Symptom-based data preprocessing for the detection of disease outbreak

• Main Authors: Khanita Duangchaemkarn, Varin Chaovatut, Phongtape Wiwatanadate, Ekkarat Boonchieng
• Format: Conference Proceeding
• Published: 2018
• Subjects: Computer Science; Engineering; Medicine
• Online Access: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85032221215&origin=inward; http://cmuir.cmu.ac.th/jspui/handle/6653943832/57054
• Institution: Chiang Mai University

© 2017 IEEE. 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.