Probabilistic Diagnosis Using a Reformulation of the INTERNIST-1/QMR Knowledge Base

By Michael Shwe, Blackford Middleton, David E. Heckerman, Max Henrion, Eric Horvitz, Harold P. Lehmann, Gregory F. Cooper. 1991

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Citations in the corpus

Improving the accuracy of medical diagnosis with causal machine learning (2020, methodology): “The most widely used of these correspond to ‘ noisy-OR ’ models 19 .”
Learning Bayesian network parameters from small data sets: application of Noisy-OR gates (2001, methodology): “Noisy-OR gates are usually used to describe the interaction between Ò causes ½, ¾, , Ò and their common effect .”
Computing upper and lower bounds on likelihoods in intractable networks (1996, background): “Although a serious restriction, there are nonethe-less many potential applications for such upper bounds,including the probabilistic reformulation of the QMRknowledge base (Shwe et al., 1991).”
Structured Priors for Structure Learning (2006, background): “The QMR-DT [Shwe et al., 1991] or Hepar II [Kraaijeveld et al., 2005] networks, for example, contain highly regular but largely nonmodular structure, and we would like to be able to discover both this sort of network and a good characterization of this sort of regularity.”
Recognition Networks for Approximate Inference in BN20 Networks (2001, background): “The difference arises partially because of invalid conditional independence assumptions encoded in the structure of the QMR-DT (see [Shwe et al., 1991] for a discussion of the effect of these simplifying assump tions).”
Unsupervised Learning of Noisy-Or Bayesian Networks (2013, methodology): “Networks of this form are frequently used models for expert systems and include the well-known Quick Medical Reference (QMR-DT) model for medical diagnosis (Miller et al. , 1982; Shwe et al. , 1991).”
Bayesian Artificial Intelligence (2004, background): “1(b) is essentially that developed in the QMRDT project [256, 190], a probabilistic version of the frame-based CPCS knowledge base for internal medicine.”
Discovering Hidden Variables in Noisy-Or Networks using Quartet Tests (2013, methodology): “…469 39,522 1 82 4,875 2 13 789 3 2 86 inf 4 198 Table 1: Right: The depth at which latent variables (i.e., diseases) are discovered and parameters learned in the aQMR-DT network for medical diagnosis (Shwe et al. , 1991) using the quartet-based structure learning algorithm, assuming infinite data.”