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

  • Dillon SmallEmail author
  • Fahad WaliEmail author
  • Christopher M. GibbEmail author
  • Vijay MagoEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 805)


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.


Length of stay Bayesian network Health care system Diabetes 



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.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Lakehead UniversityThunder BayCanada
  2. 2.Probe Development and Biomarker ExplorationThunder Bay Regional Health Research InstituteThunder BayCanada

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