University of Cambridge > Talks.cam > Seminars on Quantitative Biology @ CRUK Cambridge Institute  > Learning Engines for Healthcare: Using Machine Learning to Transform Clinical Practice and Discovery

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

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  • UserProf Mihaela van der Schaar from Department of Applied Maths and Theoretical Physics, University of Cambridge
  • ClockMonday 18 February 2019, 13:00-14:00
  • HouseCRUK CI Lecture Theatre (Room 001).

If you have a question about this talk, please contact Anna.Toporska.

The overarching goal of professor Mihaela van der Schaar’s 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, professor 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 dynamic, active, holistic & individualized display of information including alerts.

In this talk professor 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.

This talk is part of the Seminars on Quantitative Biology @ CRUK Cambridge Institute series.

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