A Novel Joint Longitudinal Model for Predicting Post-ICU Anemia

Gabriel Demuth, Curtis Storlie

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

Abstract

Patients are known to be at an elevated risk of severe hemoglobin deficiency (anemia) following an Intensive Care Unit (ICU) admission. By using a patient's full hemoglobin history, we create a novel functional predictor of hemoglobin levels for the year following discharge. Because readmission to the hospital is associated with rapid changes in hemoglobin levels due to surgeries and blood transfusions, we incorporate a multistate joint longitudinal model so that patients can change between hospitalized and at home states throughout the period of interest. This model allows for the state of hemoglobin to influence readmission and discharge hazards, and, in a significant innovation in joint longitudinal modeling, readmission events impact a patient's hemoglobin trajectory via an explicit semiparametric model.

Original languageEnglish (US)
Title of host publicationProceedings - 2022 IEEE 10th International Conference on Healthcare Informatics, ICHI 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages489-491
Number of pages3
ISBN (Electronic)9781665468459
DOIs
StatePublished - 2022
Event10th IEEE International Conference on Healthcare Informatics, ICHI 2022 - Rochester, United States
Duration: Jun 11 2022Jun 14 2022

Publication series

NameProceedings - 2022 IEEE 10th International Conference on Healthcare Informatics, ICHI 2022

Conference

Conference10th IEEE International Conference on Healthcare Informatics, ICHI 2022
Country/TerritoryUnited States
CityRochester
Period6/11/226/14/22

Keywords

  • Anemia
  • Functional Data
  • ICU
  • Joint Longitudinal Model

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Health Informatics

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