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.
Division of Biostatistics, Department of Medicine, School of Medicine, Indiana University, 410 West 10th Street, Suite 3044, Indianapolis, IN 46202, USA.
Zhiping
Z
Wang
Seongho
S
Kim
Sara K
SK
Quinney
Yingying
Y
Guo
Stephen D
SD
Hall
Luis M
LM
Rocha
Lang
L
Li
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