公开(公告)号：US10770180B1

公开(公告)日：2020-09-08

申请号：US16712947

申请日：2019-12-12

Applicant: Google LLC

Inventor： Jonas Beachey Kemp ,  Andrew M. Dai ,  Alvin Rishi Rajkomar

IPC: G16H50/20 ,  G06N3/04 ,  G16H10/60 ,  G16H50/30

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting future patient health using neural networks. One of the methods includes receiving electronic health record data for a patient; generating a respective observation embedding for each of the observations, comprising, for each clinical note: processing the sequence of tokens in the clinical note using a clinical note embedding LSTM to generate a respective token embedding for each of the tokens; and generating the observation embedding for the clinical note from the token embeddings; generating an embedded representation, comprising, for each time window: combining the observation embeddings of observations occurring during the time window to generate a patient record embedding; and processing the embedded representation of the electronic health record data using a prediction recurrent neural network to generate a neural network output that characterizes a future health status of the patient.

公开(公告)号：US11742087B2

公开(公告)日：2023-08-29

申请号：US16990172

申请日：2020-08-11

Applicant: Google LLC

Inventor： Jonas Beachey Kemp ,  Andrew M. Dai ,  Alvin Rishi Rajkomar

IPC: G16H50/20 ,  G06N3/049 ,  G16H10/60 ,  G16H50/30

CPC classification number: G16H50/20 ,  G06N3/049 ,  G16H10/60 ,  G16H50/30

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting future patient health using neural networks. One of the methods includes receiving electronic health record data for a patient; generating a respective observation embedding for each of the observations, comprising, for each clinical note: processing the sequence of tokens in the clinical note using a clinical note embedding LSTM to generate a respective token embedding for each of the tokens; and generating the observation embedding for the clinical note from the token embeddings; generating an embedded representation, comprising, for each time window: combining the observation embeddings of observations occurring during the time window to generate a patient record embedding; and processing the embedded representation of the electronic health record data using a prediction recurrent neural network to generate a neural network output that characterizes a future health status of the patient.

公开(公告)号：US20210125721A1

公开(公告)日：2021-04-29

申请号：US16990172

申请日：2020-08-11

Applicant: Google LLC

Inventor： Jonas Beachey Kemp ,  Andrew M. Dai ,  Alvin Rishi Rajkomar

IPC: G16H50/20 ,  G06N3/04 ,  G16H10/60 ,  G16H50/30

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting future patient health using neural networks. One of the methods includes receiving electronic health record data for a patient; generating a respective observation embedding for each of the observations, comprising, for each clinical note: processing the sequence of tokens in the clinical note using a clinical note embedding LSTM to generate a respective token embedding for each of the tokens; and generating the observation embedding for the clinical note from the token embeddings; generating an embedded representation, comprising, for each time window: combining the observation embeddings of observations occurring during the time window to generate a patient record embedding; and processing the embedded representation of the electronic health record data using a prediction recurrent neural network to generate a neural network output that characterizes a future health status of the patient.

公开(公告)号：US20230334306A1

公开(公告)日：2023-10-19

申请号：US16794087

申请日：2020-02-18

Applicant: Google LLC

Inventor： Kun Zhang ,  Andrew M. Dai ,  Yuan Xue ,  Alvin Rishi Rajkomar ,  Gerardo Flores

IPC: G16H10/60 ,  G06N3/08

CPC classification number: G06N3/08 ,  G16H10/60

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting future patient health using a recurrent neural network. In particular, at each time step, a network input for the time step is processed using a recurrent neural network to update a hidden state of the recurrent neural network. Specifically, the hidden state of the recurrent neural network is partitioned into a plurality of partitions and the plurality of partitions comprises a respective partition for each of a plurality of possible observational features.

公开(公告)号：US20190294973A1

公开(公告)日：2019-09-26

申请号：US16363891

申请日：2019-03-25

Applicant: Google LLC

Inventor： Anjuli Patricia Kannan ,  Kai Chen ,  Alvin Rishi Rajkomar

IPC: G06N3/08 ,  G06N3/04 ,  G06F17/27

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training conversational turn analysis neural networks. One of the methods includes obtaining unsupervised training data comprising a plurality of dialogue transcripts; training a turn prediction neural network to perform a turn prediction task on the unsupervised training data using unsupervised learning, wherein: the turn prediction neural network comprises (i) a turn encoder neural network and (ii) a turn decoder neural network; obtaining supervised training data; and training a supervised prediction neural network to perform a supervised prediction task on the supervised training data using supervised learning.