Healthcare condition monitoring using ICUdata

Chris Williamsjoint work with Yvonne Freer, Konstantinos Georgatzis, ChrisHawthorne, Partha Lal, Neil McIntosh, Ian Piper, John Quinn,

Martin Shaw, Ioan Stanculescu

School of Informatics, University of Edinburgh,and Alan Turing Institute, London

November 2017

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My main research interests:

I Time series understandingI Computer vision, especially object recognition, shape and

texture modellingI Semi-automation of data cleaning and preparationI Unsupervised learningI Gaussian processes

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Time Series UnderstandingI Explain the multivariate time series in terms of an

underlying set of discrete factorsI Make inferences for underlying variables when

observations are corrupted by artifactI We will address such problems with various switching

linear dynamical systems (SLDS) models

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ICU Condition Monitoring

I Population: patients receiving intensive careI Data: physiological vital signs recordingsI Problems: artifact corruption, false alarms, amount of dataI Goal: Determine the state of health of the patient,

uncorrupted vital signs

Image source: Wikipedia Intensive Care Unit page

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Overview

I Factorial Switching Linear Dynamical SystemI Inference and LearningI FSLDS and DSLDSI Novel RegimesI DataI ResultsI Summary

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Factors Affecting MeasurementsI The physiological observations are affected by different

factors.I Factors can be artifactual or physiological.

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Arterial blood sample Bradycardia

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Factorial Switching Linear Dynamical System

Artifactual state

Physiological state

Observations

Physiological factors

Artifactual factors

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FSLDS notation

I st is the switch variable, which indexes factor settings, e.g.‘blood sample occurring and first stage of TCPrecalibration’.

I xt is the hidden continuous state at time t . This containsinformation on the true physiology of the baby, and on thelevels of artifactual processes.

I y1:t are the observations.

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Kalman filtering

I Continuous hidden state affects some observations:

xt ∼ N (Axt−1,Q)

yt ∼ N (Cxt ,R)

I Kalman filter equations can be used to work computep(x1:t |y1:t)

I Done iteratively by predicting and updating

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Switching dynamics

I The switch variable st selects the dynamics for a particularcombination of factor settings:

xt ∼ N (A(st )xt−1,Q(st ))

yt ∼ N (C(st )xt ,R(st ))

I For each setting of st , the Kalman filter equations give apredictive distribution for xt .

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Factor interactions

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Related work

I Switching linear dynamical models have been studied bymany authors, e.g. Alspach and Sorenson (1972),Ghahramani and Hinton (1996).

I Applications include fault detection in mobile robots (deFreitas et al., 2004), speech recognition (Droppo andAcero, 2004), industrial monitoring (Morales-Menedez etal., 2002).

I A two-factor FSLDS was used for speech recognition byMa and Deng (2004). Factorised SLDS also used formusical transcription (Cemgil et al., 2006).

I There has been previous work on condition monitoring inthe ICU, though we are unaware of any previous studiesthat use a FSLDS.

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Inference and Learning

I For this application, we are interested in filtering, inferringp(st ,xt |y1:t)

I Exact inference is intractable (Lerner and Parr, 2001)I We use the Gaussian sum approximation (e.g. Murphy,

1998)I Learning uses labelled data for different regimes, and

overwriting order of factors

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Example inference results

I Can examine variance of estimates of true physiology x̂t ,e.g. for blood sample (left) and temperature probedisconnection (right):

Time (s)

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TD0 500 1000

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e te

mp.

(°C

)35

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Models: FSLDS, DSLDS

DSLDS (Georgatzis and Williams, UAI 2015)

I st is predicted with a classifierI Inference for xt is similar to FSLDSI α-mixture combines FSLDS and DSLDS

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FSLDS and DSLDS: pros and cons

+ Knowledge engineering tells us how the factors interactgeneratively

+ There is not very much labelled data+ Normality varies per patient (multi-task learning)- In the DSLDS discrete state distributions are predicted

directly, rather than inferred. Can encode knowledge withinformative features.

- Some events (esp. artifactual) might be easier to identifywith a discriminative approach. Harder to come up with agenerative model.

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Novel RegimesI There are many other factors influencing the data: drugs,

sepsis, neurological problems...

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?

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Known UnknownsI Add a factor to represent abnormal dynamics

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Known UnknownsI Add a factor to represent abnormal dynamics

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X-factor for static 1-D data

I For static data, we can use a modelM∗ representing‘abnormal’ data points.

y

p(y|

s)

I The high-variance model wins when the data is not wellexplained by the original model

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X-factor with known factors

I The X-factor can be applied to the static data inconjunction with known factors (green):

y

p(y|

s)

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X-factor for dynamic data

xt ∼ N (Axt−1,Q)

yt ∼ N (Cxt ,R)

I Can construct an ‘abnormal’ dynamic regime analogously:

Normal dynamics: {A,Q,C,R}

X-factor dynamics: {A,ξQ,C,R}, ξ > 1.

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Spectral view of the X-factor

f

S y(f)

0 1/2

I Plot shows the spectrum of a hidden AR(5) process, andaccompanying X-factor

I More power at every frequencyI Dynamical analogue of the static 1-D case

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Data

I 27 patients from Neuro ICU in the Southern GeneralHospital, Glasgow (15 TBI, 12 SAH)

I Channels:I arterial blood pressure (ABP)I electrocardiogram (ECG)I pulse oximetryI intracranial pressure (ICP)I end tidal CO2 (EtCO2)I respiratory signal (Resp)

I Downsampled to 1 Hz

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Annotation

I 46 event-types labelled, including blood sample, dampedtrace, patient turning and suctioning

I Damped trace events have a mean duration of over 8hours per patient

I Other significant events: blood sample, patient turning andsuctioning, noisy channels, preparation for or return fromtransfer

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Processing pipeline

Extraction from

ICU databasePreprocessing FSLDS

Stabilitydetection

I Made to work all together on ICU serverI System operates at ∼ 10× realtimeI Stability detection: need to estimate AR/ARMA parameters

for every patient individually for the stability regimeI This is done by predicting intervals that are stable vs

non-stable, and using these to learn the stability regimeI Software available at https://datashare.is.ed.ac.uk/handle/10283/855

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Results

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False positive rate

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Blood sample

FSLDSDSLDSalpha−combination

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Damped

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False positive rate

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Suction

FSLDSDSLDSalpha−combination

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AUC BS DT SC X

DSLDS 0.94 0.78 0.64 0.56FSLDS 0.86 0.77 0.60 0.60α-mixture 0.95(0.9) 0.79(0.9) 0.64(−∞) 0.61(1.4)

I Blood sample performance is very good, and is potentiallyuseful for silencing false alarms

I Damped trace is particularly interesting as it has significantduration and is not an event caused by nursinginterventions; it is therefore particularly helpful to flag up

I Suction events are complex and have a variable timecourse. Also suction and position change events can havesimilar effects on the patient. Position change was notmodelled with a factor in our experiments, thus it may notbe surprising if these two event types are confused

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Damped Trace Example

True X

True SC

True BS

True DT

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Patient damped_trace_demo

Dia.MeanSys.

X −− DSLDS

X −− FSLDS

X −− alpha

SC −− DSLDS

SC −− FSLDS

SC −− alpha

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BS −− FSLDS

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DT −− FSLDS

DT −− alpha

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I Note imputed x-stateI Our clinicians believe that showing imputed state and

flagging up artifact would be helpful

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Blood Sample Example

True X

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Patient blood_sample_demo

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X −− DSLDS

X −− FSLDS

X −− alpha

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SC −− FSLDS

SC −− alpha

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DT −− FSLDS

DT −− alpha

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Summary

I Quantification of the amount of artifact in this dataset,importance of damped trace events

I AUC scores are very high for blood samples (0.95), goodfor damped trace (0.79), and poor for suction (0.64) andX-factor (0.61) events

I Successful implementation of a real-time system carryingout FSLDS analysis on the raw data coming from the ICU

I FSLDS/DSLDS models can be applied to other ICUmonitoring tasks (e.g. identifying sepsis) and moregenerally

I We are also developing models for the effect ofinterventions (e.g. drug administration)

Funding: Chief Scientist Office (Scotland) CHZ/4/801

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References

I Factorial Switching Linear Dynamical Systems applied toPhysiological Condition Monitoring.John A. Quinn, Christopher K.I. Williams, Neil McIntosh. IEEETrans. on Pattern Analysis and Machine Intelligence 31(9) pp1537-1551 (2009).

I Discriminative Switching Linear Dynamical Systems applied toPhysiological Condition Monitoring. Konstantinos Georgatzis,Christopher K. I. Williams, Proc UAI 2015.

I Detecting Artifactual Events in Vital Signs Monitoring Data.Partha Lal, Christopher K. I. Williams, Konstantinos Georgatzis,Christopher Hawthorne, Paul McMonagle, Ian Piper, MartinShaw. Tech report, September 2015.

I Available from http://homepages.inf.ed.ac.uk/ckiw/

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