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：US20190034590A1

公开(公告)日：2019-01-31

申请号：US15690714

申请日：2017-08-30

Applicant: Google Inc.

Inventor： Eyal Oren ,  Yingwei Cui ,  Gerardo Flores ,  Gavin Duggan ,  Kun Zhang ,  Kurt Litsch ,  Patrik Sundberg ,  Yi Zhang

IPC: G06F19/00 ,  G06N3/08

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.