期刊
JOURNAL OF BIOMEDICAL INFORMATICS
卷 42, 期 4, 页码 726-735出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jbi.2009.03.010
关键词
Clearance; Data mining; Entity recognition; Information extraction; Linear mixed model; Midazolam; Pharmacokinetics
资金
- NIA NIH HHS [R01 AG025152] Funding Source: Medline
- NIGMS NIH HHS [R01 GM074217-03, R01 GM074217-05, R01 GM074217-02, R01 GM074217, R01 GM074217-01, R01 GM074217-04] Funding Source: Medline
- NLM NIH HHS [R01 LM011945] Funding Source: Medline
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. (C) 2009 Elsevier Inc. All rights reserved.
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