Kevin K Leung

US Patent:

20200402665, Dec 24, 2020

Filed:

Jun 19, 2020

Appl. No.:

16/906796

Inventors:

- Milwaukee WI, US
Gopal Avinash - San Ramon CA, US
Yrjö Häme - Helsinki, FI
Ali Faisal - Helsinki, FI
Kevin Leung - San Ramon CA, US
Jeff Hersh - Waukesha WI, US

International Classification:

G16H 50/20
G16H 10/60
G16H 50/30
G16H 40/20
G06Q 10/04
G16H 50/70
G16H 70/20
A61B 5/00
G06T 11/20

Abstract:

Techniques are described for predicting readmissions of patients to an inpatient healthcare facility. In an embodiment, a method comprises applying, by a system comprising a processor, applying, by a system operatively coupled to a processor, a readmission risk forecasting model to medical history data for a patient, wherein the readmission risk forecasting model comprises an attention-based graph neural network (A-GNN). The method further comprises, based on the applying, generating, by the system, a readmission risk score for the patient that reflects a probability of readmission of the patient following discharge from an inpatient healthcare facility. The method further comprises facilitating providing, by the system, the readmission risk score to at least one of the patient or a clinician involved in care of the patient.

US Patent:

20200349434, Nov 5, 2020

Filed:

Jul 21, 2020

Appl. No.:

16/934650

Inventors:

- Milwaukee WI, US
Gopal B. Avinash - San Ramon CA, US
Zili Ma - San Ramon CA, US
Kevin H. Leung - San Ramon CA, US
Wen Jin - Fremont CA, US

International Classification:

G06N 3/08
G06F 16/28
G06N 3/04

Abstract:

Techniques are provided for determining confident data samples for machine learning (ML) models on unseen data. In one embodiment, a method is provided that comprises extracting, by a system comprising a processor, a feature vector for a data sample based on projection of the data sample onto a standard feature space. The method further comprises processing, by the system, the feature vector using an outlier detection model to determine whether the data sample is within a scope of a training dataset used to train a machine learning model, wherein the outlier detection model was trained using features extracted from the training dataset based on projection of data samples included in the training dataset onto the standard feature space.