C.K. Chow, S.S.M. Wang, et al.
Computers and Biomedical Research
Due to the fast pace at which randomized controlled trials are published in the health domain, researchers, consultants and policymakers would benefit from more automatic ways to process them by both extracting relevant information and automating the meta-analysis processes. In this paper, we present a novel methodology based on natural language processing and reasoning models to 1) extract relevant information from RCTs and 2) predict potential outcome values on novel scenarios, given the extracted knowledge, in the domain of behavior change for smoking cessation.
C.K. Chow, S.S.M. Wang, et al.
Computers and Biomedical Research
G. Antonini, A.E. Ruehli, et al.
PIERS 2004
John K. Kastner, Chandler R. Dawson, et al.
Journal of Medical Systems
J.F. Ziegler
Nuclear Instruments and Methods