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Literature mining on pharmacokinetics numerical data: a feasibility study Wang Z; Kim S; Quinney SK; Guo Y; Hall SD; Rocha LM; Li LJ Biomed Inform 2009[Aug]; 42 (4): 726-35A 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.|*Linear Models[MESH]|*Models, Biological[MESH]|*Pharmacokinetics[MESH]|Algorithms[MESH]|Artificial Intelligence[MESH]|Databases, Factual[MESH]|Humans[MESH]|Information Storage and Retrieval/*methods[MESH]|Midazolam/pharmacokinetics[MESH]|PubMed[MESH]|Reproducibility of Results[MESH] |
