公开(公告)号：US20180211012A1

公开(公告)日：2018-07-26

申请号：US15415758

申请日：2017-01-25

Applicant: UCB BIOPHARMA SPRL

Inventor： Kunal MALHOTRA ,  Sungtae AN ,  Jimeng SUN ,  Myung CHOI ,  Cynthia DILLEY ,  Chris CLARK ,  Joseph ROBERTSON ,  Edward Han-Burgess

IPC: G06F19/00 ,  A61B5/00

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.

公开(公告)号：US20180211010A1

公开(公告)日：2018-07-26

申请号：US15412806

申请日：2017-01-23

Applicant: UCB BIOPHARMA SPRL

Inventor： Kunal MALHOTRA ,  Sungtae AN ,  Jimeng SUN ,  Myung CHOI ,  Cynthia DILLEY ,  Chris CLARK ,  Joseph ROBERTSON ,  Edward HAN-BURGESS

IPC: G06F19/00 ,  G06N3/04 ,  G06N3/08

CPC classification number: G16H50/20 ,  G06N3/0445 ,  G06N3/08 ,  G16H10/60 ,  G16H50/30 ,  G16H50/70 ,  G16H70/40

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.