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.

Patent No.:

US 11081215 B1

PDF
Full Text
Expired
Date of Patent:
Aug. 03, 2021

Filed:

Jun. 01, 2017
Applicant:

International Business Machines Corporation, Armonk, NY (US);

Inventors:

Murthy V. Devarakonda, Peekskill, NY (US);

Safa Messaoud, Sfax, TN;

Ching-Huei Tsou, Briarcliff Manor, NY (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06N 99/00 (2019.01); G16H 10/60 (2018.01); G16H 50/50 (2018.01); G06N 20/00 (2019.01); G06N 5/00 (2006.01); G06N 3/04 (2006.01); G16H 50/20 (2018.01); G16H 40/67 (2018.01); G16H 50/30 (2018.01);
U.S. Cl.
CPC ...
G16H 10/60 (2018.01); G06N 3/0454 (2013.01); G06N 5/003 (2013.01); G06N 20/00 (2019.01); G16H 40/67 (2018.01); G16H 50/20 (2018.01); G16H 50/30 (2018.01); G16H 50/50 (2018.01);
Abstract

Embodiments of the invention include methods, systems, and computer program products for generating a medical problem list. A non-limiting example of the method includes receiving, by a processor, a plurality of disease categories. A disease category set that includes a plurality of top level disease categories is defined using the processor, wherein the disease category set is based at least in part upon the plurality of disease categories. The processor is used to extract a plurality of candidate training problems from an electronic patient record training set. The processor is used to assign each of the candidate training problems to the plurality of top level disease categories. The processor is used to generate a disease category model for each of the top level disease categories from the electronic patient record training set using a machine learning technique.


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