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Eloise Ockenden
CDT Student
I studied for my Master’s in Mathematics at Durham before starting the Health Data Science CDT, as well as doing an internship in the finance industry. My master’s project combined machine learning and healthcare: looking at features of breast masses that would indicate malignancy. The application of machine learning to healthcare really interested me, so a DPhil seemed a natural step to take.
My DPhil project is titled: ‘Machine learning to classify periportal fibrosis from point-of-care ultrasound to develop clinical decision support systems for schistosomiasis in sub-Saharan Africa’, supervised by Goylette Chami and Alison Noble. So far, this has combined extensively reviewing ultrasound staging systems for the staging of periportal fibrosis, and I am moving on to using machine learning methods to develop a pipeline for data cleaning and fibrosis classification.
In my spare time, I enjoy baking, crochet, and playing the clarinet!
Recent publications
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The role of point-of-care ultrasound in the assessment of schistosomiasis-induced liver fibrosis: A systematic scoping review.
Ockenden ES. et al, (2024), PLoS neglected tropical diseases, 18
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An Automated Pipeline for the Identification of Liver Tissue in Ultrasound Video
Ockenden E. et al, (2024)
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Conceptualising care pathways for neglected tropical diseases in sub-Saharan Africa: A systematic scoping review
Frischer SR. et al, (2024)