Skip to main content
SpringerLink
Log in

Using Open Clinical Data to Create an Embeddable Prediction System for Hospital Stay

  • Conference paper
  • First Online:
Computing, Analytics and Networks (ICAN 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 805))

Included in the following conference series:

  • 524 Accesses

Abstract

With the ever increasing availability of open, clinical health data, there exists a deficiency of platforms to take advantage of it [1]. The global prevalence of diabetes has risen from 4.7% in 1980 to 8.5% in 2014 and continues to rise, placing an increased demand on hospital resources [2]. The management of diabetic patients within hospital can be assisted by the accurate prediction of length of stay (LOS) of patients. This paper introduces the use of Bayesian networks (BN) to accurately predict patient LOS in hospital. The results show the tree augmented naive BN classifier to be the most effective in predicting LOS. We believe that our model can be implemented by hospitals to more efficiently utilize hospital resources.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 74.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Lichman, M.: UCI machine learning repository (2013)

    Google Scholar 

  2. World Health Organization, et al.: Global report on diabetes. World Health Organization (2016)

    Google Scholar 

  3. OECD Indicators. Health at a glance (2005)

    Google Scholar 

  4. Marshall, A., Vasilakis, C., El-Darzi, E.: Length of stay-based patient flow models: recent developments and future directions. Health Care Manag. Sci. 8(3), 213–220 (2005)

    Article  Google Scholar 

  5. Damiani, G., Pinnarelli, L., Sommella, L., Vena, V., Magrini, P., Ricciardi, W.: The short stay unit as a new option for hospitals: a review of the scientific literature. Med. Sci. Monitor Int. Med. J. Exp. Clin. Res. 17(6), SR15 (2011)

    Article  Google Scholar 

  6. Escobar, G.J., Greene, J.D., Gardner, M.N., Marelich, G.P., Quick, B., Kipnis, P.: Intra-hospital transfers to a higher level of care: contribution to total hospital and intensive care unit (ICU) mortality and length of stay (LOS). J. Hosp. Med. 6(2), 74–80 (2011)

    Article  Google Scholar 

  7. Constantinou, A.C., Fenton, N., Marsh, W., Radlinski, L.: From complex questionnaire and interviewing data to intelligent Bayesian network models for medical decision support. Artif. Intell. Med. 67, 75–93 (2016)

    Article  Google Scholar 

  8. Strack, B., DeShazo, J.P., Gennings, C., Olmo, J.L., Ventura, S., Cios, K.J., Clore, J.N.: Impact of HbA1c measurement on hospital readmission rates: analysis of 70,000 clinical database patient records. BioMed. Res. Int. 2014, 1–11 (2014)

    Article  Google Scholar 

  9. Cai, X., Perez-Concha, O., Coiera, E., Martin-Sanchez, F., Day, R., Roffe, D., Gallego, B.: Real-time prediction of mortality, readmission, and length of stay using electronic health record data. J. Am. Med. Inform. Assoc. 23(3), 553–561 (2015)

    Article  Google Scholar 

  10. Pitkäaho, T., Partanen, P., Miettinen, M., Vehviläinen-Julkunen, K.: Non-linear relationships between nurse staffing and patients? Length of stay in acute care units: Bayesian dependence modelling. J. Adv. Nurs. 71(2), 458–473 (2015)

    Article  Google Scholar 

  11. Cho, I., Park, I., Kim, E., Lee, E., Bates, D.W.: Using EHR data to predict hospital-acquired pressure ulcers: a prospective study of a Bayesian network model. Int. J. Med. Inform. 82(11), 1059–1067 (2013)

    Article  Google Scholar 

  12. Marshall, A.H., McClean, S.I.: Conditional phase-type distributions for modelling patient length of stay in hospital. Int. Trans. Oper. Res. 10(6), 565–576 (2003)

    Article  Google Scholar 

  13. Marshall, A.H., McClean, S.I., Shapcott, C.M., Millard, P.H.: Modelling patient duration of stay to facilitate resource management of geriatric hospitals. Health Care Manag. Sci. 5(4), 313–319 (2002)

    Article  Google Scholar 

  14. Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Mach. Learn. 29(2–3), 131–163 (1997)

    Article  Google Scholar 

  15. Tsamardinos, I., Brown, L.E., Aliferis, C.F.: The max-min hill-climbing Bayesian network structure learning algorithm. Mach. Learn. 65(1), 31–78 (2006)

    Article  Google Scholar 

  16. Tsamardinos, I., Aliferis, C.F., Statnikov, A.: Time and sample efficient discovery of Markov blankets and direct causal relations. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 673–678. ACM (2003)

    Google Scholar 

  17. Tsamardinos, I., Aliferis, C.F., Statnikov, A.R., Statnikov, E.: Algorithms for large scale Markov blanket discovery. In: FLAIRS Conference, vol. 2, pp. 376–380 (2003)

    Google Scholar 

Download references

Acknowledgement

The work is partially supported by Dr. Mago’s NSERC Discovery Grant. All authors also like to thank Darryl Willick for his support to run the experiments on High Performance Computing Lab at Lakehead University.

Author information

Authors and Affiliations

  1. Lakehead University, Thunder Bay, ON, P7B 5E1, Canada

    Dillon Small, Fahad Wali, Christopher M. Gibb & Vijay Mago

  2. Probe Development and Biomarker Exploration, Thunder Bay Regional Health Research Institute, Thunder Bay, ON, P7B 6V4, Canada

    Christopher M. Gibb

Authors
  1. Dillon Small
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fahad Wali
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Christopher M. Gibb
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Vijay Mago
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Dillon Small , Fahad Wali , Christopher M. Gibb or Vijay Mago .

Editor information

Editors and Affiliations

  1. Chitkara University, Chandigarh, India

    Rajnish Sharma

  2. Chitkara University, Chandigarh, India

    Archana Mantri

  3. Department of Computer Science and Electrical Engineering, Louisiana Tech University, Ruston, Louisiana, USA

    Sumeet Dua

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Small, D., Wali, F., Gibb, C.M., Mago, V. (2018). Using Open Clinical Data to Create an Embeddable Prediction System for Hospital Stay. In: Sharma, R., Mantri, A., Dua, S. (eds) Computing, Analytics and Networks. ICAN 2017. Communications in Computer and Information Science, vol 805. Springer, Singapore. https://doi.org/10.1007/978-981-13-0755-3_2

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-0755-3_2

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-0754-6

  • Online ISBN: 978-981-13-0755-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation