Dexheimer, Judith W.

Prospective validation of a machine learning model that uses provider notes to identify candidates for resective epilepsy surgery.

Citation: 
Wissel BD, Greiner HM, Glauser TA, Holland-Bouley KD, Mangano FT, Santel D, Faist R, Zhang N, Pestian JP, Szczesniak RD, Dexheimer JW. Prospective validation of a machine learning model that uses provider notes to identify candidates for resective epilepsy surgery. Epilepsia. 2019 Nov 29. doi: 10.1111/epi.16398. [Epub ahead of print]. PMID: 31784992.
Principal Investigator: 
Dexheimer, Judith W.
Project Name: 
Optimal Methods for Notifying Clinicians About Epilepsy Surgery Patients

Investigation of bias in an epilepsy machine learning algorithm trained on physician notes.

Citation: 
Wissel BD, Greiner HM, Glauser TA, Mangano FT, Santel D, Pestian JP, Szczesniak RD, Dexheimer JW. Investigation of bias in an epilepsy machine learning algorithm trained on physician notes. Epilepsia. 2019 Sep;60(9):e93-e98. doi: 10.1111/epi.16320. Epub 2019 Aug 23. PMID: 31441044.
Principal Investigator: 
Dexheimer, Judith W.
Project Name: 
Optimal Methods for Notifying Clinicians About Epilepsy Surgery Patients

Optimal Methods for Notifying Clinicians About Epilepsy Surgery Patients - Final Report

Citation: 
Dexheimer J. Optimal Methods for Notifying Clinicians About Epilepsy Surgery Patients - Final Report. (Prepared by Cincinnati Children's Hospital Medical Center under Grant No. R21 HS024977). Rockville, MD: Agency for Healthcare Research and Quality, 2018.
Principal Investigator: 
Dexheimer, Judith W.
Project Name: 
Optimal Methods for Notifying Clinicians About Epilepsy Surgery Patients

Optimal Methods for Notifying Clinicians About Epilepsy Surgery Patients

Description: 

This research prospectively evaluated a machine learning algorithm that identifies candidates for neurologic surgery to control epilepsy.

Principal Investigator: 
Judith W. Dexheimer
Project Dates: 
September 30, 2016 to September 29, 2018