The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Aug. 24, 2021

Filed:

Dec. 01, 2018
Applicant:

Petuum Inc., Pittsburgh, PA (US);

Inventors:

Pengtao Xie, Pittsburgh, PA (US);

Eric Xing, Pittsburgh, PA (US);

Assignee:

PETUUM INC., Pittsburgh, PA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G16H 20/10 (2018.01); G16H 10/60 (2018.01); G06N 20/00 (2019.01); G16B 40/00 (2019.01); G16H 50/20 (2018.01); G16H 70/60 (2018.01); G06N 3/08 (2006.01); G16H 15/00 (2018.01); G16H 30/40 (2018.01); G06K 9/46 (2006.01); G06K 9/62 (2006.01); G06T 7/00 (2017.01); G06F 16/36 (2019.01); H04L 29/08 (2006.01); G06F 40/284 (2020.01); G16H 50/70 (2018.01); G16B 50/00 (2019.01); G06K 9/72 (2006.01);
U.S. Cl.
CPC ...
G16H 20/10 (2018.01); G06F 16/36 (2019.01); G06F 40/284 (2020.01); G06K 9/46 (2013.01); G06K 9/6228 (2013.01); G06N 3/08 (2013.01); G06N 20/00 (2019.01); G06T 7/0012 (2013.01); G16B 40/00 (2019.02); G16H 10/60 (2018.01); G16H 15/00 (2018.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G16H 70/60 (2018.01); H04L 67/104 (2013.01); G06K 9/628 (2013.01); G06K 9/726 (2013.01); G06K 2209/05 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G16B 50/00 (2019.02); G16H 50/70 (2018.01);
Abstract

A system for predicting medications to prescribe to a patient includes a text encoding module and a medication prediction module. The text encoding module is configured to obtain a clinical-information vector from clinical information of the patient. The medication prediction module configured to apply a machine-learned medication-prediction algorithm to the clinical-information vector to select a subset of medications to prescribe to the patient. The machine-learned medication-prediction algorithm is designed with a diversity-promoting regularization model, and is configured to simultaneously consider correlations among different medications and dependencies between patient information and medications when selecting a subset of medications to prescribe to the patient.


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