Flatiron Health, Inc.

Amazon Technologies, Inc.

Chun-Che Wang

Lauren Sutton

Angel Leung

Janet Donegan

Sharang Phadke

Alexander Padmos

Dominic Green

Addison Shelley

Edward Liu

Lucy He

Sheng Bi

Naiem Khodabandehloo Yeganeh

Paul Grunchich

Royden Mark Jacob Lewis

Hunlan Lin

Chenzhi Shao

A computer-implemented system for identifying a patient for a trial may include at least one processor. The at least one processor may be programmed to receive an indication of a selected trial, the selected trial being associated with a testing status criterion; access a plurality of patient records associated with a patient of a plurality of patients; determine, using a machine learning model and based on unstructured information from one at least one of the patient records, a likelihood of an occurrence of genomic testing for the patient; determine a genomic testing status of the patient based on the determined likelihood of the occurrence of genomic testing; determine that the genomic testing status satisfies the testing status criterion; and include the patient in a subset of the plurality of patients based on the genomic testing status satisfying the testing status criterion.

Systems and methods for determining a genomic testing status

A computer-implemented system for determining a genomic testing status of a patient may include at least one processor programmed receive, from a source, unstructured information from a plurality of patient records associated with a patient; determine, using a first machine learning model, a primary patient record from among the plurality of patient records, wherein at least a portion of information represented in the primary patient record correlates to genomic testing; determine, using a second machine learning model and based on unstructured information from one at least one of the patient records, a likelihood of an occurrence of genomic testing for the patient; determine a genomic testing status of the patient based on the determined likelihood of the occurrence of genomic testing; and display a user interface comprising an indicator of the genomic testing status of the patient and a link to the primary patient record.

Systems and methods are provided for retraining a machine learning model to learn a retrained machine learning model. After a computing system receives an execution log of a prediction service and an observed user action, it may create retraining data. From the retraining data, the computing system may learn a retrained machine learning model. The retrained machine learning model can be used to compute future predictions of user actions if it has an improved performance over the original machine learning model.

Automatic re-training of offline machine learning models

Growing community of inventors

Seattle, WA, United States of America

Chun-Che Wang