Generating Temporal Network Paths from Hospital Data

John Michael Finney, Laura Madrid Marquez

2016

Abstract

Using data from electronic medical records we were able to rapidly generate temporal network data. This data can then be loaded into a modern graph database and used to generate a temporal graph of the data. Using a specialist graph language for rapidly querying these graph databases, we are able to rapidly extract temporal path information about patient to patient contact networks based on shared ward encounters. This information can then be used to calculate various network statistics of interest that may be important for clinical use.

References

  1. Cusumano-Towner, M., Li, D., Tuo, S., Krishnan, G. and Maslove, D. (2013). A social network of hospital acquired infection built from electronic medical record data. Journal of the American Medical Informatics Association, 20(3), pp.427-434.
  2. Walker, A., Eyre, D., Wyllie, D., Dingle, K., Harding, R., O'Connor, L., Griffiths, D., Vaughan, A., Finney, J., Wilcox, M., Crook, D. and Peto, T. (2012). Characterisation of Clostridium difficile Hospital Ward Based Transmission Using Extensive Epidemiological Data and Molecular Typing. PLoS Med, 9(2), p.e1001172.
  3. Danon, L., Ford, A., House, T., Jewell, C., Keeling, M., Roberts, G., Ross, J. and Vernon, M. (2011). Networks and the Epidemiology of Infectious Disease. Interdisciplinary Perspectives on Infectious Diseases, 2011, pp.1-28.
  4. Barnes, S., Golden, B. and Wasil, E. (2010). A dynamic patient network model of hospital-acquired infections. Proceedings of the 2010 Winter Simulation Conference.
  5. Holme, P. and Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), pp.97-125.
  6. Valdano, E., Ferreri, L., Poletto, C., & Colizza, V. (2015). Analytical computation of the epidemic threshold on temporal networks. Physical Review X, 5(2), 021005.
  7. Masuda, N. and Holme, P. (2013). Predicting and controlling infectious disease epidemics using temporal networks. F1000Prime Rep, 5.
  8. Christley, R. (2005). Infection in Social Networks: Using Network Analysis to Identify High-Risk Individuals. American Journal of Epidemiology, 162(10), pp.1024- 1031.
  9. Cooper, B., Medley, G. and Scott, G. (1999). Preliminary analysis of the transmission dynamics of nosocomial infections: stochastic and management effects. Journal of Hospital Infection, 43(2), pp.131-147.
  10. Sun, W., Fokoue, A., Srinivas, K., Kementsietsidis, A., Hu, G., & Xie, G. (2015, May). SQLGraph: An Efficient Relational-Based Property Graph Store. In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data (pp. 1887-1901). ACM.
  11. Finney, J., Walker, A., Peto, T. and Wyllie, D. (2011). An efficient record linkage scheme using graphical analysis for identifier error detection. BMC Med Inform Decis Mak, 11(1), p.7.
  12. Taxiarchis Botsis, C. (2010). Secondary Use of EHR: Data Quality Issues and Informatics Opportunities. Summit on Translational Bioinformatics, [online] 2010, p.1. Available at: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC30415 34/ [Accessed 9 Sep. 2015].
  13. Ciglan, M., Averbuch, A. and Hluchy, L. (2012). Benchmarking Traversal Operations over Graph Databases. 2012 IEEE 28th International Conference on Data Engineering Workshops.
  14. Rodriguez, M. and Neubauer, P. (2010). The Graph Traversal Pattern. [online] Arxiv.org. Available at: http://arxiv.org/abs/1004.1001 [Accessed 9 Sep. 2015].
  15. Jim Webber. 2012. A programmatic introduction to Neo4j. In Proceedings of the 3rd annual conference on Systems, programming, and applications: software for humanity (SPLASH 7812). ACM, New York, NY, USA, 217-218.
  16. Gosling, J. (2000). The Java language specification. Addison-Wesley Professional.
  17. Holme, P. (2005). Network reachability of real-world contact sequences.Physical Review E, 71(4), 046119.
  18. Borgatti, S. P. (2005). Centrality and network flow. Social networks, 27(1), 55-71.
  19. Bell, D. C., Atkinson, J. S., & Carlson, J. W. (1999). Centrality measures for disease transmission networks. Social networks, 21(1), 1-21.
  20. Christley, R. M., Pinchbeck, G. L., Bowers, R. G., Clancy, D., French, N. P., Bennett, R., & Turner, J. (2005). Infection in social networks: using network analysis to identify high-risk individuals. American journal of epidemiology, 162(10), 1024-1031.
  21. Rocha, L. E., Liljeros, F., & Holme, P. (2011). Simulated epidemics in an empirical spatiotemporal network of 50,185 sexual contacts. PLoS Comput Biol, 7(3), e1001109.
  22. Wyllie, D., & Davies, J. (2015). Role of data warehousing in healthcare epidemiology. Journal of Hospital Infection, 89(4), 267-270.
  23. Easley, D., & Kleinberg, J. (2010). Networks, crowds, and markets: Reasoning about a highly connected world. Cambridge University Press.
  24. Holme, P., & Masuda, N. (2015). The basic reproduction number as a predictor for epidemic outbreaks in temporal networks. PloS one, 10(3), e0120567.
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Paper Citation


in Harvard Style

Finney J. and Madrid Marquez L. (2016). Generating Temporal Network Paths from Hospital Data . In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, (BIOSTEC 2016) ISBN 978-989-758-170-0, pages 263-268. DOI: 10.5220/0005669402630268


in Bibtex Style

@conference{healthinf16,
author={John Michael Finney and Laura Madrid Marquez},
title={Generating Temporal Network Paths from Hospital Data},
booktitle={Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, (BIOSTEC 2016)},
year={2016},
pages={263-268},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005669402630268},
isbn={978-989-758-170-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, (BIOSTEC 2016)
TI - Generating Temporal Network Paths from Hospital Data
SN - 978-989-758-170-0
AU - Finney J.
AU - Madrid Marquez L.
PY - 2016
SP - 263
EP - 268
DO - 10.5220/0005669402630268