TMCF workshop series: Prediction algorithms with a causal interpretation (online event)
Wednesday 06 May 2020
Time: 11:00 - 16:30
Bio
Niels Peek is Professor of Health Informatics and Strategic Research Domain Director for Digital Health at the University of Manchester. He has a background in Computer Science and Artificial Intelligence, and his research focuses on data-driven methods for health research, healthcare quality improvement, and computerised decision support. He leads the Greater Manchester Connected Health City, which is part of the £20M "Health North" investment to establish a learning health system in the North of England. Professor Peek has co-authored more than 150 peer-reviewed scientific publications. From 2013 to 2017 he was the President of the Society for Artificial Intelligence in Medicine.
He is a member of the editorial boards of the Journal of the American Medical Informatics Association and the Artificial Intelligence in Medicine journal. In April 2017, he organised the Informatics for Health 2017 conference in Manchester which was attended by more than 800 people from 30 countries. He also co-chaired the Scientific Programme Committee of MEDINFO-2017, the 16th World Congress on Health and Biomedical Informatics, which was held in Hangzhou, China, in August 2017. In 2018 he was elected to become a fellow of the American Collecege of Medical Informaticians and a fellow of the Alan Turing Institute.
Research interests
Health data, for example from electronic health records or other sources such as wearables and the Internet, are subject to complex data generation processes, which are not addressed with existing methods for risk prediction. Professor Peek's research for the Turing focuses on two key issues. First, routine health data is subject to ‘informative presence’ whereby presence of a particular observation (e.g. a blood test) is informative independent of the actual result of the test. For example, it gives information about an individual’s tendency to engage with the healthcare system, and/or information about a clinician’s prior beliefs about a patient’s condition that drives them to run particular tests. This is challenging to model because it essentially corresponds to a ‘missing not at random’ scenario. Second, existing risk prediction models model prognostic risk only – and do not allow consideration of ‘what-if’ scenarios.
potential outcomes (causal) framework would allow us to infer risk under different intervention scenarios – both at individual patient level and at policy level. Initial work has explored the use of marginal structural models to infer potential outcomes for patients at risk of cardiovascular events – where the intervention is a statin prescription. Currently more advanced models that allow ‘what-if’ prediction modelling using messy observational data are being developed.
Achievements and awards
- Fellow, American College of Medical Informatics, since 2018
- Fellow, Alan Turing Institute, since 2018
- Research Domain Director for Digital Health, Faculty of Biology Medicine and Health, The University of Manchester, since 2018
- President, Artificial Intelligent in Medicine (AIME) Society, 2013-2017
- Scientific Program Committee Co-Chair, 16th World Congress on Health and Biomedical Informatics (MEDINFO 2017), Hangzhou, China, August 2017
- Local Organising Committee Chair, Informatics for Health Conference, Manchester, UK, April 2017
- Member, Advisory Board, EPSRC NetworksPlus Fast ASsessment and Treatment in Healthcare (FAST Healthcare), since 2016
- Director, Greater Manchester Connected Health City (www.connectedhealthcities.org), since 2016
- Senior Scienfic Programme Committee Member, IEEE International Conference on Healthcare Informatics, Chicago, Illinois, USA, October 2016
- Steering Group member, EPSRC UK Health Data Analytics Network (UK-HDAN), since 2015
- Scientific Program Committee Chair, 14th conference on Artificial Intelligence in Medicine, Murcia, Spain, May-June 2013
- Editorial Board member, Studies in Health Technology and Informatics (IOS Press), since 2018
- Editorial Board member, Artificial Intelligence in Medicine (Elsevier), since 2017
- Editorial board member, Journal of the American Medical Informatics Association (Oxford), since 2015
- Editorial board member, Journal of Biomedical Informatics (Elsevier), 2009-2016
- Editorial board member, Methods of Information in Medicine (Schattauer), 2008-2014
