公开(公告)号：US11302446B2

公开(公告)日：2022-04-12

申请号：US16683139

申请日：2019-11-13

Applicant: Google LLC

Inventor： Nenad Tomasev ,  Xavier Glorot ,  Jack William Rae ,  Michal Zielinski ,  Anne Mottram ,  Harry Askham ,  Andre Saraiva Nobre Dos Santos ,  Clemens Ludwig Meyer ,  Suman Ravuri ,  Ivan Protsyuk ,  Trevor Back ,  Joseph R. Ledsam ,  Shakir Mohamed

IPC: G16H50/30 ,  G16H70/60 ,  G16H50/50 ,  G16H10/60 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting future adverse health events using neural networks. One of the methods includes receiving electronic health record data for a patient; generating, from the electronic health record data, an input sequence comprising a respective feature representation at each of a plurality of time window time steps, comprising, for each time window time step: determining, for each of the possible numerical features, whether the numerical feature occurred during the time window; and generating, for each of the possible numerical features, one or more presence features that identify whether the numerical feature occurred during the time window; and processing the input sequence using a neural network to generate a neural network output that characterizes a predicted likelihood that an adverse health event will occur to the patient.

公开(公告)号：US20200152333A1

公开(公告)日：2020-05-14

申请号：US16683139

申请日：2019-11-13

Applicant: Google LLC

Inventor： Nenad Tomasev ,  Xavier Glorot ,  Jack William Rae ,  Michal Zielinski ,  Anne Mottram ,  Harry Askham ,  Andre Saraiva Nobre Dos Santos ,  Clemens Ludwig Meyer ,  Suman Ravuri ,  Ivan Protsyuk ,  Trevor Back ,  Joseph R. Ledsam ,  Shakir Mohamed

IPC: G16H50/30 ,  G16H10/60 ,  G16H50/50 ,  G16H70/60 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting future adverse health events using neural networks. One of the methods includes receiving electronic health record data for a patient; generating, from the electronic health record data, an input sequence comprising a respective feature representation at each of a plurality of time window time steps, comprising, for each time window time step: determining, for each of the possible numerical features, whether the numerical feature occurred during the time window; and generating, for each of the possible numerical features, one or more presence features that identify whether the numerical feature occurred during the time window; and processing the input sequence using a neural network to generate a neural network output that characterizes a predicted likelihood that an adverse health event will occur to the patient.