Multilingual chief complaint classification for syndromic surveillance: an experiment with Chinese chief complaints.
OBJECTIVE
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.
Lu
Hsin-Min
HM
Management Information Systems Department, Eller College of Management, University of Arizona, 1130 East Helen Street, McClelland Hall 430, Tucson, Arizona 85721, USA. hmlu@email.arizona.edu
Chen
Hsinchun
H
Zeng
Daniel
D
King
Chwan-Chuen
CC
Shih
Fuh-Yuan
FY
Wu
Tsung-Shu
TS
Hsiao
Jin-Yi
JY
D002681:China / Q000453:epidemiology; D004196:Disease Outbreaks; D006801:Humans; D007802:Language; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D018571:Sentinel Surveillance