Poster Session
Issue Date: August 2020
Published Online: August 01, 2020
Updated: September 09, 2020
Identification of Significant Subgroups of Readmitted Stroke Patients: Big Data Visual Analytic Approach
Author Affiliations & Notes
  • University of Texas Medical Branch, Galveston, TX, USA
  • University of Texas Medical Branch, Galveston, TX, USA
  • University of Texas Medical Branch, Galveston, TX, USA
  • University of Texas Medical Branch, Galveston, TX, USA
  • University of Texas Medical Branch, Galveston, TX, USA
  • University of Texas Medical Branch, Galveston, TX, USA
  • University of Texas Medical Branch, Galveston, TX, USA
  • University of Texas Medical Branch, Galveston, TX, USA
  • University of Texas Medical Branch, Galveston, TX, USA
  • University of Texas Medical Branch, Galveston, TX, USA
  • UT Health San Antonio, San Antonio, TX, USA
Article Information
Neurologic Conditions / Stroke
Poster Session   |   August 01, 2020
Identification of Significant Subgroups of Readmitted Stroke Patients: Big Data Visual Analytic Approach
American Journal of Occupational Therapy, August 2020, Vol. 74, 7411510267. https://doi.org/10.5014/ajot.2020.74S1-PO1719
American Journal of Occupational Therapy, August 2020, Vol. 74, 7411510267. https://doi.org/10.5014/ajot.2020.74S1-PO1719
Abstract

Date Presented 03/26/20

Using big data visual analytics and a total of 86,887 readmitted Medicare beneficiaries living with stroke, a number of biclusters consisting of patient subgroups and their co-occurring multiple chronic comorbidities were determined. This technique informed a multimorbidity self-management program to support patients managing their health after stroke.

Primary Author and Speaker: Ickpyo Hong

Additional Authors and Speakers: Kimberly Hreha, Monique Pappadis, Hashem Shaltoni, Yu-Li Lin, Emmanuel Santillana Fayett, Julianna Dean, Chih-Ying Li, Annalisa Na, Suresh Bhavnani, Timothy Reistetter