TY - JOUR
T1 - Discovering novel causal patterns from biomedical natural-language texts using Bayesian nets
AU - Atkinson, John
AU - Rivas, Alejandro
N1 - Funding Information:
Manuscript received October 23, 2007; revised January 29, 2008. First published March 21, 2008; current version published November 5, 2008. This work was supported in part by the National Council for Scientific and Technological Research (FONDECYT, Chile) under Grant 1070714: “An Interactive Natural-Language Dialogue Model for Intelligent Filtering Based on Patterns Discovered From Text Documents.” The authors are with the Department of Computer Sciences, Universidad de Concepcion, Concepcion 3349001, Chile (e-mail: [email protected]; [email protected]).
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
KW - Bayesian nets
KW - Biomedicine
KW - Information extraction
KW - Knowledge discovery
KW - Text mining
UR - http://www.scopus.com/inward/record.url?scp=56549086478&partnerID=8YFLogxK
U2 - 10.1109/TITB.2008.920793
DO - 10.1109/TITB.2008.920793
M3 - Article
C2 - 19000950
AN - SCOPUS:56549086478
SN - 1089-7771
VL - 12
SP - 714
EP - 722
JO - IEEE Transactions on Information Technology in Biomedicine
JF - IEEE Transactions on Information Technology in Biomedicine
IS - 6
ER -