Using text mining techniques to extract phenotypic information from the PhenoCHF corpus

BMC Medical Informatics and Decision Making

Proceedings

Paul Thompson¹  Sophia Ananiadou¹  Riza Batista-Navarro²  Noha Alnazzawi³ 

[1] Manchester Institute of Biotechnology, National Centre for Text Mining, 131 Princess St., M1 7DN, Manchester, UK;Manchester Institute of Biotechnology, National Centre for Text Mining, 131 Princess St., M1 7DN, Manchester, UK;Department of Computer Science, University of the Philippines Diliman, 1101, Quezon City, Philippines;Manchester Institute of Biotechnology, National Centre for Text Mining, 131 Princess St., M1 7DN, Manchester, UK;Royal Commission for Jubail and Yanbu, Jubail University College, 10074, Jubail City, Saudi Arabia;

关键词: Text Mining;    Discharge Summary;    Text Type;    Biomedical Literature;    Name Entity Recognition;   

DOI  :  10.1186/1472-6947-15-S2-S3

来源: Springer

【 摘 要 】

BackgroundPhenotypic information locked away in unstructured narrative text presents significant barriers to information accessibility, both for clinical practitioners and for computerised applications used for clinical research purposes. Text mining (TM) techniques have previously been applied successfully to extract different types of information from text in the biomedical domain. They have the potential to be extended to allow the extraction of information relating to phenotypes from free text.MethodsTo stimulate the development of TM systems that are able to extract phenotypic information from text, we have created a new corpus (PhenoCHF) that is annotated by domain experts with several types of phenotypic information relating to congestive heart failure. To ensure that systems developed using the corpus are robust to multiple text types, it integrates text from heterogeneous sources, i.e., electronic health records (EHRs) and scientific articles from the literature. We have developed several different phenotype extraction methods to demonstrate the utility of the corpus, and tested these methods on a further corpus, i.e., ShARe/CLEF 2013.ResultsEvaluation of our automated methods showed that PhenoCHF can facilitate the training of reliable phenotype extraction systems, which are robust to variations in text type. These results have been reinforced by evaluating our trained systems on the ShARe/CLEF corpus, which contains clinical records of various types. Like other studies within the biomedical domain, we found that solutions based on conditional random fields produced the best results, when coupled with a rich feature set.ConclusionsPhenoCHF is the first annotated corpus aimed at encoding detailed phenotypic information. The unique heterogeneous composition of the corpus has been shown to be advantageous in the training of systems that can accurately extract phenotypic information from a range of different text types. Although the scope of our annotation is currently limited to a single disease, the promising results achieved can stimulate further work into the extraction of phenotypic information for other diseases. The PhenoCHF annotation guidelines and annotations are publicly available at https://code.google.com/p/phenochf-corpus.

【 授权许可】

CC BY   
© Alnazzawi et al.; licensee BioMed Central Ltd. 2015