Switching Linear Dynamical Systems for Condition Monitoring in the Intensive Care Unit

Speaker: Chris Williams , University of Edinburgh

Date: Wednesday, October 30, 2013

Time: 11:00 AM to 12:00 PM Note: all times are in the Eastern Time Zone

Public: Yes

Location: 32-G449

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Host: William Freeman, CSAIL

Contact: Maysoon Hamdiyyah, 617-253-6693, maysoon@csail.mit.edu

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Reminders to: vgn@csail.mit.edu, seminars@csail.mit.edu

Reminder Subject: TALK: Switching Linear Dynamical Systems for Condition Monitoring in the Intensive Care Unit


Data drawn from an observed system is often usefully described by a number of hidden (or latent) factors. Given a sequence of observations, the task is to infer which latent factors are active at each time frame. In this talk I will describe the application of a switching linear dynamical model to monitoring the condition of a patient receiving intensive care. The state of health of a patient cannot be observed directly, but different underlying factors are associated with particular patterns of measurements, e.g. in the heart rate, blood pressure and temperature.

We demonstrate how to exploit knowledge of the structure of how the various latent factors interact so as to reduce the amount of training
data needed for the system. A combination of domain knowledge engineering and learning is used to produce an effective solution. We use the model to infer the presence of two different types of factors: common, recognisable regimes (e.g. certain artifacts or common physiological phenomena), and novel patterns which are clinically significant but have unknown cause. Experimental results are given showing the potential of the system for the early detection of
neonatal sepsis, a major clinical concern in the care of premature babies.

Chris Williams is Professor of Machine Learning, School of Informatics, University of Edinburgh

Joint work with Yvonne Freer, Neil McIntosh, John Quinn, Ioan Stanculescu.

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Created by Maysoon Hamdiyyah Email at Friday, October 18, 2013 at 1:28 PM.