The ALARM Monitoring System: A Case Study with two Probabilistic Inference Techniques for Belief Networks

By I. Beinlich, H. J. Suermondt, R. M. Chavez, Gregory F. Cooper. 1989

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Citations in the corpus

Learning Neural Causal Models from Unknown Interventions (2019, methodology): “We evaluate the proposed method on Barley [54] (M = 48) and Alarm [53] (M = 37) from the BnLearn repository.”
Loopy Belief Propagation for Approximate Inference: An Empirical Study (1999, methodology): “This network was used by [3] to compare various inference algorithms.”
A Scoring Function for Learning Bayesian Networks based on Mutual Information and Conditional Independence Tests (2006, background): “The Alarm network displays the relevant variables and relationships for the Alarm Monitoring System (Beinlich et al., 1989), a diagnostic application for patient monitoring.”
Learning Bayesian Networks with the bnlearn R Package (2009, background): “The ALARM (“A Logical Alarm Reduction Mechanism”) network from Beinlich et al. (1989) is a Bayesian network designed to provide an alarm message system for patient monitoring.”
Scaling-Up Bayesian Network Learning to Thousands of Variables Using Local Learning Techniques (2003, methodology): “Bayesian Networks (BN) is a formalization that has proved itself a useful and important tool in medicine for building decision support systems [2] and in bioinformatics for discovering gene expression pathways [4].”
Robust Learning of Fixed-Structure Bayesian Networks (2016, background): “, the ALARM network [BSCC89]) with synthetic noise.”
Constraint-based causal discovery from multiple interventions over overlapping variable sets (2014, methodology): “We generated networks by manipulating a marginal of the ALARM network (Beinlich et al., 1989) consisting of 14 variables.”
Probabilistic similarity networks (2019, unknown): “If a certain symptom doesn't help to distinguish between a pair of diseases , the conditional probabilities of that symptom given either of the diseases must be the same.”
Permutation Testing Improves Bayesian Network Learning (2010, background): “Graphical models such as Bayesian Networks (BNs) are often at the heart of decision support systems and employed for prediction and diagnosis [4].”
Algorithms for Large Scale Markov Blanket Discovery (2003, unknown): “In order to measure the performance of each algorithm, we need to know the real MB(T) to use it as a gold standard, which in practice is possible only in simulated data.”
Learning Causal Bayesian Network Structures From Experimental Data (2008, background): “areas in science and technology ( Beinlich, Suermondt, Chavez and Cooper 1989; Pearl 1988;”
Learning Bayesian networks: approaches and issues (2011, background): “Within a similar domain, but used for a different purpose is the ALARM network developed by Beinlich et al. (1989), which was used for the monitoring of patients in intensive care situations.”
An evaluation of explanations of probabilistic inference. (1992, unknown): “We chose the ALARM belief network (Beinlich et al., 1989).”
A survey of Bayesian Network structure learning (2021, methodology): “Using the ‘good standard’, experts halved SHD on the Alarm network (Beinlich et al., 1989) commonly used in evaluations, although improvement was limited when the weak set of experts were used.”