Machine learning for the next generation of health informatics

Abstract: 

Healthcare systems worldwide are entering a new and exciting phase: ever-increasing quantities of clinical data are routinely collected, concerning all aspects of patient care, throughout the life of a patient. These Big Data in health and care are a unique combination of bacterial/viral genomics, noisy real-world clinical data, and many other data sources. Such analysis poses substantial challenges, including the high dimensionality of the data along, missing values, data heterogeneity, and scalability problems. Consequently, standard methods of medical data analysis are typically unable to handle data of this complexity. Innovations arising with machine learning approach can facilitate rapid clinical treatment, transform a hospital-only treatment pathway into a cost-effective home-based combined alternative, and improve the overall quality of health and care.  

Short bio:

I joined CVSSP in July 2020 to lead research and teaching in machine learning for health and dementia care in collaboration with the UK Dementia Research Institute (UK DRI) Care Research & Technology Centre. I previously spent three years as a postdoctoral researcher within the Institute of Biomedical Engineering at the University of Oxford. I was the senior machine learning researcher for the ‘100,000 Genomes Project for Tuberculosis’, an international consortium involving the Centres for Disease Control of most major nations (including the USA, UK and China), jointly funded by the Gates Foundation and the Wellcome Trust. My research focus was on the prediction of antibiotic resistance in pathogens such as those that cause tuberculosis. Prior to this, I was at the University of Manchester within the Division of Evolutionary and Genomic Sciences working on next-generation DNA sequencing using signal and image processing techniques coupled with unsupervised machine learning. I obtained my PhD in Computer Science at Surrey in 2015. My PhD focused on developing novel multi-way techniques for source separation with application to biomedical signals.