Heart failure hospitalization prediction in remote patient management systems

M. Pechenizkiy, E. Vasilyeva, I. Zliobaite, A. Tesanovic, G. Manev

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

4 Citations (Scopus)
105 Downloads (Pure)

Abstract

Healthcare systems are shifting from patient care in hospitals to monitored care at home. It is expected to improve the quality of care without exploding the costs. Remote patient management (RPM) systems offer a great potential in monitoring patients with chronic diseases, like heart failure or diabetes. Patient modeling in RPM systems opens opportunities in two broad directions: personalizing information services, and alerting medical personnel about the changing conditions of a patient. In this study we focus on heart failure hospitalization (HFH) prediction, which is a particular problem of patient modeling for alerting. We formulate a short term HFH prediction problem and show how to address it with a data mining approach. We emphasize challenges related to the heterogeneity, different types and periodicity of the data available in RPM systems. We present an experimental study on HFH prediction using, which results lay a foundation for further studies and implementation of alerting and personalization services in RPM systems.
Original languageEnglish
Title of host publicationProceedings of the 23rd IEEE International Symposium on Computer-Based Medical Systems (CBMS'10, Perth, Australia, October 12-15, 2010)
PublisherInstitute of Electrical and Electronics Engineers
Pages44-49
ISBN (Print)978-1-4244-9167-4
DOIs
Publication statusPublished - 2010

Fingerprint Dive into the research topics of 'Heart failure hospitalization prediction in remote patient management systems'. Together they form a unique fingerprint.

Cite this