Machine Learning Research
Temporal time series of structured data often appear in biomedical datasets, presenting challenges such as containing variables with different periodicities, being conditioned by static data, etc. Our laboratory has acquired great expertise in developing patient state space models, including interpretable clustering, time series imputation, and sepsis prediction. These approaches typically use un-, semi- or self-supervised learning techniques that extract information from a patient's past (at a specific time) to compute a representation that summarises all relevant information in a way that the representation is predictive of the future data, future diseases, or future outcomes. [Read more ...]
Probabilistic Modelling and Bayesian Deep Learning
Deep learning has revolutionised the field of machine learning and led to new possibilities in many areas of science. Neural networks often suffer from unreliable estimates and require human expertise to apply them to new data sets. Our research attempts to mitigate these limitations by fundamental research in probabilistic methods and Bayesian inference for deep learning. [Read more ...]
Imitation and Reinforcement learning
The medical community shows a growing research interest in personalised treatment strategies, which must be developed with powerful modelling tools. Large observational clinical datasets provide insight into treatment and diagnostic decisions taken by healthcare professionals. From the machine learning perspective, an exciting avenue of research consists of extracting this clinical expertise, evaluating outcomes associated with particular decisions, and developing personalised treatment- or diagnostic-recommendation systems to support physicians’ decision-making.
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