Extracting patient data from tables in clinical literature: Case study on extraction of BMI, weight and number of patients

Nikola Milosevic, Cassie Gregson, Robert Hernandez, Goran Nenadic

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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    Abstract

    Current biomedical text mining efforts are mostly focused on extracting information from the body of research articles. However, tables contain important information such as key characteristics of clinical trials. Here, we examine the feasibility of information extraction from tables. We focus on extracting data about clinical trial participants. We propose a rule-based method that decomposes tables into cell level structures and then extracts information from these structures. Our method performed with a F-measure of 83.3% for extraction of number of patients, 83.7% for extraction of patient’s body mass index and 57.75% for patient’s weight. These results are promising and show that information extraction from tables in biomedical literature is feasible.
    Original languageEnglish
    Title of host publicationProceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016)
    PublisherScience and Technology Publications Lda
    Pages223-228
    Number of pages6
    ISBN (Print)978-989-758-170-0
    DOIs
    Publication statusPublished - Feb 2016
    Event9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016) - Rome, Italy
    Duration: 21 Feb 201624 Feb 2016

    Conference

    Conference9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016)
    CityRome, Italy
    Period21/02/1624/02/16

    Keywords

    • table mining
    • text mining
    • information extraction

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