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Gaussian Processes for Monitoring Patients with Mobile Sensors
- Abstract:
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As wearable physiological sensors become more common, there is a need for algorithms that can use the resulting waveforms to perform robust data analysis. Existing techniques have failed to penetrate into clinical practice due to their perceived lack of robustness. This presentation will argue that the natural framework for inference with noisy, incomplete data is that of Bayesian Gaussian processes. We describe the use of multi-task algorithms for monitoring patients via wearable sensors. Su...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(pdf, 302.3KB)
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Bibliographic Details
- Publisher:
- IEEE Publisher's website
- Series:
- Proc. IEEE Engineering in Medicine and Biology Conf., Milan, Italy, 2015
- Host title:
- 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
- Publication date:
- 2015-01-01
- Source identifiers:
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541427
Item Description
- Pubs id:
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pubs:541427
- UUID:
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uuid:ca54334c-1f98-4c8a-82cd-cd66f040f342
- Local pid:
- pubs:541427
- Deposit date:
- 2015-09-02
Terms of use
- Copyright holder:
- IEEE
- Copyright date:
- 2015
- Notes:
- Presented at 10.M3 mHealth Review: Cross-Disciplinary Technologies, Deployments and Future Trends, Friday, August 28th 2015. Copyright © 2015 IEEE.
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