Teaching probabilistic medical reasoning with the Elvira software

F.J. Díez

doi:10.1055/s-0038-1638186

Yearbook of Medical Informatics, Table of Contents

Yearb Med Inform 2004; 13(01): 175-180
DOI: 10.1055/s-0038-1638186

Research and Education

Georg Thieme Verlag KG Stuttgart

Teaching probabilistic medical reasoning with the Elvira software

F.J. Díez

¹Dept. Artificial Intelligence Universidad Nacional de Educación a Distancia Madrid, Spain

› Author Affiliations

Abstract

Abstract:

This paper briefly describes the main features of the UNED (Spanish National University for Distance Education) and the course on Probability and Statistics in Medicine. Then it introduces Bayesian networks and influence diagrams, two of the methods taught in this course. Finally, it explains how Elvira, a software package, can help the students to understand some difficult probabilistic concepts.

Full Text

References

References
1 Pearl J. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Mateo, CA: Morgan Kaufmann; 1988
2 Jensen FV. Bayesian Networks and Decision Graphs. New York: Springer; 2001
3 Neapolitan RE. Probabilistic Reasoning in Expert Systems: Theory and Algorithms. New York: Wiley-Interscience; 1990
4 Druzdzel MJ, van der Gaag LC. Building probabilistic networks: "Where do the numbers come from?” (Guest editors’ introduction). IEEE Transactions on Knowledge and Data Engineering 2000; 12: 481-6.
5 Druzdzel M, Díez FJ. Combining knowledge from different sources in probabilistic models. J Machine Learning Research 2003; 4: 295-316.
6 Sackett DL, Richardson WS, Rosenberg W, Haynes RB. Evidence-based Medicine. How to Practice & Teach EBM. 2nd ed. New York: Churchill Livingstone; 2000
7 Raiffa H, Schlaifer RO. Applied Statistical Decision Theory. Cambridge, MA: Harvard Business School; 1961
8 Howard RA, Matheson JE. Influence diagrams. In Howard RA, Matheson JE. editors Readings on the Principles and Applications of Decision Analysis. Menlo Park, CA: Strategic Decisions Group; 1984: 719-762.
9 Olmsted SM. On Representing and Solving Decision Problems. PhD thesis. Dept. Engineering-Economic Systems. Stanford University, CA; 1983
10 The Elvira Consortium. Elvira: An environment for creating and using probabilistic graphical models. Proceedings of the First European Workshop on Probabilistic Graphical Models (PGM’02). Cuenca, Spain: 2002. Nov.
11 Lacave C, Onisko A, Díez FJ. Debugging medical Bayesian networks with Elvira’s explanation facility. Conference on Artificial Intelligence in Medicine (AIME-2001), Workshop on Bayesian Models in Medicine. Cascais, Portugal: 2001. Jul.
12 Galán SF, Aguado F, Díez FJ, Mira J. NasoNet. Modelling the spread of nasopharyngeal cancer with temporal Bayesian networks. Artificial Intelligence in Medicine 2002; 25: 247-254.
13 Lacave C, Onisko A, Díez FJ. Knowledge acquisition in Prostanet, a Bayesian network for diagnosing prostate cancer. Seventh International Conference on Knowledge-Based Intelligent Information & Engineering Systems (KES-2003). Oxford, UK: 2003. Sep.