Google Inc.

Michaela Hardt

Kai Chen

James Wilson

Alvin Rishi Rajkomar

Eyal Oren

Hector Yee

Jeffrey Adgate Dean

Quoc V Le

Yi Zhang

Kun Zhang

Gregory Sean Corrado

Yingwei Cui

Andrew M Dai

Nissan Hajaj

Peter Junteng Liu

Benjamin Irvine

James Wexler

Patrik Sundberg

Xiaomi Sun

Xiaobing Liu

Jacob Marcus

Alexander Mossin

Gerardo Flores

Kurt Litsch

Gavin Duggan

Alvin Rajkomar

Example embodiments relate to a method for training a predictive model from data. The method includes defining a multitude of predicates as binary functions operating on time sequences of the features or logical operations on the time sequences of the features. The method also includes iteratively training a boosting model by generating a number of new random predicates, scoring all the new random predicates by weighted information gain with respect to a class label associated with a prediction of the boosting model, selecting a number of the new random predicates with the highest weighted information gain and adding them to the boosting model, computing weights for all the predicates in the boosting model, removing one or more of the selected new predicates with the highest information gain from the boosting model in response to input from an operator. The method may include repeating the prior steps a plurality of times.

Human-in-the-loop interactive model training

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

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Mountain View, CA, United States of America

Michaela Hardt