\dm_csml_event_details UCL ELLIS

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


Chris Williams


Edinburgh University


Friday, 07 November 2014






Malet Place Engineering Building 1.02

Event series

DeepMind/ELLIS CSML Seminar Series



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.

I will describe two recent developments for this framework:
1) A Hierarchical Switching Linear Dynamical System (HSLDS) has been
developed for the detection of sepsis in neonates in an intensive care
unit (ICU). This adds a higher-level discrete switch variable with
semantics sepsis/non-sepsis above the factors in the Factorial
Switching LDS (FSLDS) of Quinn et al. (2009).

2) The FSLDS is a generative model for the observations. We present a
Discriminative Switching Linear Dynamical System (DSLDS) applied to
patient monitoring in ICUs. Our approach is based on identifying the
state-of-health of a patient given their observed vital signs using a
discriminative classifier, and then inferring their underlying
physiological values conditioned on this status. We demonstrate on
two real-world datasets that the DSLDS is able to outperform the FSLDS
in most cases of interest, and that a combination of the two models
achieves higher performance than either of the two models

Joint work with Yvonne Freer, Konstantinos Georgatzis, Ioan Stanculescu

Speaker Bio:

Chris Williams is Professor of Machine Learning in the School of
Informatics, University of Edinburgh. He is interested in a wide range
of theoretical and practical issues in machine learning, statistical
pattern recognition, probabilistic graphical models and computer
vision. This includes theoretical foundations, the development of new
models and algorithms, and applications. His main areas of research
are in visual object recognition and image understanding,
models for understanding time-series, unsupervised learning, and
Gaussian processes.

He obtained his MSc (1990) and PhD (1994) at the University of
Toronto, under the supervision of Geoff Hinton. He was a member of
the Neural Computing Research Group at Aston University from 1994 to
1998, and has been at the University of Edinburgh since 1998. He was
program co-chair of NIPS in 2009, and is on the editorial boards of
the Journal of Machine Learning Research and Proceedings of the Royal
Society A.