The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jul. 26, 2022

Filed:

Aug. 30, 2017
Applicant:

Google Llc, Mountain View, CA (US);

Inventors:

Kai Chen, San Bruno, CA (US);

Patrik Sundberg, San Francisco, CA (US);

Alexander Mossin, San Jose, CA (US);

Nissan Hajaj, Redwood City, CA (US);

Kurt Litsch, Mountain View, CA (US);

James Wexler, Mountain View, CA (US);

Yi Zhang, Sunnyvale, CA (US);

Kun Zhang, Mountain View, CA (US);

Jacob Marcus, Mountain View, CA;

Eyal Oren, Los Gatos, CA (US);

Hector Yee, Mountain View, CA (US);

Jeffrey Dean, Palo Alto, CA (US);

Michaela Hardt, Mountain View, CA (US);

Benjamin Irvine, Mountain View, CA (US);

James Wilson, Littleton, MA (US);

Andrew Dai, San Francisco, CA (US);

Peter Liu, Santa Clara, CA (US);

Xiaomi Sun, San Mateo, CA (US);

Quoc Le, Mountain View, CA (US);

Xiaobing Liu, Mountain View, CA (US);

Alvin Rajkomar, Mountain View, CA (US);

Gregory Corrado, San Francisco, CA (US);

Gerardo Flores, Mountain View, CA (US);

Yingwei Cui, Palo Alto, CA (US);

Gavin Duggan, Mountain View, CA (US);

Assignee:

Google LLC, Mountain View, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G16H 50/30 (2018.01); G16H 50/20 (2018.01); G16H 10/60 (2018.01); G06N 3/08 (2006.01); G16H 15/00 (2018.01); G06N 3/04 (2006.01);
U.S. Cl.
CPC ...
G16H 10/60 (2018.01); G06N 3/0445 (2013.01); G06N 3/0454 (2013.01); G06N 3/08 (2013.01); G16H 15/00 (2018.01); G16H 50/30 (2018.01); G16H 50/20 (2018.01);
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.


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