Identifying predictive health events in temporal sequences using recurrent neural network
First Claim
1. A method comprising:
- obtaining a plurality of initial temporal sequences of health events,wherein each of the plurality of initial temporal sequences of health events is associated with a different patient, andwherein each of the initial temporal sequences comprises respective health-related data associated with the patient that is associated with the initial temporal sequence at each of a plurality of time steps;
processing each of the plurality of initial temporal sequences of health events using a recurrent neural network to generate, for each of the initial temporal sequences, a respective network internal state of the recurrent neural network for each time step in the initial temporal sequence,wherein the recurrent neural network has been trained to receive input temporal sequences and, for each time step in each input temporal sequence, generate a network internal state for the time step and generate a prediction about the patient associated with the input temporal sequence from the network internal state for the time step;
storing, for each of the plurality of initial temporal sequences, one or more of the network internal states generated for the time steps in the temporal sequence in an internal state repository,wherein the internal state repository is configured to;
store one or more network internal states generated at various time steps in various temporal sequences, andassociate each network internal state with data identifying the time step and the temporal sequence for which each network internal state was generated;
obtaining a current temporal sequence of health events that is associated with a current patient;
processing the current temporal sequence of health events using the recurrent neural network to generate a sequence internal state for the current temporal sequence, wherein the sequence internal state is a network internal state for a last time step in the current temporal sequence;
computing, for each of the plurality of network internal states in the internal state repository, a respective similarity measure between the network internal state and the sequence internal state; and
selecting, as temporal sequences that are likely to include health events that are predictive of future health events that may become associated with the current patient, the initial temporal sequences corresponding to one or more most similar network internal states according to the similarity measure.
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Abstract
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using recurrent neural networks to analyze health events. One of the methods includes: processing each of a plurality of initial temporal sequences of health events to generate, for each of the initial temporal sequences, a respective network internal state of a recurrent neural network for each time step in the initial temporal sequence; storing, for each of the initial temporal sequences, one or more of the network internal states for the time steps in the temporal sequence in a repository; obtaining a first temporal sequence; processing the first temporal sequence using the recurrent neural network to generate a sequence internal state for the first temporal sequence; and selecting one or more initial temporal sequences that are likely to include health events that are predictive of future health events in the first temporal sequence.
31 Citations
18 Claims
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1. A method comprising:
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obtaining a plurality of initial temporal sequences of health events, wherein each of the plurality of initial temporal sequences of health events is associated with a different patient, and wherein each of the initial temporal sequences comprises respective health-related data associated with the patient that is associated with the initial temporal sequence at each of a plurality of time steps; processing each of the plurality of initial temporal sequences of health events using a recurrent neural network to generate, for each of the initial temporal sequences, a respective network internal state of the recurrent neural network for each time step in the initial temporal sequence, wherein the recurrent neural network has been trained to receive input temporal sequences and, for each time step in each input temporal sequence, generate a network internal state for the time step and generate a prediction about the patient associated with the input temporal sequence from the network internal state for the time step; storing, for each of the plurality of initial temporal sequences, one or more of the network internal states generated for the time steps in the temporal sequence in an internal state repository, wherein the internal state repository is configured to; store one or more network internal states generated at various time steps in various temporal sequences, and associate each network internal state with data identifying the time step and the temporal sequence for which each network internal state was generated; obtaining a current temporal sequence of health events that is associated with a current patient; processing the current temporal sequence of health events using the recurrent neural network to generate a sequence internal state for the current temporal sequence, wherein the sequence internal state is a network internal state for a last time step in the current temporal sequence; computing, for each of the plurality of network internal states in the internal state repository, a respective similarity measure between the network internal state and the sequence internal state; and selecting, as temporal sequences that are likely to include health events that are predictive of future health events that may become associated with the current patient, the initial temporal sequences corresponding to one or more most similar network internal states according to the similarity measure. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A system comprising one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the one or more computers to perform operations comprising:
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obtaining a plurality of initial temporal sequences of health events, wherein each of the plurality of initial temporal sequences of health events is associated with a different patient, and wherein each of the initial temporal sequences comprises respective health-related data associated with the patient that is associated with the initial temporal sequence at each of a plurality of time steps; processing each of the plurality of initial temporal sequences of health events using a recurrent neural network to generate, for each of the initial temporal sequences, a respective network internal state of the recurrent neural network for each time step in the initial temporal sequence, wherein the recurrent neural network has been trained to receive input temporal sequences and, for each time step in each input temporal sequence, generate a network internal state for the time step and generate a prediction about the patient associated with the input temporal sequence from the network internal state for the time step; storing, for each of the plurality of initial temporal sequences, one or more of the network internal states generated for the time steps in the temporal sequence in an internal state repository, wherein the internal state repository is configured to; store one or more network internal states generated at various time steps in various temporal sequences, and associate each network internal state with data identifying the time step and the temporal sequence for which each network internal state was generated; obtaining a current temporal sequence of health events that is associated with a current patient; processing the current temporal sequence of health events using the recurrent neural network to generate a sequence internal state for the current temporal sequence, wherein the sequence internal state is a network internal state for a last time step in the current temporal sequence; obtaining a current temporal sequence of health events that is associated with a current patient; processing the current temporal sequence of health events using the recurrent neural network to generate a sequence internal state for the current temporal sequence, wherein the sequence internal state is a network internal state for a last time step in the current temporal sequence; computing, for each of the plurality of network internal states in the internal state repository, a respective similarity measure between the network internal state and the sequence internal state; and selecting, as temporal sequences that are likely to include health events that are predictive of future health events that may become associated with the current patient, the initial temporal sequences corresponding to one or more most similar network internal states according to the similarity measure. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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15. One or more non-transitory computer storage media storing instructions that when executed by one or more computers cause the one or more computers to perform operations comprising:
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obtaining a plurality of initial temporal sequences of health events, wherein each of the plurality of initial temporal sequences of health events is associated with a different patient, and wherein each of the initial temporal sequences comprises respective health-related data associated with the patient that is associated with the initial temporal sequence at each of a plurality of time steps; processing each of the plurality of initial temporal sequences of health events using a recurrent neural network to generate, for each of the initial temporal sequences, a respective network internal state of the recurrent neural network for each time step in the initial temporal sequence, wherein the recurrent neural network has been trained to receive input temporal sequences and, for each time step in each input temporal sequence, generate a network internal state for the time step and generate a prediction about the patient associated with the input temporal sequence from the network internal state for the time step; storing, for each of the plurality of initial temporal sequences, one or more of the network internal states generated for the time steps in the temporal sequence in an internal state repository, wherein the internal state repository is configured to; store one or more network internal states generated at various time steps in various temporal sequences, and associate each network internal state with data identifying the time step and the temporal sequence for which each network internal state was generated; obtaining a current temporal sequence of health events that is associated with a current patient; processing the current temporal sequence of health events using the recurrent neural network to generate a sequence internal state for the current temporal sequence, wherein the sequence internal state is a network internal state for a last time step in the current temporal sequence; obtaining a current temporal sequence of health events that is associated with a current patient; processing the current temporal sequence of health events using the recurrent neural network to generate a sequence internal state for the current temporal sequence, wherein the sequence internal state is a network internal state for a last time step in the current temporal sequence; computing, for each of the plurality of network internal states in the internal state repository, a respective similarity measure between the network internal state and the sequence internal state; and selecting, as temporal sequences that are likely to include health events that are predictive of future health events that may become associated with the current patient, the initial temporal sequences corresponding to one or more most similar network internal states according to the similarity measure. - View Dependent Claims (16, 17, 18)
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Specification