Shaun Jason Grannis

US Patent:

20200312457, Oct 1, 2020

Filed:

Mar 27, 2020

Appl. No.:

16/831971

Inventors:

- Indianapolis IN, US
Shaun J. Grannis - Indianapolis IN, US

Assignee:

The Trustees of Indiana University - Indianapolis IN

International Classification:

G16H 50/20
G06N 20/00
G06N 3/04
G16H 50/50

Abstract:

A method is provided for creating synthetic unstructured free-text medical data that closely mimics real data for enabling machine learning research, but with limited re-identification risk. The method includes leveraging two neural networks that compete with each other (adversarial networks) to create a synthetic message dataset that closely mimics the real medical data. Machine learning models trained using the synthetic data yield performance metrics that are statistically similar to models trained using the real dataset, ensuring that our approach can be used to replicate machine learning studies. Further, the synthetic message datasets can be easily shared with researchers with limited re-identification risk.

US Patent:

20200035360, Jan 30, 2020

Filed:

Jul 26, 2019

Appl. No.:

16/523176

Inventors:

- Indianapolis IN, US
Nir MENACHEMI - Zionsville IN, US
Shaun Jason GRANNIS - Indianapolis IN, US
Paul Kenneth HALVERSON - Indianapolis IN, US

International Classification:

G16H 50/20
G06N 20/00
G16H 50/30

Abstract:

Technologies for predicting need for one or more treatment services include obtaining data representative of a patient cohort. From the data, one or more features associated with each of the patients of the cohort are extracted. The one or more features includes features indicative of social determinants of each of the plurality of patients and of a general population of individuals. A predictive model for determining a need for referring a patient to the one or more treatment services is trained.