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Lloyd, Katherine L. (2017) Machine learning stratification for oncology patient survival. PhD thesis, University of Warwick.
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WRAP_Theses_Lloyd_2017.pdf - Submitted Version - Requires a PDF viewer. Download (2743Kb) | Preview |
Official URL: http://webcat.warwick.ac.uk/record=b3216490~S15
Abstract
Personalised medicine for cancer treatment promises benefits for patient survival and effective use of medical resources. This goal requires the development of predictive models for the identification and implementation of biomarkers for the prediction of patient survival given treatment options. This thesis addresses research questions in this area.
The systematic review detailed in Chapter 2 investigates the literature concerning the prediction of resistance to chemotherapy for ovarian cancer patients using statistical methods and gene expression measurements. The range of models used by studies in the systematic review highlights the popularity of traditional models, such as Cox proportional hazards, with few more complex models being utilised.
In Chapters 3 and 4, new methods are presented for modelling right-censored survival data. Due to the nature of biomedical data, the methods used need to be flexible and adequately account for high dimensional, noisy data. Gaussian processes fulfil these requirements and were hence used for the development of three Gaussian process models for right-censored survival data. Chapter 3 details these models, and they are applied to synthetic and cancer data in Chapter 4. In all cases the Gaussian processes for survival were found to equal or outperform all comparison models, as measured by concordance index.
Given the application to molecular cancer data, it was expected that the data would be high dimensional. Two feature selection methods are investigated in Chapter 5 for use with Gaussian processes to address this.
In Chapter 6 a program is developed for the analysis of the data produced by a test for cancer mutations using qPCR. The automated program was designed to isolate the analysis from the user and produce results and reports for clinical use. It is observed that this approach of automated analysis would be suitable for application to any form of clinical test or complex predictive model without the requirement of user guidance.
| Item Type: | Thesis (PhD) | ||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software R Medicine > R Medicine (General) |
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| Library of Congress Subject Headings (LCSH): | Personalized medicine, Cancer -- Treatment, Machine learning, Medical informatics, Artificial intelligence -- Medical applications, Biochemical markers, Gaussian processes, Survival analysis (Biometry) | ||||
| Official Date: | September 2017 | ||||
| Dates: |
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| Institution: | University of Warwick | ||||
| Theses Department: | Molecular Organisation and Assembly in Cells | ||||
| Thesis Type: | PhD | ||||
| Publication Status: | Unpublished | ||||
| Supervisor(s)/Advisor: | Savage, Richard S. ; Cree, Ian A. | ||||
| Format of File: | |||||
| Extent: | xvii, 190 leaves : illustrations, charts | ||||
| Language: | eng |
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