David C. Spellmeyer, William C. Swope
Perspectives in Drug Discovery and Design
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.
David C. Spellmeyer, William C. Swope
Perspectives in Drug Discovery and Design
Yufang Hou, Charles Jochim, et al.
ACL 2019
F. James Rohlf
Mathematical Biosciences
Jesus J. Caban, Noah Lee, et al.
ISBI 2009