Biomedical Literature Mining for Pharmacokinetics Numerical Parameter Collection
نویسندگان
چکیده
BIOMEDICAL LITERATURE MINING FOR PHARMACOKINETICS NUMERICAL PARAMETER COLLECTION Model-based drug studies have been developing very fast recently. They require high quality pharmacokinetics (PK) parameter numerical data. However, most parameter measurements are still buried in the scientific literature. Traditional manual data extraction is too expensive to handle the exponentially growing number of publications. This thesis focuses on the application of text mining (TM) and machine learning (ML) for drug pharmacokinetics parameter data collection from the published literature. First, we explore the feasibility of TM on the extraction of drug PK parameter data from PubMed abstracts. Our method achieves higher precision and obtains rich information content. For the test drug Midazolam, it extracts 10 times more PK clearance data than the manually constructed commercial Drug Interaction Database (DiDB). Similar performance is obtained on additional test drugs. Following the success of TM on abstracts; we extended the methodology to full text articles and developed a literature mining pipeline for PK parameter data extraction. It combines machine learning, automatic information processing, and manual curation. It compromises four main components: (1) information retrieval, which applies both ontology-based name entity recognition (NER) and ML methods to classify PubMed search results; (2) article downloading of full PDF articles through PubMed external links; (3) information extraction of PK data from both tables and free text of articles; and (4) transformation and storage of mined information, so that it can be reachable in a drug-modeling-friendly manner. This literature mining pipeline and methodology is the first working approach to extract numerical data from full text articles, capable of processing both plain text and tabular data. The specific contributions of this thesis include: A new PK ontology for entity template construction Comparison of NLP and machine learning algorithms for PK information retrieval Tabular data extraction PK information extraction from full text literature A complete pipeline of numerical data extraction from both abstracts and full-text articles for pharmacokinetics
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