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:
Nov. 16, 2021

Filed:

Jan. 09, 2018
Applicant:

Siemens Healthcare Gmbh, Erlangen, DE;

Inventors:

Halid Ziya Yerebakan, Indianapolis, IN (US);

Yoshihisa Shinagawa, Downingtown, PA (US);

Parmeet Singh Bhatia, Frazer, PA (US);

Yiqiang Zhan, Berwyn, PA (US);

Assignee:

Siemens Healthcare GmbH, Erlangen, DE;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 16/00 (2019.01); G06F 16/35 (2019.01); G16H 50/70 (2018.01); G06K 9/00 (2006.01); G06N 20/00 (2019.01); G06F 16/93 (2019.01); G06K 9/62 (2006.01); G06F 16/36 (2019.01); G16H 15/00 (2018.01); G16H 30/40 (2018.01); G06N 3/04 (2006.01); G06N 3/08 (2006.01); G06N 7/00 (2006.01); G16H 10/60 (2018.01); G06F 40/30 (2020.01); G06N 20/10 (2019.01); G06N 5/00 (2006.01);
U.S. Cl.
CPC ...
G06F 16/358 (2019.01); G06F 16/35 (2019.01); G06F 16/367 (2019.01); G06F 16/93 (2019.01); G06F 40/30 (2020.01); G06K 9/00442 (2013.01); G06K 9/00483 (2013.01); G06K 9/627 (2013.01); G06K 9/6215 (2013.01); G06K 9/6226 (2013.01); G06N 3/0445 (2013.01); G06N 3/0454 (2013.01); G06N 3/08 (2013.01); G06N 7/005 (2013.01); G06N 20/00 (2019.01); G06N 20/10 (2019.01); G16H 10/60 (2018.01); G16H 15/00 (2018.01); G16H 30/40 (2018.01); G16H 50/70 (2018.01); G06N 5/003 (2013.01);
Abstract

A visualization framework based on document representation learning is described herein. The framework may first convert a free text document into word vectors using learning word embeddings. Document representations may then be determined in a fixed-dimensional semantic representation space by passing the word vectors through a trained machine learning model, wherein more related documents lie closer than less related documents in the representation space. A clustering algorithm may be applied to the document representations for a given patient to generate clusters. The framework then generates a visualization based on these clusters.


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