An Introduction to Machine Learning in Healthcare schedule

In this session we will cover the basics and key concepts of machine learning, what it is and how it can be applied. The emphasis will be on demonstrating, through real-world examples of application, how machine learning can benefit biomedical research, healthcare and precision medicine.

Nophar is a lecturer in Biomedical Data Analysis and Modelling at the Centre for Health Informatics at the University of Manchester. She holds a PhD in Biomedical Informatics and has completed her postdoctoral training with Prof. Atul J. Butte at Stanford University and the Institute for Computational Health Sciences at UCSF. Her interests lie in the fields of knowledge and data mining, endotype discovery, patient stratification and precision medicine, integration and meta-analyses of health-related big-data; and in improving the sharing and utility of these data. Her research focuses on the development and application of informatics techniques for analysis and discovery in various areas of medicine – particularly where conventional research methods have over-simplified the natural complexity of disease and care.

Andreas is a Teaching Fellow and a PhD student in the Intelligent Systems group at the University of Bath, where he works under the supervision of Dr Joanna Bryson in Artificial Models of Natural Intelligence. His main research interest is the design and application of intelligent systems. He is engaged in a research programme trying to understand the ethical and legal implications of Artificial Intelligence in societies by building transparent to inspection intelligent agents. Currently, he is participating at both domestic and international initiatives, such as the IEEE Standards Association Initiative for Intelligence System and AI APPG, at establishing AI-related standards and policy. Finally, Andreas is exploring the understanding and intuitions that can guide human cooperative behaviour, by using interventions in the form of computer game technology with Artificial Intelligence.

Deep learning for healthcare technologies

With the confluence of very large datasets now becoming available in healthcare along with methods from deep learning to providing new ways of using them, healthcare is poised to adopt new tools based on such technologies in coming years. This talk describes work undertaken between the University of Oxford and the Oxford University Hospitals in developing early systems in this field.

David A. Clifton is Associate Professor of Engineering Science at the University of Oxford and leads the Computational Health Informatics (CHI) Laboratory, after having trained in information engineering at the University of Oxford. CHI Lab focusses on the interface between machine learning and healthcare, in partnership with leading clinicians from the Oxford University Hospitals NHS Trust, and has grown rapidly to over 20 members in the two years since its founding, with support from the Wellcome Trust, UK Department of Health, NHS National Institute of Health Research, Engineering & Physical Sciences Research Council, Royal Academy of Engineering, Natural Environment Research Council, and the Bill & Melinda Gates Foundation.

Dr. Maria Chatzou, CEO of Lifebit, is a biotech innovator and a proud geek, expert in bioinformatics, medical informatics and high performance computing. She is also a passionate entrepreneur and has founded two companies, Innovation Forum Barcelona and, Techstars-backed Lifebit. Prior to Lifebit, she was a researcher at the Centre for Genomic Regulation, in Barcelona, where she was designing and deploying tools and methods that facilitate the analysis of Big Biomedical Data, allow for biological discoveries, and promote personalised medicine. She was part of the developing team of Nextflow, a programming framework that is revolutionising the computational analysis of genomic data and setting the foundations for personalised medicine computational analyses. Maria is also a frequent industry speaker and has spoken in many international conferences on the subjects of docker containers, genomics workflows, the computational challenges of personalised medicine, AI and HPC in genomics, women in leadership, entrepreneurship, science ventures, among many other topics.

What are the key questions to consider when applying ML to healthcare and medicine? How can you evaluate the impact of ML on data? Which ML frameworks are most suitable?