Journal
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
Volume 60, Issue 1, Pages 193-197Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBME.2012.2208459
Keywords
e-health; Gaussian processes; patient monitoring; personalized healthcare; wearable sensors
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Funding
- National Institute for Health Research (NIHR) Biomedical Research Centre Programme, Oxford
- Centre of Excellence in Personalised Healthcare
- Wellcome Trust
- Engineering and Physical Sciences Research Council (EPSRC) [WT 088877/Z/09/Z]
- EPSRC [EP/F058845/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/H019944/1, 985352, EP/F058845/1] Funding Source: researchfish
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Advances in wearable sensing and communications infrastructure have allowed the widespread development of prototype medical devices for patient monitoring. However, such devices have not penetrated into clinical practice, primarily due to a lack of research into intelligent analysis methods that are sufficiently robust to support large-scale deployment. Existing systems are typically plagued by large false-alarm rates, and an inability to cope with sensor artifact in a principled manner. This paper has two aims: 1) proposal of a novel, patient-personalized system for analysis and inference in the presence of data uncertainty, typically caused by sensor artifact and data incompleteness; 2) demonstration of the method using a large-scale clinical study in which 200 patients have been monitored using the proposed system. This latter provides much-needed evidence that personalized e-health monitoring is feasible within an actual clinical environment, at scale, and that the method is capable of improving patient outcomes via personalized healthcare.
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