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:
Jan. 28, 2025

Filed:

Nov. 29, 2023
Applicant:

Georgetown University, Washington, DC (US);

Inventors:

Ophir Frieder, Chevy Chase, MD (US);

Hao-Ren Yao, McLean, VA (US);

Der-Chen Chang, Burtonsville, MD (US);

Assignee:

Georgetown University, Washington, DC (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 7/01 (2023.01); G06F 9/54 (2006.01); G06F 18/24 (2023.01); G16H 20/10 (2018.01); G16H 50/20 (2018.01); G16H 50/80 (2018.01); G16H 70/40 (2018.01);
U.S. Cl.
CPC ...
G16H 50/20 (2018.01); G06F 9/545 (2013.01); G06F 18/24 (2023.01); G06N 7/01 (2023.01); G16H 20/10 (2018.01); G16H 50/80 (2018.01); G16H 70/40 (2018.01);
Abstract

Embodiments of the present systems and methods may provide techniques to predict the success or failure of a drug used for disease treatment. For example, a method of determining drug efficacy may include, for a plurality of patients, generating a directed acyclic graph from health related information of each patient comprising nodes representing a medical event of the patient, at least one first edge connecting the first node to an additional node, each additional edge connecting nodes representing two consecutive medical events, the edge having a weight based on a time difference between the two consecutive medical events, capturing a plurality of features from each directed acyclic graph, generating a binary graph classification model on captured features of each directed acyclic graph, determining a probability that a drug or treatment will be effective using the binary graph classification model, and determining a drug to be prescribed to a patient based on the determined probability.


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