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Modelling Adverse Events with the TOP Phenotyping Framework.

Christoph Beger1, Anna Maria Boehmer2, Beate Mussawy3

  • 1Institute for Medical Informatics, Statistics and Epidemiology, Leipzig University.

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|September 12, 2023
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Summary
This summary is machine-generated.

Identifying preventable medication-associated adverse events (AEs) is crucial. This study introduces an ontology-based framework to model and execute phenotype algorithms for detecting AEs like delirium in electronic medical records, improving patient care.

Keywords:
adverse eventsalgorithmscomputable phenotypeselectronic health records

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Area of Science:

  • Clinical informatics
  • Health informatics
  • Biomedical informatics

Background:

  • Medication-associated adverse events (AEs) pose a significant challenge in patient care, with a substantial proportion being preventable.
  • Electronic Medical Records (EMRs) are utilized for AE detection via phenotype algorithms, but existing tools lack standardized representation and complex logic handling.
  • Delirium, an acute brain disorder, presents difficulties in operationalization within EMR data.

Conclusions:

  • The TOP Framework offers a standardized and flexible approach to modeling and executing phenotype algorithms for AE detection.
  • Semantic modeling enhances the reusability and interoperability of phenotype algorithms across diverse healthcare settings.
  • This approach can improve the identification and prevention of medication-associated adverse events, including complex conditions like delirium.