Abstract: |
Techniques described herein relate to training and applying predictive models using discretized physiological sensor data. In various embodiments, a continuous stream of samples measured by a physiological sensor may be discretized into a training sequence of quantized beats. A training sequence of vectors determined based on the training sequence of quantized beats and an embedding matrix may be associated with labels indicative of medical conditions, and applied as input across a neural network to generate corresponding instances of training output. Based on a comparison of each instance of training output with a respective label, the neural network and the embedding matrix may be trained and used to predict medical conditions from unlabeled continuous streams of physiological sensor samples. In some embodiments, the trained embedding matrix may be visualized to identify correlations between medical conditions and physiological signs. |
Inventor: |
Rahman, Asif (Brookline, MA, US); Conroy, Bryan (Garden City South, NY, US) |
Applicant: |
KONINKLIJKE PHILIPS N.V. (Eindhoven, NL) |
Face Assignee: |
N/A |
Filed: |
2018-11-02 |
Issued: |
2019-05-09 |
Claims: |
20 |
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US20190133480
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1. A method implemented at least in part by one or more processors, comprising:
(8)
(12)
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12. A system comprising one or more processors and memory operably coupled with the one or more processors, wherein the memory stores instructions that, in response to execution of the instructions by one or more processors, cause the one or more processors to perform the following operations:
(6)
(12)
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20. At least one non-transitory computer-readable medium comprising instructions that, in response to execution of the instructions by one or more processors, cause the one or more processors to perform the following operations:
(0)
(12)
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