Tadpole: A Framework for Characterizing Temporal Model Data Drifts at the Point-of-Care

Sarah Pungitore, Vignesh Subbian

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

Abstract

Temporal models that use timestamped data from electronic health record (EHR) systems could enhance patient outcomes and reduce costs in point-of-care (POC) settings, especially for complex prediction tasks. However, a major obstacle to their widespread use is data drifts, which are changes in how input data relates to target outcomes. Currently, no studies have systematically categorized these data drifts for temporal EHR models or identified the key questions needed to address their impact. This study aims to create a conceptual framework, TADPole, for characterizing data drifts specifically for temporal models in POC environments.

Original languageEnglish (US)
Title of host publication2024 IEEE Healthcare Innovations and Point of Care Technologies, HI-POCT 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages25-28
Number of pages4
ISBN (Electronic)9798331508036
DOIs
StatePublished - 2024
Event2024 IEEE Healthcare Innovations and Point of Care Technologies, HI-POCT 2024 - Tucson, United States
Duration: Sep 19 2024Sep 20 2024

Publication series

Name2024 IEEE Healthcare Innovations and Point of Care Technologies, HI-POCT 2024

Conference

Conference2024 IEEE Healthcare Innovations and Point of Care Technologies, HI-POCT 2024
Country/TerritoryUnited States
CityTucson
Period9/19/249/20/24

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Instrumentation
  • Health(social science)

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