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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ray, B., Ghedin, E., Chunara, R.: Network inference from multimodal data: a review of approaches from infectious disease transmission. J. Biomed. Inform. 64, 44–54 (2016)
Teunis, P., Heijne, J.C.M., Sukhrie, F., van Eijkeren, J., Koopmans, M., Kretzschmar, M.: Infectious disease transmission as a forensic problem: who infected whom? J. Roy. Soc. Interface 10, 1–9 (2013)
Granger, C.W.J.: Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37, 424–438 (1969)
Hsieh, Y.-H.: Ascertaining the 2004–2006 HIV type 1 CRF07_BC outbreak among injecting drug users in taiwan. Int. J. Infect. Dis. 17, e838–e844 (2013)
Yang, X., Liu, J., Zhou, X.-N., Cheung, W.K.: Inferring disease transmission networks at a metapopulation level. Health Inf. Sci. Syst. 2, 8 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-67964-8_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67963-1
Online ISBN: 978-3-319-67964-8
eBook Packages: Computer ScienceComputer Science (R0)