title: Transformation of an acetaminophen toxicity nomogram to an artificial neural network.
abstract: A computerized artificial neural network was built based on the Rumack-Matthew nomogram for treatment of acetaminophen toxicity. NeuralWorks Predict 1.0 was used to build the network. The training set consisted of two input parameters, plasma concentration (microg/ml) and time of ingestion, and an output. The output was a binary unit representing the recommended use of N-acetylcysteine for the prevention of acetaminophen hepatotoxicity. The predictions made by the neural network correlated well with the expected values (95%). Since these networks can be modified with additional data, the present network may be useful for optimizing the use of N-acetylcysteine for the prevention of acetaminophen hepatotoxicity.
authors.affiliation: Department of Physiology and Pharmacology, University of the Pacific School of Pharmacy, Stockton, CA 95211, USA.
authors.forename: S
authors.initials: S
authors.lastname: Padar
authors.forename: T J
authors.initials: TJ
authors.lastname: Smith
mesh_terms: D000082:Acetaminophen / Q000097:blood / Q000633:toxicity*; D000111:Acetylcysteine / Q000494:pharmacology*; D056486:Chemical and Drug Induced Liver Injury; D003198:Computer Simulation; D005772:Gastrointestinal Transit / Q000502:physiology*; D008107:Liver Diseases / Q000517:prevention & control; D016571:Neural Networks, Computer
chemical_list: D000082:Acetaminophen; D000111:Acetylcysteine