Dr. David A. Clifton, College Lecturer Institute of Biomedical Engineering University of Oxford

I have a neural network processor.

 23,000 preventable cardiac arrests occur every year in UK hospitals  20,000 readmissions into ICU every year – mortality 50%  The majority of these occur because physiological deterioration goes undetected – why?

 Level 3:ICU1 : 1  Level 2: Step-down1 : 4  Level 1: Acute wards1 : 4  Level 0: General wards1 : 10  Level -1: Home1 : ?  Patient monitors generate very high numbers of false alerts (e.g. 86% of alerts)

 Existing methods apply simple thresholds to each parameter  Intolerant to transient noise  Possibly not the appropriate domain (time, frequency)  Where do we set these thresholds in a principled, reliable manner?  Nurses & junior doctors trained to ignore alarms  Rolls-Royce has deactivated conventional automated methods

 EEG / GCS  Heart rate  Breathing rate  SpO2  Blood pressure  Temperature

 Obvious tachycardia  Obvious tachypnea  Obvious desaturations  Obvious hypotension  Obviously unconscious  Abnormalities were detected by clinicians, patient escalated.  Note the difficulties:  Incomplete data  Noisy data  Varying sample rates

 Gradual deterioration  Is this patient getting worse?  Should we make a call to emergency teams?  Patient unescalated, died soon after.

 How can we detect abnormality in patient biomedical signals?  How can we do it in a reliable way?  What are the pitfalls that we have to avoid?  How can we evaluate it?

 Plenty more to look forward to: machine learning in biomedical engineering

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