Literature mining on pharmacokinetics numerical data: a feasibility study.
A feasibility study of literature mining is conducted on drug PK parameter numerical data with a sequential mining strategy. Firstly, an entity template library is built to retrieve pharmacokinetics relevant articles. Then a set of tagging and extraction rules are applied to retrieve PK data from the article abstracts. To estimate the PK parameter population-average mean and between-study variance, a linear mixed meta-analysis model and an E-M algorithm are developed to describe the probability distributions of PK parameters. Finally, a cross-validation procedure is developed to ascertain false-positive mining results. Using this approach to mine midazolam (MDZ) PK data, an 88% precision rate and 92% recall rate are achieved, with an F-score=90%. It greatly out-performs a conventional data mining approach (support vector machine), which has an F-score of 68.1%. Further investigate on 7 more drugs reveals comparable performances of our sequential mining approach.
Wang
Zhiping
Z
Division of Biostatistics, Department of Medicine, School of Medicine, Indiana University, 410 West 10th Street, Suite 3044, Indianapolis, IN 46202, USA.
Kim
Seongho
S
Quinney
Sara K
SK
Guo
Yingying
Y
Hall
Stephen D
SD
Rocha
Luis M
LM
Li
Lang
L
D000465:Algorithms; D001185:Artificial Intelligence; D016208:Databases, Factual; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D016014:Linear Models; D008874:Midazolam / Q000493:pharmacokinetics; D008954:Models, Biological; D010599:Pharmacokinetics; D039781:PubMed; D015203:Reproducibility of Results
D008874:Midazolam