Research ExplorerPublicationsModelling Adverse Events with the TOP Phenotyping Framework

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. ed. / R Röhrig; N Grabe; M Haag; U Hübner; U Sax; C O Schmidt; M Sedlmayr. 1. ed. IOS Press, 2023. p. 69-77 (Stud Health Technol Inform; Vol. 307).

Research output: SCORING: Contribution to book/anthology › SCORING: Contribution to collected editions/anthologies › Research › peer-review

Harvard

Beger, C, Boehmer, AM, Mussawy, B, Redeker, L, Matthies, F, Schäfermeier, R, Härdtlein, A, Dreischulte, T, Neumann, D & Uciteli, A 2023, Modelling Adverse Events with the TOP Phenotyping Framework. in R Röhrig, N Grabe, M Haag, U Hübner, U Sax, CO Schmidt & M Sedlmayr (eds), 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. 1 edn, Stud Health Technol Inform, vol. 307, IOS Press, pp. 69-77. https://doi.org/10.3233/SHTI230695

APA

Beger, C., Boehmer, A. M., Mussawy, B., Redeker, L., Matthies, F., Schäfermeier, R., Härdtlein, A., Dreischulte, T., Neumann, D., & Uciteli, A. (2023). Modelling Adverse Events with the TOP Phenotyping Framework. In R. Röhrig, N. Grabe, M. Haag, U. Hübner, U. Sax, C. O. Schmidt, & M. Sedlmayr (Eds.), 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 (1 ed., pp. 69-77). (Stud Health Technol Inform; Vol. 307). IOS Press. https://doi.org/10.3233/SHTI230695

Vancouver

Beger C, Boehmer AM, Mussawy B, Redeker L, Matthies F, Schäfermeier R et al. Modelling Adverse Events with the TOP Phenotyping Framework. In Röhrig R, Grabe N, Haag M, Hübner U, Sax U, Schmidt CO, Sedlmayr M, editors, 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. 1 ed. IOS Press. 2023. p. 69-77. (Stud Health Technol Inform). https://doi.org/10.3233/SHTI230695

Bibtex

@inbook{9d377569d6404938ad0affde8cae2f6f,
title = "Modelling Adverse Events with the TOP Phenotyping Framework",
abstract = "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.",
keywords = "Humans, Algorithms, Brain, Databases, Factual, Electronic Health Records, Delirium",
author = "Christoph Beger and Boehmer, {Anna Maria} and Beate Mussawy and Louisa Redeker and Franz Matthies and Ralph Sch{\"a}fermeier and Annette H{\"a}rdtlein and Tobias Dreischulte and Daniel Neumann and Alexandr Uciteli",
year = "2023",
month = sep,
day = "12",
doi = "10.3233/SHTI230695",
language = "English",
isbn = "978-1-64368-428-4",
series = "Stud Health Technol Inform",
publisher = "IOS Press",
pages = "69--77",
editor = "R R{\"o}hrig and N Grabe and M Haag and U H{\"u}bner and U Sax and Schmidt, {C O} and M Sedlmayr",
booktitle = "German Medical Data Sciences 2023 – Science. Close to People.",
address = "Netherlands",
edition = "1",

}

RIS

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 -