Symptom-based data preprocessing for the detection of disease outbreak | IEEE Conference Publication | IEEE Xplore

Symptom-based data preprocessing for the detection of disease outbreak


Abstract:

Early warning systems for outbreak detection is a challenge topic for researchers in the epidemiology and biomedical informatics fields. We are proposing a new method for...Show More

Abstract:

Early warning systems for outbreak detection is a challenge topic for researchers in the epidemiology and biomedical informatics fields. We are proposing a new method for detecting disease epidemics using a symptom-based approach. The data was collected from developed mobile applications which include users' demographic information and a list of chief complaint symptoms. Deliberated outbreaks are differentiated from seasonal outbreak by specific symptoms that represent a sign of infection. These symptoms were grouped, classified, and then converted to a time-series digital signal using the consensus scoring approach. Through the syndromic grouping method, the system digitized each data package into a single independent variable that is ready for further one-dimensional signal processing to predict disease outbreaks in the future.
Date of Conference: 11-15 July 2017
Date Added to IEEE Xplore: 14 September 2017
ISBN Information:

ISSN Information:

PubMed ID: 29060435
Conference Location: Jeju, Korea (South)

References

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