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

Tuesday, May 14, 2019 - 4:00pm to Wednesday, May 15, 2019 - 4:55pm

Event Calendar Category

LIDS Seminar Series

Speaker Name

Mihaela van der Schaar


University of Cambridge

Building and Room Number



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, Prof. van der Schaar is creating what she calls 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 a dynamic, active, holistic & individualized display of information including alerts.  

In this talk Prof. van der Schaar will focus on 3 steps in the development of LEH’s:

  1. Building a comprehensive model that accommodates irregularly sampled, 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 causal inference 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.


Professor van der Schaar is John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence, and Medicine at the University of Cambridge, 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 the University of Oxford. She is an IEEE Fellow (2009). She has received the Oon Prize 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 35 granted USA patents. Her current research focus is on data science, machine learning, AI and operations research for medicine.