公开(公告)号：US20240185043A1

公开(公告)日：2024-06-06

申请号：US18389010

申请日：2023-11-13

Applicant: Google LLC

Inventor： Jinsung Yoon ,  Michel Jonathan Mizrahi ,  Nahid Farhady Ghalaty ,  Thomas Dunn Henry Jarvinen ,  Ashwin Sura Ravi ,  Peter Robert Brune ,  Fanyu Kong ,  David Roger Anderson ,  George Lee ,  Farhana Bandukwala ,  Eliezer Yosef Kanal ,  Sercan Omer Arik ,  Tomas Pfister

IPC: G06N3/0475 ,  G06N3/0455

CPC classification number: G06N3/0475 ,  G06N3/0455

Abstract: The present disclosure provides a generative modeling framework for generating highly realistic and privacy preserving synthetic records for heterogenous time-series data, such as electronic health record data, financial data, etc. The generative modeling framework is based on a two-stage model that includes sequential encoder-decoder networks and generative adversarial networks (GANs).

公开(公告)号：US20240021310A1

公开(公告)日：2024-01-18

申请号：US18349945

申请日：2023-07-10

Applicant: Google LLC

Inventor： Farhana Bandukwala ,  Peter Brune ,  Fanyu Kong ,  David Roger Anderson

IPC: G16H50/20

CPC classification number: G16H50/20

Abstract: A method includes obtaining a dataset that includes health data in a Fast Healthcare Interoperability Resources (FHIR) standard. The health data includes a plurality of healthcare events. The method includes generating, using the dataset, an events table that includes the plurality of healthcare events and is indexed by time and a unique identifier per patient encounter. The method also includes generating, using the dataset, a traits table that includes static data and is indexed by the unique identifier per patient encounter. The method includes training a machine learning model using the events table and the traits table and predicting, using the trained machine learning model and one or more additional healthcare events associated with a patient, a health outcome for the patient.