Optimizing medication reminders using a decision-theoretic framework

Misha Pavel, Holly Jimison, Tamara Hayes, Nicole Larimer, Stuart Hagler, Yves Vimegnon, Todd Leen, Umut Ozertem

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

We discuss a new approach to patients' adherence to enhance to their medication-taking regimen by developing a context-aware alerting system that would optimize the expected utility of alerts. Each patient's instantaneous context is assessed using a real-time sensor network deploying a variety of sensors. The alerts are generated to optimize the expected value to the patient. This paper is focused on the initial assessment of the utility of alerts, including the tradeoff between effectiveness and annoyance.

Original languageEnglish (US)
Title of host publicationMedinfo 2010 - Proceedings of the 13th World Congress on Medical Informatics
PublisherIOS Press
Pages791-795
Number of pages5
EditionPART 1
ISBN (Print)9781607505877
DOIs
StatePublished - Jan 1 2010
Event13th World Congress on Medical and Health Informatics, Medinfo 2010 - Cape Town, South Africa
Duration: Sep 12 2010Sep 15 2010

Publication series

NameStudies in Health Technology and Informatics
NumberPART 1
Volume160
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Other

Other13th World Congress on Medical and Health Informatics, Medinfo 2010
CountrySouth Africa
CityCape Town
Period9/12/109/15/10

Keywords

  • Artificial intelligence
  • Home monitoring
  • Machine learning
  • Medication adherence
  • Reminders

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

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  • Cite this

    Pavel, M., Jimison, H., Hayes, T., Larimer, N., Hagler, S., Vimegnon, Y., Leen, T., & Ozertem, U. (2010). Optimizing medication reminders using a decision-theoretic framework. In Medinfo 2010 - Proceedings of the 13th World Congress on Medical Informatics (PART 1 ed., pp. 791-795). (Studies in Health Technology and Informatics; Vol. 160, No. PART 1). IOS Press. https://doi.org/10.3233/978-1-60750-588-4-791