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AI Biotech/Diagnostics: Cardio
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Application US20190133480
Published 2019-05-09
Discretized Embeddings Of Physiological Waveforms
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.
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- 1. A method implemented at least in part by one or more processors, comprising:
obtaining a first continuous stream of samples measured by one or more physiological sensors; discretizing the first continuous stream of samples to generate a training sequence of quantized beats; determining a training sequence of vectors corresponding to the training sequence of quantized beats, wherein each vector of the training sequence of vectors is determined based on a respective quantized beat of the training sequence of quantized beats and an embedding matrix; associating a label with each vector of the training sequence of vectors, wherein each label is indicative of a medical condition that is evidenced by samples of the first continuous stream of samples obtained during a time interval associated with the respective vector of the training sequence of vectors; applying the training sequence of vectors as input across a neural network to generate corresponding instances of training output; comparing each instance of training output to the label that is associated with the corresponding vector of the training sequence of vectors; based on the comparing, training the neural network and the embedding matrix; obtaining a second continuous stream of samples from one or more of the physiological sensors; discretizing the second continuous stream of samples to generate a live sequence of quantized beats; determining a live sequence of vectors corresponding to the live sequence of quantized beats, wherein each vector of the live sequence of vectors is determined based on a respective quantized beat and the embedding matrix; applying the live sequence of vectors as input across the neural network to generate corresponding instances of live output; and providing, at one or more output devices operably coupled with one or more of the processors, information indicative of the live output.
- 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:
obtaining a first continuous stream of samples measured by one or more physiological sensors; discretizing the first continuous stream of samples to generate a training sequence of quantized beats; determining a training sequence of vectors corresponding to the training sequence of quantized beats, wherein each vector of the training sequence of vectors is determined based on a respective quantized beat of the training sequence of quantized beats and an embedding matrix; associating a label with each vector of the training sequence of vectors, wherein each label is indicative of a medical condition that is evidenced by samples of the first continuous stream of samples obtained during a time interval associated with the respective vector of the training sequence of vectors; applying the training sequence of vectors as input across a neural network to generate corresponding instances of training output; comparing each instance of training output to the label that is associated with the corresponding vector of the training sequence of vectors; based on the comparing, training the neural network and the embedding matrix; obtaining a second continuous stream of samples from one or more of the physiological sensors; discretizing the second continuous stream of samples to generate a live sequence of quantized beats; determining a live sequence of vectors corresponding to the live sequence of quantized beats, wherein each vector of the live sequence of vectors is determined based on a respective quantized beat and the embedding matrix; applying the live sequence of vectors as input across the neural network to generate corresponding instances of live output; and providing, at one or more output devices operably coupled with one or more of the processors, information indicative of the live output.
- 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:
obtaining a first continuous stream of samples measured by one or more physiological sensors; discretizing the first continuous stream of samples to generate a training sequence of quantized beats; determining a training sequence of vectors corresponding to the training sequence of quantized beats, wherein each vector of the training sequence of vectors is determined based on a respective quantized beat of the training sequence of quantized beats and an embedding matrix; associating a label with each vector of the training sequence of vectors, wherein each label is indicative of a medical condition that is evidenced by samples of the first continuous stream of samples obtained during a time interval associated with the respective vector of the training sequence of vectors; applying the training sequence of vectors as input across a neural network to generate corresponding instances of training output; comparing each instance of training output to the label that is associated with the corresponding vector of the training sequence of vectors; based on the comparing, training the neural network and the embedding matrix; obtaining a second continuous stream of samples from one or more of the physiological sensors; discretizing the second continuous stream of samples to generate a live sequence of quantized beats; determining a live sequence of vectors corresponding to the live sequence of quantized beats, wherein each vector of the live sequence of vectors is determined based on a respective quantized beat and the embedding matrix; applying the live sequence of vectors as input across the neural network to generate corresponding instances of live output; and providing, at one or more output devices operably coupled with one or more of the processors, information indicative of the live output.
