Chris Vinson Clark

US Patent:

20180211010, Jul 26, 2018

Filed:

Jan 23, 2017

Appl. No.:

15/412806

Inventors:

- Brussels, BE
Sungtae AN - Atlanta GA, US
Jimeng SUN - Atlanta GA, US
Myung CHOI - Atlanta GA, US
Cynthia DILLEY - Smyrna GA, US
Chris CLARK - Smyrna GA, US
Joseph ROBERTSON - Smyrna GA, US

International Classification:

G06F 19/00
G06N 3/04
G06N 3/08

Abstract:

A method of building a machine learning pipeline for predicting refractoriness of epilepsy patients is provided. The method includes providing electronic health records data; constructing a patient cohort from the electronic health records data by selecting patients based on failure of at least one anti-epilepsy drug; constructing a set features found in or derived from the electronic health records data; electronically processing the patient cohort to identify a subset of the features that are predictive for refractoriness for inclusion in a predictive model configured for classifying patients as refractory or non-refractory; and training the predictive computerized model to classify the patients having at least one anti-epilepsy drug failure based on likelihood of becoming refractory.

US Patent:

20180211012, Jul 26, 2018

Filed:

Jan 25, 2017

Appl. No.:

15/415758

Inventors:

- Brussels, BE
Sungtae AN - Atlanta GA, US
Jimeng SUN - Atlanta GA, US
Myung CHOI - Atlanta GA, US
Cynthia DILLEY - Smyrna GA, US
Chris CLARK - Smyrna GA, US
Joseph ROBERTSON - Smyrna GA, US

International Classification:

G06F 19/00
A61B 5/00

Abstract:

A method of building a machine learning pipeline for predicting the efficacy of anti-epilepsy drug treatment regimens is provided. The method includes providing electronic health records data; constructing a patient cohort from the electronic health records data by selecting patients based on a defined target variable indicating anti-epilepsy drug treatment regimen efficacy; constructing a set features found in or derived from the electronic health records data; electronically processing the patient cohort to identify a subset of the features that are predictive for anti-epilepsy drug treatment regimen efficacy for inclusion in predictive models configured for generating predictions representative of efficacy for a plurality of anti-epilepsy drug treatment regimens; and training the predictive computerized model to generate predictions representative of efficacy for a plurality of anti-epilepsy drug treatment regimens for the patients based on the defined target variable indicating anti-epilepsy drug treatment regimen efficacy.