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rdf:type |
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lifeskim:mentions |
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pubmed:issue |
4
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pubmed:dateCreated |
2009-7-6
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pubmed:abstractText |
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.
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pubmed:grant |
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pubmed:commentsCorrections |
http://linkedlifedata.com/resource/pubmed/commentcorrection/19345282-14980013,
http://linkedlifedata.com/resource/pubmed/commentcorrection/19345282-15358623,
http://linkedlifedata.com/resource/pubmed/commentcorrection/19345282-15637526,
http://linkedlifedata.com/resource/pubmed/commentcorrection/19345282-15960821,
http://linkedlifedata.com/resource/pubmed/commentcorrection/19345282-16353932,
http://linkedlifedata.com/resource/pubmed/commentcorrection/19345282-16972706,
http://linkedlifedata.com/resource/pubmed/commentcorrection/19345282-17186012,
http://linkedlifedata.com/resource/pubmed/commentcorrection/19345282-17522597,
http://linkedlifedata.com/resource/pubmed/commentcorrection/19345282-17885865,
http://linkedlifedata.com/resource/pubmed/commentcorrection/19345282-18991108,
http://linkedlifedata.com/resource/pubmed/commentcorrection/19345282-2723115
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pubmed:language |
eng
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pubmed:journal |
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pubmed:citationSubset |
IM
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pubmed:chemical |
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pubmed:status |
MEDLINE
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pubmed:month |
Aug
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pubmed:issn |
1532-0480
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pubmed:author |
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pubmed:issnType |
Electronic
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pubmed:volume |
42
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pubmed:owner |
NLM
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pubmed:authorsComplete |
Y
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pubmed:pagination |
726-35
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pubmed:dateRevised |
2010-9-22
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pubmed:meshHeading |
pubmed-meshheading:19345282-Algorithms,
pubmed-meshheading:19345282-Artificial Intelligence,
pubmed-meshheading:19345282-Databases, Factual,
pubmed-meshheading:19345282-Humans,
pubmed-meshheading:19345282-Information Storage and Retrieval,
pubmed-meshheading:19345282-Linear Models,
pubmed-meshheading:19345282-Midazolam,
pubmed-meshheading:19345282-Models, Biological,
pubmed-meshheading:19345282-Pharmacokinetics,
pubmed-meshheading:19345282-PubMed,
pubmed-meshheading:19345282-Reproducibility of Results
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pubmed:year |
2009
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pubmed:articleTitle |
Literature mining on pharmacokinetics numerical data: a feasibility study.
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pubmed:affiliation |
Division of Biostatistics, Department of Medicine, School of Medicine, Indiana University, 410 West 10th Street, Suite 3044, Indianapolis, IN 46202, USA.
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pubmed:publicationType |
Journal Article
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