Learning Bayesian network parameters from small data sets: application of Noisy-OR gates

By A. Onisko, Marek J Druzdzel, H. Wasyluk. 2001

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Improving the accuracy of medical diagnosis with causal machine learning (2020, background): “In addition, they support ef ﬁ cient inference 67 and learning 68,69 , and allow for large BNs to be described by a number of parameters that grows linearly with the size of the network 68,70 .”
Evaluate the Malignancy of Pulmonary Nodules Using the 3-D Deep Leaky Noisy-OR Network (2017, methodology): “The noisy-or Bayesian model is wildly used in inferring the probability of diseases such as liver disorder [31] and asthma case [32].”
Learning a Health Knowledge Graph from Electronic Medical Records (2017, methodology): “Noisy OR was chosen as an example of a probabilistic model that jointly models diseases and symptoms; similar models have successfully been used in previous medical diagnosis applications 10, 12, 32–34 .”
Structured Priors for Structure Learning (2006, methodology): “ologies and parameterizations inspired by networksof real-worldinterest, including the QMRDT network and a gene regulatory network. Finally, we report model performance on data sampled from HEPAR II [Onisko et al., 2001], an engineered network that captures knowledge about liver disorders. Because o”
A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia (2020, unknown): “Finally, the overall analysis report for one CT sample was calculated using the Noisy-OR Bayesian function [25].”