Google Inc.

Xiaobing Liu

Gregory Sean Corrado

Jeffrey Adgate Dean

Kai Chen

Quoc V Le

Yi Zhang

Kun Zhang

James Wilson

Yingwei Cui

Andrew M Dai

Nissan Hajaj

Alvin Rishi Rajkomar

Peter Junteng Liu

Benjamin Irvine

Eyal Oren

James Wexler

Patrik Sundberg

Hector Yee

Michaela Hardt

Xiaomi Sun

Jacob Marcus

Alexander Mossin

Hrishikesh Balkrishna Aradhye

Tushar Deepak Chandra

Tal Shaked

Wei Chai

Jeremiah Harmsen

Glen Anderson

Mustafa Ispir

Mehmet Levent Koc

Rohan Anil

Heng-Tze Cheng

Gerardo Flores

Kurt Litsch

Gavin Duggan

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.

System and method for predicting and summarizing medical events from electronic health records

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.

Wide and deep machine learning models

Growing community of inventors

Mountain View, CA, United States of America

Xiaobing Liu