Invention Grant
- Patent Title: Identifying predictive health events in temporal sequences using recurrent neural network
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Application No.: US15595644Application Date: 2017-05-15
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Publication No.: US10402721B2Publication Date: 2019-09-03
- Inventor: Gregory Sean Corrado , Jeffrey Adgate Dean
- Applicant: Google LLC
- Applicant Address: US CA Mountain View
- Assignee: Google LLC
- Current Assignee: Google LLC
- Current Assignee Address: US CA Mountain View
- Agency: Fish & Richardson P.C.
- Main IPC: G06N3/02
- IPC: G06N3/02 ; G06N3/04 ; G06N3/08 ; G06N3/10 ; G16H50/20 ; G16H50/30

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.
Public/Granted literature
- US20170316313A1 ANALYZING HEALTH EVENTS USING RECURRENT NEURAL NETWORKS Public/Granted day:2017-11-02
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