NESTOR

NESTOR: A Computer-Based Medical Diagnostic Aid that Integrates Causal and Probabilistic Knowledge. n order to address some existing problems in computer-aided medical decision making, a computer program called NESTOR has been developed to aid physicians in determining the most likely diagnostic hypothesis to account for a set of patient findings. The domain of hypercalcemic disorders is used to test solution methods that should be applicable to other medical areas. A key design philosophy underlying NESTOR is that the physicians should have control of the computer interaction to determine what is done and when. In order to provide such controllable, interactive aid, specific technical tasks to be addressed. The unifying philosophy in addressing them is the use of knowledge-based methods within a formal probability theory framework. A user interface module gives the physician control over when and how these tasks are used to aid in diagnosing the cause of a patient’s condition. This dissertation presents the problems that are addressed by each of the three tasks, and the details of the methods used to address them. In addition, the results of an evaluation of the hypothesis scoring and search techniques are presented and discussed. Additional keywords: artificial intelligence; expert systems; medical applications; computer aided diagnosis; medical computer applications.


References in zbMATH (referenced in 25 articles )

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  1. Guo, Zhi-gao; Gao, Xiao-guang; Ren, Hao; Yang, Yu; Di, Ruo-hai; Chen, Da-qing: Learning Bayesian network parameters from small data sets: a further constrained qualitatively maximum a posteriori method (2017)
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  3. Kwisthout, Johan: Most probable explanations in Bayesian networks: complexity and tractability (2011)
  4. Feelders, Ad; van der Gaag, Linda C.: Learning Bayesian network parameters under order constraints (2006)
  5. Dagum, Paul; Luby, Michael: An optimal approximation algorithm for Bayesian inference (1997)
  6. Thöne, Helmut; Güntzer, Ulrich; Kießling, Werner: Increased robustness of Bayesian networks through probability intervals (1997)
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  8. Shimony, Solomon Eyal; Santos, Eugene jun.: Exploiting case-based independence for approximating marginal probabilities (1996)
  9. Chrisman, Lonnie: Incremental conditioning of lower and upper probabilities (1995)
  10. Cooper, Gregory F.: A Bayesian method for learning belief networks that contain hidden variables. (1995) ioport
  11. Shimony, Solomon Eyal: The role of relevance in explanation. II: Disjunctive assignments and approximate independence (1995)
  12. Charniak, Eugene; Shimony, Solomon Eyal: Cost-based abduction and MAP explanation (1994)
  13. Sy, Bon K.: An adaptive reasoning approach towards effficient ordering of composite hypotheses (1994)
  14. Dagum, Paul; Luby, Michael: Approximating probabilistic inference in Bayesian belief networks is NP- hard (1993)
  15. Shimony, Solomon Eyal: The role of relevance in explanation. I: Irrelevance as statistical independence (1993)
  16. Sy, Bon K.: A recurrence local computation approach towards ordering composite beliefs in Bayesian belief networks (1993)
  17. Neapolitan, Richard E.: The principle of interval constraints: A generalization of the symmetric Dirichlet distribution (1991)
  18. Cooper, Gregory F.: The computational complexity of probabilistic inference using Bayesian belief networks (1990)
  19. Suermondt, H. Jacques; Cooper, Gregory F.: Probabilistic inference in multiply connected belief networks using loop cutsets (1990)
  20. Wellman, Michael P.: Fundamental concepts of qualitative probabilistic networks (1990)

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