Learning Engines for Healthcare: Using Machine Learning to Transform Clinical Practice and Discovery

Prof. Mihaela van der SchaarDate: 11:00am, January 28, 2019
Location: Davis Auditorium (412 CEPSR)
Speaker:  Prof. Mihaela van der Schaar, John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine, University of Cambridge

Abstract:  The overarching goal of my research is to develop cutting-edge machine learning, AI and operations research theory, methods, algorithms and systems to understand the basis of health and disease; develop methodology to catalyze clinical research; support clinical decisions through individualized medicine; inform clinical pathways, better utilize resources & reduce costs; and inform public health.

To do this, I am creating what I call Learning Engines for Healthcare (LEH's). An LEH is an integrated ecosystem that uses machine learning, AI and operations research to provide clinical insights and healthcare intelligence to all the stakeholders (patients, clinicians, hospitals, administrators). In contrast to an Electronic Health Record, which provides a static, passive, isolated display of information, an LEH provides dynamic, active, holistic & individualized display of information including alerts.

In this talk I will focus on 3 steps in the development of LEH's:

1. Building a comprehensive model that accommodates irregularly samples, temporally correlated, informatively censored and non-stationary processes in order to understand and predict the longitudinal trajectories of diseases.

2. Establishing the theoretical limits of casual interference and using what has been established to create a new approach that makes it possible to better estimate individualized treatment effects.

3. Using Machine Learning itself to automate the design and construction of entire pipelines of Machine Learning algorithms for risk prediction, screening, diagnosis and prognosis.

Biography: Professor van der Schaar is John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge and a Turing Faculty Fellow at The Alan Turing institute in London, where she leads the effort on data science and machine learning for personalized medicine. Prior to this, she was a Chancellor's Professor at UCLA and MAN Professor of Quantitative Finance at University of Oxford. She is an IEEE Fellow (2009). She has received the Oon Price on Preventative Medicine from the University of Cambridge (2018). She has also been the recipient of an NSF Career Award, 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award. She holds 33 granted USA patents. Her current research focus is on data science and machine learning for medicine and education.

Host: Professor Debasis Mitra

About The Electrical Engineering Distinguished Lecture Series

This series of lectures offered by the Department of Electrical Engineering at Columbia University in New York was established to recognize the scholarship and accomplishment of prominent scholars in the field of electrical engineering, and to provide an opportunity for the public to learn about cutting-edge technologies and research breakthroughs in the field.


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