Abstract
In the current paper, the Promedas model for internal medicine, developed by our team, is introduced. The model is based on up-to-date medical knowledge and consists of approximately 2000 diagnoses, 1000 findings and 8600 connections between diagnoses and findings, covering a large part of internal medicine. We show that Belief Propagation (BP) can be successfully applied as approximate inference algorithm in the Promedas network. In some cases, however, we find errors that are too large for this application. We apply a recently developed method that improves the BP results by means of a loop expansion scheme. This method, termed Loop Corrected (LC) BP, is able to improve the marginal probabilities significantly, leaving a remaining error which is acceptable for the purpose of medical diagnosis.
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References
Pearl, J.: Probabilistic reasoning in intelligent systems: Networks of Plausible Inference. Morgan Kaufmann, San Francisco, California (1988)
Murphy, K.P., Weiss, Y., Jordan, M.I.: Loopy belief propagation for approximate inference: An empirical study. In: Proceedings of Uncertainty in AI, pp. 467–475 (1999)
Gallager, R.G.: Low-density parity check codes. MIT Press, Cambridge (1963)
McElice, R., MacKay, D., Cheng, J.: Turbo decoding as an instance of pearl’s belief propagation algorithm. Journal of Selected Areas of Communication 16, 140–152 (1998)
Freeman, W.T., Pasztor, E.C., Carmichael, O.T.: Learning low-level vision. Int. J. Comp. Vision 40, 25–47 (2000)
Mezard, M., Parisi, G., Zecchina, R.: Analytic and algorithmic solution of random satisfiability problems. Science 297 (2002)
Sun, J., Li, Y., Kang, S.B., Shum, H.-Y.: Symmetric stereo matching for occlusion handling. Proceedings CVPR 2, 399–406 (2005)
Jaakkola, T., Jordan, M.I.: Variational probabilistic inference and the QMR-DT network. Journal of artificial intelligence research 10, 291–322 (1999)
Shwe, M.A, Middleton, B., Heckerman, D.E., Henrion, M., Horvitz, E.J., Lehman, H.P., Cooper, G.F.: Probabilistic Diagnosis Using a Reformulation of the Internist-1/ QMR Knowledge Base. Methods of Information in Medicine 30, 241–255 (1991)
Heskes, T., Albers, K., Kappen, H.J.: Approximate inference and constraint optimisation. In: Proceedings UAI, pp. 313–320 (2003)
Mooij, J.M., Wemmenhove, B., Kappen, H.J., Rizzo, T.: Loop corrected belief propagation. In: Proceedings of AISTATS (2007)
Lauritzen, S.L., Spiegelhalter, D.J.: Local computations with probabilties on graphical structures and their application to expert systems. J. Royal Statistical society B 50, 154–227 (1988)
Heckerman, D.: A tractable inference algorithm for diagnosing multiple diseases. In: Proceedings UAI, pp. 163–171. Elsevier, Amsterdam (1989)
Takinawa, M., D’Ambrosio, B.: Multiplicative factorization of noisy-MAX. In: Proceedings of the 15th Conference on Uncertainty in Artificial Intelligence UAI 1999, pp. 622–30 (1999)
Montanari, A., Rizzo, T.: How to compute loop corrections to the bethe approximation. Journal of Statistical Mechanics P10011 (2005)
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Wemmenhove, B., Mooij, J.M., Wiegerinck, W., Leisink, M., Kappen, H.J., Neijt, J.P. (2007). Inference in the Promedas Medical Expert System. In: Bellazzi, R., Abu-Hanna, A., Hunter, J. (eds) Artificial Intelligence in Medicine. AIME 2007. Lecture Notes in Computer Science(), vol 4594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73599-1_61
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DOI: https://doi.org/10.1007/978-3-540-73599-1_61
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