title: Discovering novel causal patterns from biomedical natural-language texts using Bayesian nets.
abstract: Most of the biomedicine text mining approaches do not deal with specific cause--effect patterns that may explain the discoveries. In order to fill this gap, this paper proposes an effective new model for text mining from biomedicine literature that helps to discover cause--effect hypotheses related to diseases, drugs, etc. The supervised approach combines Bayesian inference methods with natural-language processing techniques in order to generate simple and interesting patterns. The results of applying the model to biomedicine text databases and its comparison with other state-of-the-art methods are also discussed.
authors.affiliation: Department of Computer Sciences, Universidad de Concepcion, Concepcion 3349001, Chile. atkinson@inf.udec.cl
authors.forename: John
authors.initials: J
authors.lastname: Atkinson
authors.forename: Alejandro
authors.initials: A
authors.lastname: Rivas
mesh_terms: D000043:Abstracting and Indexing; D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003628:Database Management Systems; D016206:Databases, Bibliographic; D016208:Databases, Factual; D016247:Information Storage and Retrieval / Q000379:methods*; D009323:Natural Language Processing; D016571:Neural Networks, Computer; D010506:Periodicals as Topic