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:
Apr. 18, 2023

Filed:

Jun. 19, 2018
Applicant:

Siemens Healthcare Gmbh, Erlangen, DE;

Inventors:

Philipp Hoelzer, Baltimore, MD (US);

Sasa Grbic, Plainsboro, NJ (US);

Daguang Xu, Princeton, NJ (US);

Assignee:

Siemens Healthcare GmbH, Erlangen, DE;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 18/2411 (2023.01); G06N 3/08 (2023.01); G16H 50/20 (2018.01); G16H 30/40 (2018.01); G06N 3/02 (2006.01); G16H 50/00 (2018.01); G16H 50/70 (2018.01); G06V 10/70 (2022.01); G06F 18/22 (2023.01); G06F 18/2415 (2023.01); G06V 10/772 (2022.01); G06V 10/778 (2022.01); G06V 10/94 (2022.01);
U.S. Cl.
CPC ...
G06N 3/08 (2013.01); G06F 18/22 (2023.01); G06F 18/2411 (2023.01); G06F 18/24155 (2023.01); G06N 3/02 (2013.01); G06V 10/70 (2022.01); G06V 10/772 (2022.01); G06V 10/7796 (2022.01); G06V 10/945 (2022.01); G16H 30/40 (2018.01); G16H 50/00 (2018.01); G16H 50/20 (2018.01); G16H 50/70 (2018.01); G06V 2201/03 (2022.01);
Abstract

The user is to be informed of the reliability of the machine-learned model based on the current input relative to the training data used to train the model or the model itself. In a medical situation, the data for a current patient is compared to the training data used to train a prediction model and/or to a decision function of the prediction model. The comparison indicates the training content relative to the current patient, so provides a user with information on the reliability of the prediction for the current situation. The indication deals with the variation of the data of the current patient from the training data or relative to the prediction model, allowing the user to see how well trained the predication model is relative to the current patient. This indication is in addition to any global confidence output through application of the prediction model to the data of the current patient.


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