Advertisement

Multi-domain and multi-view networks model for clustering hospital admissions from the emergency department

  • Nouf Albarakati
  • Zoran Obradovic
Regular Paper
  • 32 Downloads

Abstract

As the healthcare industry continues to generate a massive amount of medical data, healthcare organizations integrate data-driven insights into their clinical and operational processes to enhance the quality of healthcare services. Our preliminary hospital clustering analysis (Albarakati and Obradovic, in The IEEE 29th international symposium on computer-based medical systems (CBMS), IEEE, 2017) studied hospitals monthly admission behavior for different diseases. Results showed consistent behavior when disease symptoms similarity is considered. This study extends our preliminary work to include other aspects of disease data and the fusion of different views of disease data. It is an original approach that tackles clustering complex networks using a combination of multi-view and multi-domain clustering models while imposing data on the clustering goal from both medical and non-medical domains simultaneously. The objective of the study is to determine the effect of disease networks on characterizing the underlying clustering structure of 145 disease-specific hospital networks, each consisting of up to 152 hospitals. This is achieved by extracting two different views of disease networks. One disease network view based on similarity of symptom profiles was extracted from a 20 million medical bibliographic literature records. Another disease network view based on monthly hospitalization distribution was extracted from over 7 million discharge records data obtained from the California State Inpatient Database for years 2009–2011. Patient admission records included both medical and sociodemographic information. These multiple views were analyzed separately and were also integrated in a joint model that combined the two views. It is shown that the fusion of multi-view disease networks of monthly hospitalization distributions explained the hidden common structure shared among multiple hospital-specific disease networks. The group homogeneity measures for obtained hospital clusters ranged between 33 and 60% with average close to 50%. However, integrating multiple views of disease networks extracted from different domains, i.e., from literature and medical databases, better revealed the underlying clustering structure of disease-specific hospital networks. The group homogeneity measures for this multi-domain setting ranged between 38 and 76% with average close to 60%.

Keywords

Hospital clustering Disease-based Multi-view Multi-domain Homogeneity analysis 

Notes

Acknowledgements

This research was supported in part by NSF BIGDATA Grant 14476570. Healthcare Cost and Utilization Project (HCUP), Agency for Healthcare Research and Quality, provided part of the data used in this study.

References

  1. 1.
    Albarakati, N., Obradovic, Z.: Disease-based clustering of hospital admission: disease network of hospital networks approach. In: The IEEE 29th International Symposium on Computer Based Medical Systems (CBMS). IEEE (2017)Google Scholar
  2. 2.
    Belciug, S., Gorunescu, F.: Improving hospital bed occupancy and resource utilization through queuing modeling and evolutionary computation. J. Biomed. Inform. 53, 261–269 (2015)CrossRefGoogle Scholar
  3. 3.
    Bourgeois, F.T., Monuteaux, M.C., Stack, A.M., Neuman, M.I.: Variation in emergency department admission rates in us children’s hospitals. Pediatrics 134(3), 539–545 (2014)CrossRefGoogle Scholar
  4. 4.
    Delamater, P.L., Shortridge, A.M., Messina, J.P.: Regional health care planning: a methodology to cluster facilities using community utilization patterns. BMC Health Serv. Res. 13(1), 333 (2013)CrossRefGoogle Scholar
  5. 5.
    Glass, J., Ghalwash, M.F., Vukicevic, M., Obradovic, Z.: Extending the modelling capacity of Gaussian conditional random fields while learning faster. In: AAAI, pp. 1596–1602 (2016)Google Scholar
  6. 6.
    Gligorijevic, V., Panagakis, Y., Zafeiriou, S.: Non-negative matrix factorizations for multiplex network analysis. arXiv:1612.00750 (2016)
  7. 7.
    Groves, P., Kayyali, B., Knott, D., Van Kuiken, S.: The big data revolution in healthcare: accelerating value and innovation. McKinsey Q. 2, 3 (2013)Google Scholar
  8. 8.
    Khojah, I., Li, S., Luo, Q., Davis, G., Galarraga, J.E., Granovsky, M., Litvak, O., Davis, S., Shesser, R., Pines, J.M.: The relative contribution of provider and ED-level factors to variation among the top 15 reasons for ED admission. Am. J. Emerg. Med. 35(9), 1291–1297 (2017).  https://doi.org/10.1016/j.ajem.2017.03.074
  9. 9.
    Kivelä, M., Arenas, A., Barthelemy, M., Gleeson, J.P., Moreno, Y., Porter, M.A.: Multilayer networks. J. Complex Netw. 2(3), 203–271 (2014)CrossRefGoogle Scholar
  10. 10.
    McMahon Jr., L.F., Wolfe, R.A., Tedeschi, P.J.: Variation in hospital admissions among small areas: a comparison of Maine and Michigan. Med. Care 27, 623–631 (1989)CrossRefGoogle Scholar
  11. 11.
    Moni, M.A., Liò, P.: Network-based analysis of comorbidities risk during an infection: SARS and HIV case studies. BMC Bioinform. 15(1), 333 (2014)CrossRefGoogle Scholar
  12. 12.
    Ni, J., Tong, H., Fan, W., Zhang, X.: Inside the atoms: ranking on a network of networks. In: Proceedings of the 20th SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1356–1365. ACM (2014)Google Scholar
  13. 13.
    Ni, J., Tong, H., Fan, W., Zhang, X.: Flexible and robust multi-network clustering. In: Proceedings of the 21st SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 835–844. ACM (2015)Google Scholar
  14. 14.
    Phillip, P.J., Mullner, R., Andes, S.: Toward a better understanding of hospital occupancy rates. Health Care Financ. Rev. 5(4), 53 (1984)Google Scholar
  15. 15.
    Raghupathi, W., Raghupathi, V.: Big data analytics in healthcare: promise and potential. Health Inf. Sci. Syst. 2(1), 3 (2014)CrossRefGoogle Scholar
  16. 16.
    Reid, F.D., Cook, D.G., Majeed, A.: Explaining variation in hospital admission rates between general practices: cross sectional study. BMJ 319(7202), 98–103 (1999)CrossRefGoogle Scholar
  17. 17.
    Sabbatini, A.K., Nallamothu, B.K., Kocher, K.E.: Reducing variation in hospital admissions from the emergency department for low-mortality conditions may produce savings. Health Aff. 33(9), 1655–1663 (2014)CrossRefGoogle Scholar
  18. 18.
    Shay, P.D.: More ore than just hospitals: an examination of cluster components and configurations. Ph.D. dissertation, Health Services Organization and Research Department, Virginia Commonwealth University (2014)Google Scholar
  19. 19.
    Thomas, J.W., Griffith, J.R., Durance, P.: Defining hospital clusters and associated service communities in metropolitan areas. Socioecon. Plan. Sci. 15(2), 45–51 (1981)CrossRefGoogle Scholar
  20. 20.
    Zhou, X., Menche, J., Barabási, A.L., Sharma, A.: Human symptoms-disease network. Nat. Commun. 5, 4212 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Center for Data Analytics and Biomedical InformaticsTemple UniversityPhiladelphiaUSA
  2. 2.Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia

Personalised recommendations