Modelling Adverse Events with the TOP Phenotyping Framework
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Modelling Adverse Events with the TOP Phenotyping Framework. / Beger, Christoph; Boehmer, Anna Maria; Mussawy, Beate; Redeker, Louisa; Matthies, Franz; Schäfermeier, Ralph; Härdtlein, Annette; Dreischulte, Tobias; Neumann, Daniel; Uciteli, Alexandr.
German Medical Data Sciences 2023 – Science. Close to People.: Proceedings of the 68th Annual Meeting of the German Association of Medical Informatics, Biometry, and Epidemiology e.V. (gmds) 2023 in Heilbronn, Germany. Hrsg. / R Röhrig; N Grabe; M Haag; U Hübner; U Sax; C O Schmidt; M Sedlmayr. 1. Aufl. IOS Press, 2023. S. 69-77 (Stud Health Technol Inform; Band 307).Publikationen: SCORING: Beitrag in Buch/Sammelwerk › SCORING: Beitrag in Sammelwerk › Forschung › Begutachtung
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TY - CHAP
T1 - Modelling Adverse Events with the TOP Phenotyping Framework
AU - Beger, Christoph
AU - Boehmer, Anna Maria
AU - Mussawy, Beate
AU - Redeker, Louisa
AU - Matthies, Franz
AU - Schäfermeier, Ralph
AU - Härdtlein, Annette
AU - Dreischulte, Tobias
AU - Neumann, Daniel
AU - Uciteli, Alexandr
PY - 2023/9/12
Y1 - 2023/9/12
N2 - The detection and prevention of medication-related health risks, such as medication-associated adverse events (AEs), is a major challenge in patient care. A systematic review on the incidence and nature of in-hospital AEs found that 9.2% of hospitalised patients suffer an AE, and approximately 43% of these AEs are considered to be preventable. Adverse events can be identified using algorithms that operate on electronic medical records (EMRs) and research databases. Such algorithms normally consist of structured filter criteria and rules to identify individuals with certain phenotypic traits, thus are referred to as phenotype algorithms. Many attempts have been made to create tools that support the development of algorithms and their application to EMRs. However, there are still gaps in terms of functionalities of such tools, such as standardised representation of algorithms and complex Boolean and temporal logic. In this work, we focus on the AE delirium, an acute brain disorder affecting mental status and attention, thus not trivial to operationalise in EMR data. We use this AE as an example to demonstrate the modelling process in our ontology-based framework (TOP Framework) for modelling and executing phenotype algorithms. The resulting semantically modelled delirium phenotype algorithm is independent of data structure, query languages and other technical aspects, and can be run on a variety of source systems in different institutions.
AB - The detection and prevention of medication-related health risks, such as medication-associated adverse events (AEs), is a major challenge in patient care. A systematic review on the incidence and nature of in-hospital AEs found that 9.2% of hospitalised patients suffer an AE, and approximately 43% of these AEs are considered to be preventable. Adverse events can be identified using algorithms that operate on electronic medical records (EMRs) and research databases. Such algorithms normally consist of structured filter criteria and rules to identify individuals with certain phenotypic traits, thus are referred to as phenotype algorithms. Many attempts have been made to create tools that support the development of algorithms and their application to EMRs. However, there are still gaps in terms of functionalities of such tools, such as standardised representation of algorithms and complex Boolean and temporal logic. In this work, we focus on the AE delirium, an acute brain disorder affecting mental status and attention, thus not trivial to operationalise in EMR data. We use this AE as an example to demonstrate the modelling process in our ontology-based framework (TOP Framework) for modelling and executing phenotype algorithms. The resulting semantically modelled delirium phenotype algorithm is independent of data structure, query languages and other technical aspects, and can be run on a variety of source systems in different institutions.
KW - Humans
KW - Algorithms
KW - Brain
KW - Databases, Factual
KW - Electronic Health Records
KW - Delirium
U2 - 10.3233/SHTI230695
DO - 10.3233/SHTI230695
M3 - SCORING: Contribution to collected editions/anthologies
C2 - 37697839
SN - 978-1-64368-428-4
T3 - Stud Health Technol Inform
SP - 69
EP - 77
BT - German Medical Data Sciences 2023 – Science. Close to People.
A2 - Röhrig, R
A2 - Grabe, N
A2 - Haag, M
A2 - Hübner, U
A2 - Sax, U
A2 - Schmidt, C O
A2 - Sedlmayr, M
PB - IOS Press
ER -