公开(公告)号：US20190034589A1

公开(公告)日：2019-01-31

申请号：US15690703

申请日：2017-08-30

Applicant: Google Inc.

Inventor： Kai Chen ,  Patrik Sundberg ,  Alexander Mossin ,  Nissan Hajaj ,  Kurt Litsch ,  James Wexler ,  Yi Zhang ,  Kun Zhang ,  Jacob Marcus ,  Eyal Oren ,  Hector Yee ,  Jeffrey Dean ,  Michaela Hardt ,  Benjamin Irvine ,  James Wilson ,  Andrew Dai ,  Peter Liu ,  Xiaomi Sun ,  Quoc Le ,  Xiaobing Liu ,  Alvin Rajkomar ,  Gregory Corrado ,  Gerardo Flores ,  Yingwei Cui ,  Gavin Duggan

IPC: G06F19/00 ,  G06N3/08 ,  G06N3/04

Abstract: A system for predicting and summarizing medical events from electronic health records includes a computer memory storing aggregated electronic health records from a multitude of patients of diverse age, health conditions, and demographics including medications, laboratory values, diagnoses, vital signs, and medical notes. The aggregated electronic health records are converted into a single standardized data structure format and ordered arrangement per patient, e.g., into a chronological order. A computer (or computer system) executes one or more deep learning models trained on the aggregated health records to predict one or more future clinical events and summarize pertinent past medical events related to the predicted events on an input electronic health record of a patient having the standardized data structure format and ordered into a chronological order. An electronic device configured with a healthcare provider-facing interface displays the predicted one or more future clinical events and the pertinent past medical events of the patient.

公开(公告)号：US20190034591A1

公开(公告)日：2019-01-31

申请号：US15690721

申请日：2017-08-30

Applicant: Google Inc.

Inventor： Alexander Mossin ,  Alvin Rajkomar ,  Eyal Oren ,  James Wilson ,  James Wexler ,  Patrik Sundberg ,  Andrew Dai ,  Yingwei Cui ,  Gregory Corrado ,  Hector Yee ,  Jacob Marcus ,  Jeffrey Dean ,  Benjamin Irvine ,  Kai Chen ,  Kun Zhang ,  Michaela Hardt ,  Xiaomi Sun ,  Nissan Hajaj ,  Peter Liu ,  Quoc Le ,  Xiaobing Liu ,  Yi Zhang

IPC: G06F19/00 ,  G06N3/08 ,  G06N3/04

Abstract: A system for predicting and summarizing medical events from electronic health records includes a computer memory storing aggregated electronic health records from a multitude of patients of diverse age, health conditions, and demographics including medications, laboratory values, diagnoses, vital signs, and medical notes. The aggregated electronic health records are converted into a single standardized data structure format and ordered arrangement per patient, e.g., into a chronological order. A computer (or computer system) executes one or more deep learning models trained on the aggregated health records to predict one or more future clinical events and summarize pertinent past medical events related to the predicted events on an input electronic health record of a patient having the standardized data structure format and ordered into a chronological order. An electronic device configured with a healthcare provider-facing interface displays the predicted one or more future clinical events and the pertinent past medical events of the patient.

公开(公告)号：US20170300814A1

公开(公告)日：2017-10-19

申请号：US15394668

申请日：2016-12-29

Applicant: Google Inc.

Inventor： Tal Shaked ,  Rohan Anil ,  Hrishikesh Balkrishna Aradhye ,  Mustafa Ispir ,  Glen Anderson ,  Wei Chai ,  Mehmet Levent Koc ,  Jeremiah Harmsen ,  Xiaobing Liu ,  Gregory Sean Corrado ,  Tushar Deepak Chandra ,  Heng-Tze Cheng

IPC: G06N3/08 ,  G06N3/04

CPC classification number: G06N3/08 ,  G06N3/0454 ,  G06N3/0472 ,  G06N3/084

Abstract: A system includes one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the computers to implement a combined machine learning model for processing an input including multiple features to generate a predicted output for the machine learning input. The combined model includes: a deep machine learning model configured to process the features to generate a deep model output; a wide machine learning model configured to process the features to generate a wide model output; and a combining layer configured to process the deep model output generated by the deep machine learning model and the wide model output generated by the wide machine learning model to generate the predicted output, in which the deep model and the wide model have been trained jointly on training data to generate the deep model output and the wide model output.