Thesis title: Analysing and recommending personalised care pathways for multimorbid patients with the use of artificial intelligence techniques
I graduated from the University of Sheffield in 2019, having completed a degree in Medical Genetics BSc. During the degree, I took part in the Sheffield Undergraduate Research Experience Scheme (SURE), where I had the pleasure of working with Professor Angela Cox on fine-mapping a genomic region associated with breast cancer, while my third-year project was focused on using cytogenetic tools to diagnose the myelodysplastic syndrome. A year later I began working on estimating the effect BRCA1 and BRCA2 mutations have on the survival of breast cancer in the population of patients at the Leeds Teaching Hospitals NHS Trust as a part of my MRes Medicine degree at the University of Leeds, which I completed in 2020.
I am now a member of the 2020 cohort of the MRC funded Precision Medicine Doctoral Training Programme, working on a project titled "Analysing and recommending personalised care pathways for multimorbid patients with the use of artificial intelligence techniques". The primary aim of the project is to contribute to the digitally supported, person-centered, and integrated model of care by using data science approaches and artificial intelligence techniques to analyse and recommend personalised care pathways for multimorbid patients. Additionally, the project will create a proof of concept decision support tool to disseminate the findings.
2020: MRes Medicine, University of Leeds
2019: Medical Genetics, University of Sheffield