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European-Federation-for-Medical-Informatics (EFMI) Special Topic Conference (STC)

作者： Haberson, Andrea Rinner, Christoph Gall, Walter Med Univ Vienna Ctr Med Stat Informat & Intelligent Syst Spitalgasse 23 A-1090 Vienna Austria

ISBN: (纸本)9781614999591;9781614999584

The suitability of the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) for Austrian pseudonymized claims data from social security institutions and information about hospital stays is evaluated. 1,023 (99.7%) of ATC codes and 3,695 (98.6%) of ICD10 codes coincide with the OMOP vocabulary. Mappings for the local vocabularies like the Austrian pharmaceutical registration numbers, the Socio-Economic Index and professional groups, to the OMOP vocabulary do not exist. A standardization with the OMOP CDM is possible, however an initial, not negligible effort is required to adapt and incorporate the vocabulary.

关键词： standardized health data secondary use claims data drug safety common data model OMOP

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Annotating Cohort data Elements with OHDSI common data model to Promote Research Reproducibility

Annotating Cohort Data Elements with OHDSI Common Data Model...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM) - Human Genomics

作者： Zhao, Yiqing Wang, Yanshan Wang, Henry Yan, Benjamin Shen, Feichen Peterson, Kevin J. Rocca, Walter A. St Sauver, Jennifer Liu, Hongfang Mayo Clin Dept Hlth Sci Res Rochester MN 55905 USA Princeton Univ Dept Comp Sci Princeton NJ 08544 USA

ISBN: (纸本)9781538654880

Rapid increase in the implementation of electronic health records (EHRs) has led to an unprecedented expansion in the availability of dense longitudinal cohort datasets for clinical studies. However, there is a growing need to ensure data traceability, validity, and reproducibility for EHR-based clinical research. Applying common data models that standardize EHR data elements could reduce research discrepancies and improve research reproducibility. As a pilot study, we utilized the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) developed by the Observational Health data Sciences and Informatics (OHDSI) community to annotate cohort data elements from the local Rochester Epidemiology Project (REP). We evaluated the data coverage of the OMOP CDM by manually annotating the cohorts from 92 REP publications. Next, we examined cohort similarities between different publications using OMOP elements. Evaluation results showed that the OMOP CDM covers 99.8% of the content that is associated with cohort attributes. It demonstrated that the OMOP CDM can be used for data element standardization when extracting information from EHR and clinical registries. The OMOP CDM also shows its potential to be used as a tool for retrospective examination of cohort definition consistencies and epidemiology model similarities.

关键词： common data model electronic health record cohort study data standardization

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Implementing a Portable Clinical NLP System with a common data model a - Lisp Perspective

Implementing a Portable Clinical NLP System with a Common Da...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM) - Human Genomics

作者： Luo, Yuan Szolovits, Peter Northwestern Univ Dept Prevent Med Chicago IL 60611 USA MIT CSAIL 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (纸本)9781538654880

This paper presents a Lisp architecture for a portable NLP system, termed LAPNLP, for processing clinical notes. LAPNLP integrates multiple standard, customized and in-house developed NLP tools. Our system facilitates portability across different institutions and data systems by incorporating an enriched common data model (CDM) to standardize necessary data elements. It utilizes LMLS to perform domain adaptation when integrating generic domain NLP tools. It also features stand-off annotations that are specified by positional reference to the original document. We built an interval tree based search engine to efficiently query and retrieve the stand-off annotations by specifying positional requirements. We also developed a utility to convert an inline annotation format to stand-off annotations to enable the reuse of clinical text datasets with in line annotations. We experimented with our system on several NLP facilitated tasks including computational phenotyping for lymphoma patients and semantic relation extraction for clinical notes. These experiments showcased the broader applicability and utility of LAPNLP.

关键词： Portable ALP Computational Phenotyping Relation Extraction common data model Lisp

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Key Research Areas for Building and Deploying a common data model for an Intensive Medicine data Space in Europe and Beyond

Key Research Areas for Building and Deploying a Common Data ...

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34th Medical Informatics Europe Conference, MIE 2024

作者： Parra-Calderón, Carlos Luis Rodríguez Mejías, Silvia Colpaert, Kirsten Balzer, Felix Delange, Boris Cuggia, Marc Delamarre, Denis Daniel, Christel Moinat, Maxim van Den Brand, Jan van Genderen, Michel E. Fleureng, Lucas Jungh, Christian Álvarez-Romero, Celia Computational Health Informatics Group Institute of Biomedicine of Seville IBIS Virgen del Rocío University Hospital CSIC University of Seville Spain Data Science Institute Ghent University Hospital Ghent Belgium Institute of Medical Informatics Charité - Universitätsmedizin Berlin Freie Universität Berlin Humboldt-Universität zu Berlin Germany CHU Rennes INSERM LTSI-UMR 1099 Univ Rennes Rennes35000 France Assistance Publique Hôpitaux de Paris Paris France Sorbonne Université Inserm Université Sorbonne Paris Nord LIMICS Paris France Erasmus MC University Medical Center Department of Adult Intensive Care Rotterdam Netherlands Dusseldorf Germany

ISBN: (纸本)9781643685335

Key Research Areas (KRAs) were identified to establish a semantic interoperability framework for intensive medicine data in Europe. These include assessing common data model value, ensuring smooth data interoperability, supporting data standardization for efficient dataset use, and defining anonymization requirements to balance data protection and innovation. © 2024 The Authors.

关键词： Artificial Intelligence Clinical Decision Support common data model data Space Intensive Care Interoperability Standardization

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CdmCL, a specific textual constraint language for common data model 14

CdmCL, a specific textual constraint language for common dat...

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14th International Workshop on OCL and Textual modeling: Applications and Case Studies, OCL 2014

作者： Ahmed, Ahmed Vallejo, Paola Kerboeuf, Mickaël Babau, Jean-Philippe Lab-STICC UBO UEB Brest France

common data model is an abstract data model for scientific datasets that can be constrained by OCL. To hide complexity of OCL, CdmCL is proposed as a specific textual constraint language for CDM. CdmCL is based on the CDM structure and results in a set of constraint categories. CdmCL provides a user-friendly front end in order to define constraints which are subsequently translated to OCL. The conformity tool is based on an existing OCL checker integrated in EMF. CdmCL is experimented on the OceanSITES standard.

关键词： common data model Conformity Constraint generation OCL

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How to Accurately Detect Renal Replacement Therapy Weaning in Intensive Care: data Quality and Standardization Considerations for the OMOP common data model

How to Accurately Detect Renal Replacement Therapy Weaning i...

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34th Medical Informatics Europe Conference, MIE 2024

作者： Popoff, Benjamin Delange, Boris Kallout, Julien Cuggia, Marc Clavier, Thomas Bouzille, Guillaume Univ Rennes CHU Rennes INSERM LTSI-UMR 1099 Rennes France CHU Rouen Department of Anaesthesiology and Critical Care Rouen France

ISBN: (纸本)9781643685335

This study assesses the effectiveness of the Observational Medical Outcomes Partnership common data model (OMOP CDM) in standardising Continuous Renal Replacement Therapy (CRRT) data from intensive care units (ICU) of two French university hospitals. Our objective was to extract and standardise data from various sources, enabling the development of predictive models for CRRT weaning that are agnostic to the data's origin. data for 1,696 ICU stays from the two data sources were extracted, transformed, and loaded into the OMOP format after semantic alignment of 46 CRRT standard concepts. Although the OMOP CDM demonstrated potential in harmonising CRRT data, we encountered challenges related to data variability and the lack of standard concepts. Despite these challenges, our study supports the promise of the OMOP CDM for ICU data standardization, suggesting that further refinement and adaptation could significantly improve clinical decision making and patient outcomes in critical care settings. © 2024 The Authors.

关键词： common data model data standardisation intensive care unit OMOP renal replacement therapy

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common data model for Healthcare data

Common Data Model for Healthcare data

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IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology

作者： Umair Khan Aditya Kuchekar Huzaifa Kothari Reeta Koshy Department of Computer Engineering Bharatiya Vidya Bhavan's Sardar Patel Institute of Technology

ISBN: (数字)9781538624401

ISBN: (纸本)9781538624418

There is a huge volume of data made openly available to the masses on Government data sites such as the . However, it is very difficult to make sense between data from two different data sets or different domains. As a result a lot of time is spent in just collecting and linking this data from various sources before it can be put to use. A potential solution to this problem is using a common data model. A common data model is a data silo or warehouse which can accommodate any data from any source in one pre-designed format. In this paper we discuss an ETL process that allows users to remodel and store data, and a proposed schema for a common data model which will house this transformed data under one roof. We also perform our own analysis on the proposed data model for a particular use-case;state-wise statistics of data related to pregnancy in India. Thus we prove that the common data model is able to integrate data from different sources and facilitate cross domain analysis and linking of data.

关键词： common data model ETL extract transform load data warehouse Scan file Mapping file data models data warehousing Remodeling WAREHOUSE

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common data model for Healthcare data

Common Data Model for Healthcare data

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International Conference on Computing, Communication and Networking Technologies

作者： Umair M. Khan Huzaifa Kothari Aditya Kuchekar Reeta Koshy Department of Computer Engineering Bharatiya Vidya Bhavan's Sardar Patel Institute of Technology Mumbai India

关键词： common data model ETL Extract Transform Load data Warehouse Columnar database Mapping file

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Converting clinical document architecture documents to the common data model for incorporating health information exchange data in observational health studies: CDA to CDM

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JOURNAL OF BIOMEDICAL INFORMATICS 2020年 107卷 103459-103459页

作者： Ji, Hyerim Kim, Seok Yi, Soyoung Hwang, Hee Kim, Jeong-Whun Yoo, Sooyoung Seoul Natl Univ Bundang Hosp Off eHlth Res & Business Seoul South Korea Seoul Natl Univ Bundang Hosp Dept Pediat Seoul South Korea Seoul Natl Univ Bundang Hosp Dept Otorhinolaryngol Seoul South Korea

Background: Utilization of standard health information exchange (HIE) data is growing due to the high adoption rate and interoperability of electronic health record (EHR) systems. However, integration of HIE data into an EHR system is not yet fully adopted in clinical research. In addition, data quality should be verified for the secondary use of these data. Thus, the aims of this study were to convert referral documents in a Health Level 7 (HL7) clinical document architecture (CDA) to the common data model (CDM) to facilitate HIE data availability for longitudinal data analysis, and to identify data quality levels for application in future clinical studies. Methods: A total of 21,492 referral CDA documents accumulated for over 10 years in a tertiary general hospital in South Korea were analyzed. To convert CDA documents to the Observational Medical Outcomes Partnership (OMOP) CDM, processes such as CDA parsing, data cleaning, standard vocabulary mapping, CDA-to-CDM mapping, and CDM conversion were performed. The quality of CDM data was then evaluated using the Achilles Heel and visualized with the Achilles tool. Results: Mapping five CDA elements (document header, problem, medication, laboratory, and procedure) into an OMOP CDM table resulted in population of 9 CDM tables (person, visit occurrence, condition_occurrence, drug_exposure, measurement, observation, procedure_occurrence, care_site, and provider). Three CDM tables (drug_era, condition_era, and observation_period) were derived from the converted table. From vocabulary mapping codes in CDA documents according to domain, 98.6% of conditions, 68.8% of drugs, 35.7% of measurements, 100% of observation, and 56.4% of procedures were mapped as standard concepts. The conversion rates of the CDA to the OMOP CDM were 96.3% for conditions, 97.2% for drug exposure, 98.1% for procedure occurrence, 55.1% for measurements, and 100% for observation. Conclusions: We examined the possibility of CDM conversion by defining ma

关键词： Clinical document architecture common data model Observational Medical Outcomes Partnership Referral documents

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Harmonizing Norwegian registries onto OMOP common data model: Mapping challenges and opportunities for pregnancy and COVID-19 research

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INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS 2024年 191卷 105602-105602页

作者： Trinh, Nhung T. H. Houghtaling, Jared Bernal, Fabian L. M. Hayati, Saeed Maglanoc, Luigi A. Lupattelli, Angela Halvorsen, Lars Nordeng, Hedvig M. E. Univ Oslo Fac Math & Nat Sci Dept Pharm Pharmacoepidemiol & Drug Safety Res Grp Oslo Norway EdenceHlth NV Kontich Belgium Univ Oslo IT Dept Div Res Disseminat & Educ Oslo Norway Norwegian Inst Publ Hlth Dept Child Hlth & Dev Oslo Norway

Objective: Norwegian health registries covering entire population are used for administration, research, and emergency preparedness. We harmonized these data onto the Observational Medical Outcomes Partnership common data model (OMOP CDM) and enrich real-world data in OMOP format with COVID-19 related data. Methods: data from six registries (2018-2021) covering birth registrations, selected primary and secondary care events, vaccinations, and communicable disease notifications were mapped onto the OMOP CDM v5.3. An Extract-Transform-Load (ETL) pipeline was developed on simulated data using data characterization documents and scanning tools. We ran dashboard quality checks, cohort generations, investigated differences between source and mapped data, and refined the ETL accordingly. Results: We mapped 1.5 billion rows of data of 5,673,845 individuals. Among these, there were 804,277 pregnancies, 483,585 mothers together with 792,477 children, and 472,948 fathers. We identified 382,516 positive tests for COVID-19 in 380,794 patients. These figures are consistent with results from source data. In addition to 11 million source codes mapped automatically, we mapped 237 non-standard codes to standard concepts and introduced 38 custom concepts to accommodate pregnancy-related terminologies that were not supported by OMOP CDM vocabularies. A total of 3,700/3,705 (99.8%) checks passed. The 5 failed checks could be explained by the nature of the data and only represent a small number of records. Discussion and conclusion: Norwegian registry data were successfully harmonized onto OMOP CDM with high level of concordance and provides valuable source for federated COVID-19 related research. Our mapping experience is highly valuable for data partners with Nordic health registries.

关键词： Health registries COVID-19 Phenotyping OMOP common data model

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