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1.发明公开PREDICTING LIKELIHOODS OF CONDITIONS BEING SATISFIED USING RECURRENT NEURAL NETWORKS 审中-公开用递归神经网络预测条件满足的可能性
公开(公告)号：EP3292492A1
公开(公告)日：2018-03-14
申请号：EP16748417.9
申请日：2016-07-26
申请人： Google Inc.
CPC分类号： G06N3/0472 , G06F19/00 , G06N3/02 , G06N3/0427 , G06N3/0445 , G06N3/063 , G16H50/20
摘要： Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting likelihoods of conditions being satisfied using recurrent neural networks. One of the systems is configured to process a temporal sequence comprising a respective input at each of a plurality of time steps and comprises: one or more recurrent neural network layers; one or more logistic regression nodes, wherein each of the logistic regression nodes corresponds to a respective condition from a predetermined set of conditions, and wherein each of the logistic regression nodes is configured to, for each of the plurality of time steps: receive the network internal state for the time step; and process the network internal state for the time step in accordance with current values of a set of parameters of the logistic regression node to generate a future condition score for the corresponding condition for the time step.
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公开(公告)号：EP3274888A1
公开(公告)日：2018-01-31
申请号：EP16747965.8
申请日：2016-07-26
申请人： Google Inc.
CPC分类号： G06N3/08 , G06F19/00 , G06N3/02 , G06N3/04 , G06N3/0445 , G06N3/049 , G06N3/084 , G06N3/10 , G16H50/20 , G16H50/30
摘要： 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.
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