Machine Learning, Data Science and Decision Lab
Directed by: Prof. Mihaela van der Schaar
Our lab is developing state-of-the-art methods in machine learning, data science, artificial intelligence and operations research to transform the way medicine is practiced and to advance medical science. Our lab has extensive collaborations with statisticians and mathematicians at the University of Oxford, at UCLA and at the Alan Turing Institute (where Professor van der Schaar plays a lead role in the program on data science for medicine) and with a well-established global network of medical collaborators across the world.
Our current emphasis is on
- Theoretical and statistical machine learning
- MedAdvance
- Personalized medicine
- Intelligent clinical decision support systems
- Early diagnosis and prevention
- Individualized treatment effects
- New designs for clinical trials
- Medical discovery
Theoretical and statistical machine learning
We develop theory, methods and algorithms for machine learning, data science and artificial intelligence. Our current emphasis is on causal inference, deep learning, time series, ensemble learning, reinforcement learning and multi-armed bandits, recovering missing data.
Current medical practice is driven by the experience of clinicians, by the difficulties of integrating enormous amounts of complex and heterogeneous static and dynamic data and by clinical guidelines designed for the "average" patient. MedAdvance aims to transform medical practice by developing novel, specially-crafted machine learning theories, methods and systems aimed at extracting actionable intelligence from the wide variety of information that is becoming available (in electronic health records and elsewhere) and enabling every aspect of medical care to be personalized to the patient at hand. The intended outputs of MedAdvance include clinical decision support systems for personalized risk assessment, diagnosis, prognosis and treatment; disease atlases which contain electronic representations of data-driven medical knowledge; and early warning systems - all intended to assist clinicians to make the best choices for the particular patient at hand. The construction of these outputs will require novel models for learning the non-stationary trajectory of disease progression, new learning architectures to learn from multiple heterogeneous time series data, novel conceptions of causal inference to learn individualized treatment effects in the absence of counterfactuals, novel methods for learning from data that is missing but not at random etc. MedAdvance will revolutionize healthcare for many types of populations and for patients suffering from many diseases (including cancer, cardiovascular disease, cystic fibrosis, etc.) and will make medicine more systematic, consistent and effective. MedAdvance also aims to transform the process of medical discovery from using the data to test hypotheses suggested by clinicians and researchers to using the data to create and test hypotheses suggested by the data itself, thereby leading to new theories of disease, discovery of new risk factors and new modes of treatment.
· Our paper on A Micro-Foundation of Social Capital in Evolving Social Networks is featured by IEEE Trans. on Network Science - see [Link] [Pdf Link]
· Our work on Data Science for Cystic Fibrosis highlighted in Nature article. [Link]
· Prof. van der Schaar's talk on machine learning for medicine at Alan Turing Institute. [Youtube Link]
· Prof. van der Schaar's talk on her group's research. [Youtube Link]
· Please see the interview with Prof. van der Schaar about her vision on how machine learning can transform medical practice and discovery. [Link] [pdf]
· Mihaela and her students aim to crack the code for cystic fibrosis [Link]
· Our research on machine learning for medicine is highlighted on EPSRC website in the article of Sir Prof. Alan Wilson, CEO of the Alan Turing Institute - Data Science: a new discipline to change the world
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Machine Learning, Data Science and Decisions Lab |
