Abstract
Disease transmission network can provide important information for individuals to protect themselves and to support governments to prevent and control infectious diseases. Current studies on disease transmission network mostly focus on scenarios in small, confined areas. We propose to construct disease transmission network using health status time series computed based on health insurance claims. We adopted Granger causality tests to identify potential links from the health status time series from all pairs of individuals. We evaluated our approach by predicting future health care seeking activates for similar diseases based on past health care seeking activates of neighbors in the disease network. The results suggest that the transmission network is able to improve prediction performance in a small random sample of 500 individuals.
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Lu, HM., Chang, YC. (2017). Mining Disease Transmission Networks from Health Insurance Claims. In: Chen, H., Zeng, D., Karahanna, E., Bardhan, I. (eds) Smart Health. ICSH 2017. Lecture Notes in Computer Science(), vol 10347. Springer, Cham. https://doi.org/10.1007/978-3-319-67964-8_26
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DOI: https://doi.org/10.1007/978-3-319-67964-8_26
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