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

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.