Dr. David A. Clifton W.K. Kellogg Research Fellow in Biomedical Engineering Institute of Biomedical Engineering, University of Oxford

Transcription

1 Dr. David A. Clifton W.K. Kellogg Research Fellow in Biomedical Engineering Institute of Biomedical Engineering, University of Oxford

2 The clinical need Method: machine learning for vital-signs monitoring Results and clinical trials so far

3 The clinical need Method: machine learning for vital-signs monitoring Results and clinical trials so far

4 23,000 preventable cardiac arrests occur every year in UK hospitals 20,000 unplanned ICU readmissions every year Between 5% and 24% survival rate The majority of these occur because physiological deterioration goes undetected... Why?

5 Summary of existing state-of-the-art: Patients need monitoring, because the nurse:patient ratio decreases from 1:1 outside ICU Existing bed-side patient monitors have 86% false-positive rate, and are ignored Recommended track-and-trigger systems are incomplete, with no evidence base (heuristic) Therefore: intelligent patient monitoring, with reliable false-alarm rates

6 The clinical need Method: machine learning for vital-signs monitoring Results and clinical trials so far

7 Heart rate (ECG + Oximetry) Breathing rate (ECG + IP) SpO2 (Oximetry) Blood pressure (Cuff) Temperature (Cuff)

8 Conventional methods are useful for one-off binary diagnostic tests Does this mammogram indicate the possible presence of cancer? Does this blood sample indicate the presence of condition XYZ? Continuous vital-signs monitoring is a different problem we are effectively making one test every second While normality is well-understood, the abnormal classes are numerous, highly variable between patients, and often under-represented Novelty detection (one-class classification), models normality and then tests for deviation from this model

9 Heart rate Breathing rate SpO2 Blood pressure Temperature Training Population Density estimation

10 Density estimation Parzen window estimator (place multivariate, isotropic Gaussian kernels on each of the training data) (e.g.) Gaussian Mixture Model (a small number of multivariate Gaussian kernels with independent covariance) Unconditional pdf, p(x), in d dimensions (typically d = 5)

11 When Visensia is used to monitor a high-risk patient, an alert is generated whenever the vital signs are about to go outside the boundaries of normality How can we define the boundaries of normality in a principled manner?

12 Is this window of (5-dimensional) data normal? 5 minutes p(x) Extremum (least probable) x 1 x 2 x 3 x 4 x 5 x 6 x 7 x 8 x 9 x 10 If this window of data were normal, then what is the most extreme value that any of its data could take? In other words: what is the distribution of this window s extremum?

13 Extreme Value Theory (EVT) characterises the tails of distributions Determines our expectation of where extrema generated from some pdf, p(x), will lie allows us to determine where to set the alarm threshold Previously proven for health monitoring of jet engines (using engine pressures, engine temperatures, etc.)

14 where (Please see IEEE MLSP 2009 papers for a full treatment...)

15 (Example 3-D model of normality)

16 (Experimental extrema)

17 (Prediction from new EVT)

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20 PSI Patient Status Index Vital-sign data

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22 The clinical need Method: machine learning for vital-signs monitoring Results and clinical trials so far

23 There were 0.94 false alerts per 100 hours of monitoring This corresponds to a false alert rate of 0.23 per patient per day. Our data fusion model automatically switches to a lowerdimensional model when a parameter is artifactual or missing This makes the technology usable by the nursing team

24 Hravnak et al, MET Conference, Toronto (2009) Three-fold reduction in the number of patients becoming critically unstable for a sustained period of time (17.8% in Phase 1, 5.2% in Phase 3) Data fusion system was not withdrawn from the SDU at the end of the 6-month trial No unexpected fatal cardiac arrests in last 18 months (compared with 50 in previous 18 months, prior to introduction of data fusion technology)

25 Many preventable deaths occur in hospitals because of unnoticed deterioration Existing methods are failing, intelligent monitoring approaches are required Extreme Value Theory is an excellent fit to the problem, but is too simple (univariate, unimodal) We have extended EVT to multivariate, multimodal real-world datasets Useful for identifying periods of abnormal deterioration, if we are willing to make i.i.d. assumptions

26 Relaxing the i.i.d. assumption time-series novelty detection, using order statistics & Gaussian processes Monitoring of ambulatory post-operative cancer patients in the home (Oxford Cancer Hospital) Monitoring of haemodialysis patients at home, between sessions, to improve their dialysis and, ultimately, to provide home dialysis (Oxford Churchill Renal Unit) Oxfordshire-wide community hospitals project, in which patients are sent home with ambulatory patient monitoring systems, and whom then undergo teleconsulting and telemedicine (Oxford Radcliffe Trust & Oxford Social Services)

27 Dr. David A. Clifton W.K. Kellogg Research Fellow in Biomedical Engineering Institute of Biomedical Engineering, University of Oxford