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Updating Stochastic Networks to Integrate Cross-Sectional and Longitudinal Studies

  • Allan TuckerEmail author
  • Yuanxi Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9105)

Abstract

Clinical trials are typically conducted over a population within a defined time period in order to illuminate certain characteristics of a health issue or disease process. These cross-sectional studies provide a snapshot of these disease processes over a large number of people but do not allow us to model the temporal nature of disease, which is essential for modelling detailed prognostic predictions. Longitudinal studies on the other hand, are used to explore how these processes develop over time in a number of people but can be expensive and time-consuming, and many studies only cover a relatively small window within the disease process. This paper explores the application of intelligent data analysis techniques for building reliable models of disease progression from both cross-sectional and longitudinal studies. The aim is to learn disease ‘trajectories’ from cross-sectional data by building realistic trajectories from healthy patients to those with advanced disease. We focus on exploring whether we can ‘calibrate’ models learnt from these trajectories with real longitudinal data using Baum-Welch re-estimation.

Keywords

Disease progression Cross-sectional studies Stochastic networks 

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References

  1. 1.
    Albert, P.S.: Longitudinal data analysis (repeated measures) in clinical trials. Statistics in Medicine 18(13), 1707–1732 (1999)CrossRefGoogle Scholar
  2. 2.
    Mann, C.J.: Observational research methods. research design ii: cohort, cross sectional, and case-control studies. Emergency Medicine Journal 20(1), 54–60 (2003)CrossRefGoogle Scholar
  3. 3.
    Siddiqui, Z.F., et al.: Predicting the post-treatment recovery of patients suffering from traumatic brain injury (TBI). Brain Informatics 2, 33–44 (2015)CrossRefGoogle Scholar
  4. 4.
    Frank, A., Asuncion, A.: UCI machine learning repository. Irvine: University of California, school of information and computer science (2010), http://archive.ics.uci.edu/ml (last accessed December 17, 2013)
  5. 5.
    Seber, G.A.F.: In Multivariate Observations. John Wiley and Sons, Hoboken (1984)CrossRefGoogle Scholar
  6. 6.
    Tucker, A., Garway-Heath, D.: The pseudo temporal bootstrap for predicting glaucoma from cross-sectional visual field data. IEEE Trans. IT Biomed. 14(1), 79–85 (2010)CrossRefGoogle Scholar
  7. 7.
    Li, Y., Swift, S., Tucker, A.: Modelling and analysing the dynamics of disease progression from cross-sectional studies. Journal of Biomedical Informatics 46(2), 266–274 (2013)CrossRefGoogle Scholar
  8. 8.
    Shen, R., et al.: Integrative Subtype Discovery in Glioblastoma Using iCluster. Plos One 7(4), e35236 (2012)Google Scholar
  9. 9.
    Inmon, W.H.: Building the Data Warehouse, 2nd edn. John Wiley and Sons (1996)Google Scholar
  10. 10.
    Murphy, K.: Dynamic Bayesian Networks: Representation, Inference and Learning, PhD Thesis, University of Califronia, Berkeley (2002)Google Scholar
  11. 11.
    Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 257–286 (1989)Google Scholar
  12. 12.
    Floyd, R.W.: Algorithm 97: shortest path. Communications of the ACM 5(6), 345 (1962)CrossRefGoogle Scholar
  13. 13.
    Kasza, J., Solomon, P.J.: A comparison of score-based methods for estimating Bayesian networks using the Kullback Leibler divergence. Communications in Statistics: Theory and Methods (2013), arXiv:1009.1463v2 (stat.ME)(in press)Google Scholar
  14. 14.
    Efron, B., Tibshirani, R.: An introduction to the bootstrap (monographs on statistics and applied probability). CRC Press, Boca Raton (1993)CrossRefGoogle Scholar
  15. 15.
    Bauer, D.F.: Constructing confidence sets using rank statistics. Journal of the American Statistical Association 67, 687–690 (1972)zbMATHCrossRefGoogle Scholar
  16. 16.
    Pocock, J., Stuart, L., Geller, N., Anastasios, A.T.: The Analysis of Multiple Endpoints in clinical trials. Biometrics 43, 487–498 (1987)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Computer ScienceBrunel UniversityMiddlesexUK

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