Transformation and Evaluation of the MIMIC ...
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Article dans une revue scientifique: Article original
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Title :
Transformation and Evaluation of the MIMIC Database in the OMOP Common Data Model: Development and Usability Study
Author(s) :
Paris, N. [Auteur]
Lamer, Antoine [Auteur]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Parrot, A. [Auteur]
Lamer, Antoine [Auteur]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Parrot, A. [Auteur]
Journal title :
JMIR Medical Informatics
Abbreviated title :
JMIR Med Inform
Volume number :
9
Pages :
e30970
Publication date :
2021-12
ISSN :
2291-9694
English keyword(s) :
data reuse
open data
OMOP
common data model
critical care
machine learning
big data
health informatics
health data
health database
electronic health records
open access database
digital health
intensive care
health care
open data
OMOP
common data model
critical care
machine learning
big data
health informatics
health data
health database
electronic health records
open access database
digital health
intensive care
health care
HAL domain(s) :
Sciences du Vivant [q-bio]
English abstract : [en]
Background
In the era of big data, the intensive care unit (ICU) is likely to benefit from real-time computer analysis and modeling based on close patient monitoring and electronic health record data. The Medical ...
Show more >Background In the era of big data, the intensive care unit (ICU) is likely to benefit from real-time computer analysis and modeling based on close patient monitoring and electronic health record data. The Medical Information Mart for Intensive Care (MIMIC) is the first open access database in the ICU domain. Many studies have shown that common data models (CDMs) improve database searching by allowing code, tools, and experience to be shared. The Observational Medical Outcomes Partnership (OMOP) CDM is spreading all over the world. Objective The objective was to transform MIMIC into an OMOP database and to evaluate the benefits of this transformation for analysts. Methods We transformed MIMIC (version 1.4.21) into OMOP format (version 5.3.3.1) through semantic and structural mapping. The structural mapping aimed at moving the MIMIC data into the right place in OMOP, with some data transformations. The mapping was divided into 3 phases: conception, implementation, and evaluation. The conceptual mapping aimed at aligning the MIMIC local terminologies to OMOP's standard ones. It consisted of 3 phases: integration, alignment, and evaluation. A documented, tested, versioned, exemplified, and open repository was set up to support the transformation and improvement of the MIMIC community's source code. The resulting data set was evaluated over a 48-hour datathon. Results With an investment of 2 people for 500 hours, 64% of the data items of the 26 MIMIC tables were standardized into the OMOP CDM and 78% of the source concepts mapped to reference terminologies. The model proved its ability to support community contributions and was well received during the datathon, with 160 participants and 15,000 requests executed with a maximum duration of 1 minute. Conclusions The resulting MIMIC-OMOP data set is the first MIMIC-OMOP data set available free of charge with real disidentified data ready for replicable intensive care research. This approach can be generalized to any medical field.Show less >
Show more >Background In the era of big data, the intensive care unit (ICU) is likely to benefit from real-time computer analysis and modeling based on close patient monitoring and electronic health record data. The Medical Information Mart for Intensive Care (MIMIC) is the first open access database in the ICU domain. Many studies have shown that common data models (CDMs) improve database searching by allowing code, tools, and experience to be shared. The Observational Medical Outcomes Partnership (OMOP) CDM is spreading all over the world. Objective The objective was to transform MIMIC into an OMOP database and to evaluate the benefits of this transformation for analysts. Methods We transformed MIMIC (version 1.4.21) into OMOP format (version 5.3.3.1) through semantic and structural mapping. The structural mapping aimed at moving the MIMIC data into the right place in OMOP, with some data transformations. The mapping was divided into 3 phases: conception, implementation, and evaluation. The conceptual mapping aimed at aligning the MIMIC local terminologies to OMOP's standard ones. It consisted of 3 phases: integration, alignment, and evaluation. A documented, tested, versioned, exemplified, and open repository was set up to support the transformation and improvement of the MIMIC community's source code. The resulting data set was evaluated over a 48-hour datathon. Results With an investment of 2 people for 500 hours, 64% of the data items of the 26 MIMIC tables were standardized into the OMOP CDM and 78% of the source concepts mapped to reference terminologies. The model proved its ability to support community contributions and was well received during the datathon, with 160 participants and 15,000 requests executed with a maximum duration of 1 minute. Conclusions The resulting MIMIC-OMOP data set is the first MIMIC-OMOP data set available free of charge with real disidentified data ready for replicable intensive care research. This approach can be generalized to any medical field.Show less >
Language :
Anglais
Audience :
Internationale
Popular science :
Non
Administrative institution(s) :
Université de Lille
CHU Lille
CHU Lille
Submission date :
2023-11-15T05:21:40Z
2024-04-19T07:05:09Z
2024-04-19T07:05:09Z
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