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Logical Analysis of Computed Tomography Data to Differentiate Entities of Idiopathic Interstitial Pneumonias

  • M. W. Brauner
  • N. Brauner
  • P. L. Hammer
  • I. Lozina
  • D. Valeyre
Part of the Springer Optimization and Its Applications book series (SOIA, volume 7)

Abstract

The aim of this chapter is to analyze computed tomography (CT) data by using the Logical Analysis of Data (LAD) methodology in order to distinguish between three types of idiopathic interstitial pneumonias (IIPs). The chapter demonstrates that LAD can distinguish different forms of IIPs with high accuracy It shows also that the patterns developed by LAD techniques provide additional information about outliers, redundant features, the relative significance of attributes, and makes possible the identification of promoters and blockers of various forms of IIPs.

Keywords

Idiopathic Pulmonary Fibrosis Logical Analysis Idiopathic Pulmonary Fibrosis Patient Polygonal Line Negative Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    G. Alexe, S. Alexe, P.L. Hammer, L. Liotta, E. Petricoin, and M. Reiss. Logical Analysis of Proteomic Ovarian Cancer Dataset. Proteomics, 4: 766–783, 2004.PubMedCrossRefGoogle Scholar
  2. 2.
    S. Alexe, E. Blackstone, P.L. Hammer, H. Ishwaran, M.S. Lauer, and C.E.P. Snader. Coronary Risk Prediction by Logical Analysis of Data. Annals of Operations Research, 119: 15–42, 2003.CrossRefGoogle Scholar
  3. 3.
    S. Alexe and P.L. Hammer. Pattern-Based Discriminants in the Logical Analysis of Data. In this Volume.Google Scholar
  4. 4.
    American Thoracic Society / European Respiratory Society International Multidisciplinary Consensus. Classification of the Idiopathic Interstitial Pneumonias. American Journal of Respiratory and Critical Care Medicine, 165: 277–304, 2002.Google Scholar
  5. 5.
    E. Boros, P.L. Hammer, T. Ibaraki, and A. Kogan. Logical Analysis of Numerical Data. Mathematical Programming, 79: 163–190, 1997.Google Scholar
  6. 6.
    E. Boros, P.L. Hammer, T. Ibaraki, A. Kogan, E. Mayoraz, and I. Muchnik. An Implementation of the Logical Analysis of Data. IEEE Transactions on Knowledge and Data Engineering, 12(2): 292–306, 2000.CrossRefGoogle Scholar
  7. 7.
    Y. Crama, P.L. Hammer, and T. Ibaraki. Cause-Effect Relationships and Partially Defined Boolean Functions. Annals of Operations Research, 16: 299–326, 1988.CrossRefGoogle Scholar
  8. 8.
    P.L. Hammer. Partially Defined Boolean Functions and Cause-Effect Relationships. International Conference on Multi-Attribute Decision Making Via OR-Based Expert Systems, University of Passau, Passau, Germany, 1986.Google Scholar
  9. 9.
    T.E. Hartman, S.J. Swensen, D.M. Hansell, T.V. Colby, J.L. Myers, H.D. Tazelaar, A.G. Nicholson, A.U. Wells, J.H. Ryu, D.E. Midthun, R.M. du Bois, and N.L. Muller. Nonspecific Interstitial Pneumonia: Variable Appearance at High-Resolution Chest CT. Radiology, 217(3): 701–705, 2000.PubMedGoogle Scholar
  10. 10.
    M.S. Lauer, S. Alexe, C.E.P. Snader, E. Blackstone, H. Ishwaran, and P.L. Hammer. Use of the “Logical Analysis of Data” Method for Assessing Long-Term Mortality Risk After Exercise Electrocardiography. Circulation, 106: 685–690, 2002.PubMedGoogle Scholar
  11. 11.
    T. Johkoh, N.L. Muller, Y. Cartier, P.V. Kavanagh, T.E. Hartman, M. Akira, K. Ichikado, M. Ando, and H. Nakamura. Idiopathic Interstitial Pneumonias: Diagnostic Accuracy of Thin-Section CT in 129 Patients. Radiology, 211(2): 555–560, 1999.PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • M. W. Brauner
    • 1
  • N. Brauner
    • 2
  • P. L. Hammer
    • 3
  • I. Lozina
    • 3
  • D. Valeyre
    • 4
  1. 1.Department of Radiology, Fédération MARTHA, UFR BobignyUniversité Paris 13 et Hôpital Avicenne AP-HPBobigny CedexFrance
  2. 2.Laboratoire Leibniz-IMAGGrenoble CedexFrance
  3. 3.RUTCORRutgers UniversityPiscatawayUSA
  4. 4.Department of Pneumology, Fédération MARTHA, UFR BobignyUniversité Paris 13 et Hôpital Avicenne AP-HPBobigny CedexFrance

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