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Flexible deep learning for heterogeneous clinical time series

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If you have a question about this talk, please contact Mateja Jamnik.

Extensive monitoring in the hospital, and in particular the intensive care unit, generates large quantities of data which contain numerous trends that are difficult for clinicians to systematically evaluate. Current attempts to address such heterogeneity in Electronic Health Records (EHRs) discard pertinent information and state-of-the-art models are often the final stage in vast pre-processing pipelines which are left unseen. I will discuss a more flexible approach to outcome prediction from EHR data that attempts to use all uncurated events without variable selection or pre-processing. Moreover, in realistic scenarios, multivariate timeseries evolve over case-by-case time-scales. This is particularly clear in medicine, where the rate of clinical events varies by ward, patient, and application. Increasingly complex models have been shown to effectively predict patient outcomes, but have failed to adapt granularity to these inherent temporal resolutions. I will also outline initial research into adaptive prediction timing for clinical time series.

This talk is part of the Artificial Intelligence Research Group Talks (Computer Laboratory) series.

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