Slater, Luke Thomas (2020). Ontology and text mining: methods and applications for hypertrophic cardiomyopathy and beyond. University of Birmingham. Ph.D.
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Slater2020PhD.pdf
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Abstract
In this thesis we describe a number of contributions across the deeply interlinked domains of ontology, text mining, and prognostic modelling. We explore and evaluate ontology interoperability, and develop new methods for synonym expansion and negation detection in biomedical text. In addition to evaluating these pieces of work individually, we use them to form the basis of a text mining pipeline that can identify and phenotype patients across a clinical text record, which is used to reveal hundreds of University Hospitals Birmingham patients diagnosed with hypertrophic cardiomyopathy who are unknown to the specialist clinic. The work culminates in the text mining results being used to enable prognostic modelling of complication development in patients with hypertrophic cardiomyopathy, finding that routine blood markers, in addition to already well known variables, are powerful predictors.
| Type of Work: | Thesis (Doctorates > Ph.D.) | |||||||||
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| Award Type: | Doctorates > Ph.D. | |||||||||
| Supervisor(s): |
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| Licence: | All rights reserved | |||||||||
| College/Faculty: | Colleges (2008 onwards) > College of Medical & Dental Sciences | |||||||||
| School or Department: | Institute of Cancer and Genomic Sciences | |||||||||
| Funders: | Other | |||||||||
| Other Funders: | Innovate UK, NSF, OpenRiskNet | |||||||||
| Subjects: | R Medicine > R Medicine (General) | |||||||||
| URI: | http://etheses.bham.ac.uk/id/eprint/11060 |
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