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Dynamic outcomes-based clustering of disease progression in mechanically ventilated patients
Emma Rocheteau · Ioana Bica · Pietro Lió · Ari Ercole

Fri Dec 02 09:30 AM -- 10:30 AM (PST) @
in Learning from Time Series for Health »
Event URL: https://openreview.net/forum?id=S7FEB6rwc5R »

The advancement of Electronic Health Records (EHRs) and machine learning have enabled a data-driven and personalised approach to healthcare. One step in this direction is to uncover patient sub-types with similar disease trajectories in a heterogeneous population. This is especially important in the context of mechanical ventilation in intensive care, where mortality is high and there is no consensus on treatment. In this work, we present a new approach to clustering mechanical ventilation episodes, using a multi-task combination of supervised, self-supervised and unsupervised learning techniques. Our dynamic clustering assignment is explicitly guided to reflect the phenotype, trajectory and outcomes of the patient. Experimentation on a real-world dataset is encouraging, and we hope that we could someday translate this into actionable insights in guiding future clinical research.

Author Information

Emma Rocheteau (University of Cambridge)

2nd Year PhD Student

Ioana Bica (DeepMind)
Pietro Lió (University of Cambridge)
Ari Ercole (University of Cambridge)

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