Medical Malware - Machine Learning in the UK

Transcription

1 Machine Learning for Robust Mobile Monitoring Dr. David A. Clifton Group Leader Computational Health Informatics (CHI) Laboratory Faculty Member Department of Engineering Science, Oxford Governing Body Fellow, Balliol College

2 Data from GPs / clinics Social care data Electronic health record Smart patches, smart pills, & smartphones Patient monitors Clinicians notes Lightweight sensors Lab tests Point-of-care genomics

3 Data from GPs / clinics Social care data Electronic health record Smart patches, smart pills, & smartphones Patient monitors Clinicians notes Lightweight sensors Lab tests Point-of-care genomics

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5 Early warning of patient deterioration in the UK NHS

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8 Recent trial in Oxford University Hospitals of 10,000 emergency patients Revealed that nurses spent the same amount of time with patients but that our early warning systems caused them to focus their time on the at-risk patients

9 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critically unstable for a sustained period of time (17.8% in Phase 1, 5.2% in Phase 3) Data fusion system was not withdrawn from the hospital at the end of the 6-month trial ( It would be unethical not to use it. ) No unexpected fatal cardiac arrests in last 18 months (compared with 50 in previous 18 months, prior to introduction of data fusion technology)

10 Sensor data Nurse observations Gaussian process Existing methods cannot cope with sensor failure, sensor degradation, and artefact - common in mobile data These prevent mobile systems from being used in healthcare

11 We can discover the patterns underlying patient data, to allow us to detect at-risk patients

12 Detect deteriorating patients by comparing mobile data to our Bayesian models Sensor data Model (Various existing systems) Results from a 3-year study of cancer surgery patients in Oxford University Hospitals

13 Patients dislike sensors so acquire vital signs via video

14 joint NHS / OU spin-out company formed in 2013

15 Seamless technology integration Pilot & RCT in Oxford 16

16 HCP!Patient communications Personalisation Messages sent from the nurse are customised to the user Pilot & RCT in Oxford

17 Massively-multivariate machine learning for tracking infectious disease linking the genome to the EHR Collaborating with clinicians, including Prof. Derrick Crook Director of Public Health England (UK CDC) Infectious disease is as great a threat to national security as climate change UK Chief Medical Officer, m tuberculosis deaths last year, infects 1/3 rd of world population No new classes of antibiotics discovered since the late 1980s Testing takes up 2 months and a microbiology lab

18 Massively-multivariate machine learning for tracking infectious disease linking the genome to the EHR Collaborating with clinicians, including Prof. Derrick Crook Director of Public Health England (UK CDC) We are developing machine learning methods for whole-genome analysis, linked to physiological data: identifying novel pathogens (novelty detection) estimating virulence (how quickly will it spread?) discovering new genomic biomarkers sponsored by NHS, Wellcome Trust, Microsoft

19 Big Data Institute, Oxford UK Dept. of Health, 3-year programme for Hospital-Wide Early Warning Systems UK NHS, 5-year programme for Health Protection Research Unit Oxford University Research Centre, Suzhou The George Institute, Oxford & Beijing Beijing University, International Hospital

20 CHI Lab, Oxford Collaborators: Prof. Lionel Tarassenko Prof. Gari Clifford Dr. Peter Watkinson Dr. Duncan Young Dr. Richard Pullinger Prof. Derrick Crook Prof. Tim Peto Prof. Fiona Powrie FRS Dr. Holm Uhlig (Oxford) (Emory University, USA) (NHS) (NHS) (NHS) (NHS) (NHS) (NHS) (NHS) Glen Wright Colopy Heloise Greeff Marketa Tomkova Support: UK Department of International Development UK Department of Health UK Foreign & Commonwealth Office Wellcome Trust China Mobile Research ARM Microsoft Researchers Marco Pimentel Mauro Santos Tingting Zhu Jakub Tomek Kate Niehaus Farah Colchester Robert Duerichen