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:
Sep. 24, 2024

Filed:

Jul. 16, 2021
Applicant:

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

Inventors:

Ying Li, Fort Lee, NJ (US);

Mohamed Ghalwash, Yorktown Heights, NY (US);

Kenney Ng, Arlington, MA (US);

Vibha Anand, Cambridge, MA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G16H 50/70 (2018.01); G06F 40/30 (2020.01); G16H 10/40 (2018.01); G16H 10/60 (2018.01); G16H 50/20 (2018.01); G16H 50/30 (2018.01); G16H 50/50 (2018.01); G16H 70/60 (2018.01);
U.S. Cl.
CPC ...
G16H 50/70 (2018.01); G06F 40/30 (2020.01); G16H 10/40 (2018.01); G16H 10/60 (2018.01); G16H 50/20 (2018.01); G16H 50/30 (2018.01); G16H 70/60 (2018.01); G16H 50/50 (2018.01);
Abstract

Techniques for generating an ontology based on biomarker information associated with persons to facilitate improving clinical predictions relating to medical conditions are presented. An ontology generator component (OGC) can extract clinical features associated with patients and their associated times from medical records or databases to develop clinical profiles associated with the patients and relating to a medical condition. OGC can develop an ontology relating to the medical condition, including progression and severity of biomarkers associated with the medical condition, based on the clinical profiles and domain knowledge information relating to the medical condition. OGC can determine global features relating to progression and severity associated with the medical condition based on the ontology. At a forecasting point, the global features can be extracted from the ontology and applied to a prediction model to enhance prediction of onset of, or progression of, the medical condition for a patient.


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