Katherine J Shen

US Patent:

20190355458, Nov 21, 2019

Filed:

May 17, 2018

Appl. No.:

15/982281

Inventors:

- ARMONK NY, US
Sanjoy Dey - White Plains NY, US
Katherine Shen - New York NY, US

International Classification:

G16H 20/10
G16H 70/40
G06N 7/00
A61B 5/00
G06F 19/00

Abstract:

Embodiments of the present invention disclose a method, a computer program product, and a computer system for predicting drug and food interactions. A computer identifies one or more drug similarity measures between one or more drugs and one or more food similarity measures between one or more foods. In addition, the computer identifies one or more interactions between the one or more drugs and the one or more foods, then calculates one or more drug-food feature vectors based on the one or more interactions, the one or more drug similarity measures, and the one or more food similarity measures. Furthermore, the computer calculates a first probability indicating whether a first drug of the one or more drugs will interact with a first food of the one or more foods based on a model, wherein the model is trained based on the one or more drug-food feature vectors.

US Patent:

20190348179, Nov 14, 2019

Filed:

May 11, 2018

Appl. No.:

15/976970

Inventors:

- Armonk NY, US
Sanjoy Dey - White Plains NY, US
Katherine Shen - New York NY, US

International Classification:

G16H 50/20
G16H 70/40
G06N 99/00

Abstract:

Embodiments of the present invention disclose a method, a computer program product, and a computer system for predicting drug and disease interactions. A computer identifies one or more drug similarity measures between one or more drugs and one or more disease similarity measures between one or more diseases. In addition, the computer identifies one or more interactions between the one or more drugs and the one or more diseases, then calculates one or more drug-disease feature vectors based on the one or more interactions, the one or more drug similarity measures, and the one or more disease similarity measures. Furthermore, the computer calculates a first probability indicating whether a first drug of the one or more drugs will interact with a first disease of the one or more diseases based on a model, wherein the model is trained based on the one or more drug-disease feature vectors.

US Patent:

20190228864, Jul 25, 2019

Filed:

Jan 24, 2018

Appl. No.:

15/878523

Inventors:

- Armonk NY, US
Katherine Shen - New York NY, US
Ping Zhang - White Plains NY, US

International Classification:

G16H 50/30
G16H 10/60
G16H 20/10
G16H 50/50

Abstract:

Mechanisms are provided that implement a plurality of heterogeneous causality models and a metaclassifier for predicting a likelihood of causality between a drug and an adverse event (AE). The plurality of heterogenous causality models process drug information for the drug to generate a plurality of risk predictions for a drug and AE pair. The risk predictions include at least one of a risk score or a risk label indicating a probability of the AE occurring with use of the drug. The plurality of heterogenous causality models provide the risk predictions, associated with the drug and AE pair, to a metaclassifier which generates a single causality score value indicative of a probability of causality between the drug and the AE, of the drug and AE pair, based on an aggregation of the risk predictions from the plurality of heterogenous causality models. The metaclassifier outputs the single causality score value in association with information identifying the drug and AE pair.

US Patent:

20190228865, Jul 25, 2019

Filed:

Nov 2, 2018

Appl. No.:

16/178734

Inventors:

- Armonk NY, US
Katherine Shen - New York NY, US
Ping Zhang - White Plains NY, US

International Classification:

G16H 50/30
G16H 10/60
G16H 50/50
G16H 20/10

Abstract:

Mechanisms are provided that implement a plurality of heterogeneous causality models and a metaclassifier for predicting a likelihood of causality between a drug and an adverse event (AE). The plurality of heterogenous causality models process drug information for the drug to generate a plurality of risk predictions for a drug and AE pair. The risk predictions include at least one of a risk score or a risk label indicating a probability of the AE occurring with use of the drug. The plurality of heterogenous causality models provide the risk predictions, associated with the drug and AE pair, to a metaclassifier which generates a single causality score value indicative of a probability of causality between the drug and the AE, of the drug and AE pair, based on an aggregation of the risk predictions from the plurality of heterogenous causality models. The metaclassifier outputs the single causality score value in association with information identifying the drug and AE pair.