Trajectories: A framework for detecting temporal clinical event sequences from health data standardized to the Observational Medical Outcomes Partnership (OMOP) Common Data Model

Kadri Künnapuu; Solomon Ioannou; Kadri Ligi; Raivo Kolde; Sven Laur; Jaak Vilo; Peter R. Rijnbeek; Sulev Reisberg

doi:10.1093/jamiaopen/ooac021

Trajectories: A framework for detecting temporal clinical event sequences from health data standardized to the Observational Medical Outcomes Partnership (OMOP) Common Data Model

Kadri Künnapuu, Solomon Ioannou, Kadri Ligi, Raivo Kolde, Sven Laur, Jaak Vilo, Peter R. Rijnbeek, Sulev Reisberg^*

^*Corresponding author for this work

Medical Informatics

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Abstract

Objective: To develop a framework for identifying temporal clinical event trajectories from Observational Medical Outcomes Partnership-formatted observational healthcare data. Materials and Methods: A 4-step framework based on significant temporal event pair detection is described and implemented as an open-source R package. It is used on a population-based Estonian dataset to first replicate a large Danish population-based study and second, to conduct a disease trajectory detection study for type 2 diabetes patients in the Estonian and Dutch databases as an example. Results: As a proof of concept, we apply the methods in the Estonian database and provide a detailed breakdown of our findings. All Estonian population-based event pairs are shown. We compare the event pairs identified from Estonia to Danish and Dutch data and discuss the causes of the differences. The overlap in the results was only 2.4%, which highlights the need for running similar studies in different populations. Conclusions: For the first time, there is a complete software package for detecting disease trajectories in health data.

Original language	English
Article number	ooac021
Journal	JAMIA Open
Volume	5
Issue number	1
DOIs	https://doi.org/10.1093/jamiaopen/ooac021
Publication status	Published - Apr 2022

Bibliographical note

Funding Information:
This work was supported by the Estonian Research Council (grants number PRG1095, RITA1/02-96-11); the European Union through the European Regional Development Fund (grant number EU48684); and the European Social Fund via IT Academy program. The European Health Data & Evidence Network has received funding from the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement no. 806968. The JU receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA. This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 813533.

Publisher Copyright:
© 2022 The Author(s).

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Trajectories a framework for detecting temporal clinical event sequences from health data standardized to the Observational Medical Outcomes Partnership (OMOP) Common Data ModelFinal published version, 1.04 MBLicence: CC BY

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title = "Trajectories: A framework for detecting temporal clinical event sequences from health data standardized to the Observational Medical Outcomes Partnership (OMOP) Common Data Model",

abstract = "Objective: To develop a framework for identifying temporal clinical event trajectories from Observational Medical Outcomes Partnership-formatted observational healthcare data. Materials and Methods: A 4-step framework based on significant temporal event pair detection is described and implemented as an open-source R package. It is used on a population-based Estonian dataset to first replicate a large Danish population-based study and second, to conduct a disease trajectory detection study for type 2 diabetes patients in the Estonian and Dutch databases as an example. Results: As a proof of concept, we apply the methods in the Estonian database and provide a detailed breakdown of our findings. All Estonian population-based event pairs are shown. We compare the event pairs identified from Estonia to Danish and Dutch data and discuss the causes of the differences. The overlap in the results was only 2.4%, which highlights the need for running similar studies in different populations. Conclusions: For the first time, there is a complete software package for detecting disease trajectories in health data.",

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note = "Funding Information: This work was supported by the Estonian Research Council (grants number PRG1095, RITA1/02-96-11); the European Union through the European Regional Development Fund (grant number EU48684); and the European Social Fund via IT Academy program. The European Health Data & Evidence Network has received funding from the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement no. 806968. The JU receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA. This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sk{\l}odowska-Curie grant agreement no. 813533. Publisher Copyright: {\textcopyright} 2022 The Author(s).",

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AU - Laur, Sven

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Trajectories: A framework for detecting temporal clinical event sequences from health data standardized to the Observational Medical Outcomes Partnership (OMOP) Common Data Model

Abstract

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