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：US20210034973A1

公开(公告)日：2021-02-04

申请号：US16943957

申请日：2020-07-30

Applicant: Google LLC

Inventor： Zhen Xu ,  Andrew M. Dai ,  Jonas Beachey Kemp ,  Luke Shekerjian Metz

IPC: G06N3/08 ,  G06N3/04

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network. One of the methods includes training the neural network for one or more training steps in accordance with a current learning rate; generating a training dynamics observation characterizing the training of the trainee neural network on the one or more training steps; providing the training dynamics observation as input to a controller neural network that is configured to process the training dynamics observation to generate a controller output that defines an updated learning rate; obtaining as output from the controller neural network the controller output that defines the updated learning rate; and setting the learning rate to the updated learning rate.

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.