公开(公告)号：US20230138371A1

公开(公告)日：2023-05-04

申请号：US17912233

申请日：2020-09-01

Applicant: Google LLC

Inventor： Farhana Bandukwala ,  Georg Matthias Goerg ,  Samuel Paul Ruth ,  Jonathan Jesse Halcrow

IPC: G06F11/36

Abstract: A method of identifying a contributing cause of an anomaly including receiving a set of timeseries data representing metric values over time, wherein the timeseries data has at least two dimensions, for each of two or more of the timestamps in the set of timeseries data, generating a first and second graph representing (i) the metric values at that timestamp, (ii) the at least two dimensions at that timestamp, and (iii) associations between the metric values at that timestamp, analyzing the first and second graphs associated with each timestamp to identify a particular timestamp including an anomaly, and analyzing the first and second graphs associated with the identified particular timestamp to identify a node that contributed in causing the anomaly.

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.