1016/j.ijmedinf.2008.08.004
bibo:pmid	18838292
has license	no-cc
sha1sum (hex)	0429e5b02e2579de8c97e5874a218a455d906780
schema:url	https://doi.org/10.1016/j.ijmedinf.2008.08.004
resource representing a document's title	Multilingual chief complaint classification for syndromic surveillance: An experiment with Chinese chief complaints
has PubMed Central identifier	PMC7108263
has PubMed identifier	18838292
schema:publication	Int J Med Inform
resource representing a document's body	covid:0429e5b02e2579de8c97e5874a218a455d906780#body_text
is http://vocab.deri.ie/void#inDataset of	https://covidontheweb.inria.fr:4443/about/id/http/ns.inria.fr/covid19/0429e5b02e2579de8c97e5874a218a455d906780
is schema:about of	named entity 'syndromic surveillance' named entity 'CHINESE' named entity 'COMPLAINTS' named entity '308' named entity 'DATABASE' named entity 'S T' named entity 'MEANS' named entity 'FORM' named entity 'COVID-19' named entity 'ACTIVE' named entity 'USE' named entity 'CLASSIFICATION' named entity 'MULTILINGUAL' named entity 'EXTRACTION' named entity 'N A' named entity 'SYNDROMIC SURVEILLANCE' named entity 'EXPERIMENT' named entity 'LONG' named entity 'PERMISSIONS' named entity 'D I C' named entity 'RESOURCE' named entity 'CLASSIFICATION' named entity 'SYNDROMIC SURVEILLANCE' named entity 'RIGHTS' named entity 'REPOSITORIES' named entity 'ORIGINAL SOURCE' named entity 'CHIEF COMPLAINT' named entity 'UNRESTRICTED' named entity '320' named entity 'CENTRE' named entity 'FUNDED' named entity 'STATISTICAL' named entity 'CHIEF COMPLAINT' named entity 'R M' named entity 'PATTERN' named entity 'THESE' named entity 'A T' named entity 'FREE' named entity 'MULTILINGUAL' named entity 'RESEARCH' covid:arg/0429e5b02e2579de8c97e5874a218a455d906780 named entity 're-use' named entity 'Chinese characters' named entity 'gold standard' named entity 'web mining' named entity 'database' named entity 'Syndromic surveillance' named entity 'chief complaint' named entity 'Google Translation' named entity 'machine translation' named entity 'EMI' named entity 'ICD-9' named entity 'Machine translation' named entity 'UMLS' named entity 'Machine translation' named entity 'Chinese characters' named entity 'gold standard' named entity 'Emergency department' named entity '1, 2' named entity 'NLM' named entity 'search engines' named entity 'syndromic surveillance' named entity 'PPV' named entity 'Google Translation' named entity 'Google' named entity 'string matching' named entity 'true positive' named entity 'NLM' named entity 'triage' named entity 'Google Translation' named entity 'mutual information' named entity 'Centers for Disease Control and Prevention' named entity 'symptom'

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