ODMSummary: A Tool for Automatic Structured Comparison of Multiple Medical Forms Based on Semantic Annotation with the Unified Medical Language System

Storck, Michael; Krumm, Rainer; Dugas, Martin

doi:10.1371/journal.pone.0164569

Cited by 11 publications

(7 citation statements)

References 16 publications

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“…However, although several countries do not have a national SNOMED license yet, users with a SNOMED license can make use of existing cross-mappings between UMLS and SNOMED. Further, semantically annotated data models can be compared with tools like ODMSummary 21 or CDEGenerator, 14 for example, to develop common data elements for information systems. This has already been done, e.g., for acute coronary syndrome, 5 acute myeloid leukemias, 6 and the neuroinflammatory disease multiple sclerosis.…”

Section: Discussionmentioning

confidence: 99%

Europe's Largest Research Infrastructure for Curated Medical Data Models with Semantic Annotations

Riepenhausen,

Blumenstock,

Niklas

et al. 2024

Methods Inf Med

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Background Structural metadata from the majority of clinical studies and routine health care systems is currently not yet available to the scientific community. Objective To provide an overview of available contents in the Portal of Medical Data Models (MDM Portal). Methods The MDM Portal is a registered European information infrastructure for research and health care, and its contents are curated and semantically annotated by medical experts. It enables users to search, view, discuss, and download existing medical data models. Results The most frequent keyword is “clinical trial” (n = 18,777), and the most frequent disease-specific keyword is “breast neoplasms” (n = 1,943). Most data items are available in English (n = 545,749) and German (n = 109,267). Manually curated semantic annotations are available for 805,308 elements (554,352 items, 58,101 item groups, and 192,855 code list items), which were derived from 25,257 data models. In total, 1,609,225 Unified Medical Language System (UMLS) codes have been assigned, with 66,373 unique UMLS codes. Conclusion To our knowledge, the MDM Portal constitutes Europe's largest collection of medical data models with semantically annotated elements. As such, it can be used to increase compatibility of medical datasets and can be utilized as a large expert-annotated medical text corpus for natural language processing.

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Section: Discussionmentioning

confidence: 99%

Europe's Largest Research Infrastructure for Curated Medical Data Models with Semantic Annotations

Riepenhausen,

Blumenstock,

Niklas

et al. 2024

Methods Inf Med

Self Cite

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“…By using this technical approach, initiatives may save time and costs by proposing a semi-automatically generated set of data elements to experts, who will not have to ponder about every single item from scratch, but may rely on the core of previously used items. All required tools can be accessed online [28, 33, 54].…”

Section: Discussionmentioning

confidence: 99%

“…The tool “CDEGenerator”, was used to compare semantic annotation and identify a semantic core via frequency analysis [32]. With the tool “ODMSummary”, the amount of ItemGroups was determined [33]. A medical expert manually reviewed the results and generated a catalog of disease related, i.e.…”

Section: Methodsmentioning

confidence: 99%

EMR-integrated minimal core dataset for routine health care and multiple research settings: A case study for neuroinflammatory demyelinating diseases

et al. 2019

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Although routine health care and clinical trials usually require the documentation of similar information, data collection is performed independently from each other, resulting in redundant documentation efforts. Standardizing routine documentation can enable secondary use for medical research. Neuroinflammatory demyelinating diseases (NIDs) represent a heterogeneous group of diseases requiring further research to improve patient management. The aim of this work is to develop, implement and evaluate a minimal core dataset in routine health care with a focus on secondary use as case study for NIDs. Therefore, a draft minimal core dataset for NIDs was created by analyzing routine, clinical trial, registry, biobank documentation and existing data standards for NIDs. Data elements (DEs) were converted into the standard format Operational Data Model, semantically annotated and analyzed via frequency analysis. The analysis produced 1958 DEs based on 864 distinct medical concepts. After review and finalization by an interdisciplinary team of neurologists, epidemiologists and medical computer scientists, the minimal core dataset (NID CDEs) consists of 46 common DEs capturing disease-specific information for reuse in the discharge letter and other research settings. It covers the areas of diagnosis, laboratory results, disease progress, expanded disability status scale, therapy and magnetic resonance imaging findings. NID CDEs was implemented in two German university hospitals and a usability study in clinical routine was conducted (participants n = 16) showing a good usability (Mean SUS = 75). From May 2017 to February 2018, 755 patients were documented with the NID CDEs, which indicates the feasibility of developing a minimal core dataset for structured documentation based on previously used documentation standards and integrating the dataset into clinical routine. By sharing, translating and reusing the minimal dataset, a transnational harmonized documentation of patients with NIDs might be realized, supporting interoperability in medical research.

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“…One of the largest repositories of ODM files, the Medical Data Models portal 45 attaches semantic concept codes with the aid of the Alias element to allow rediscovery and further analyses. 6 , 46 , 47 However, this requires an agreement on how concept codes are exactly specified in the free-text attributes. More advanced solutions would be among others the definition of XML schema extensions within ODM.…”

Section: Discussionmentioning

confidence: 99%

Comparison and transformation between CDISC ODM and EN13606 EHR standards in connecting EHR data with clinical trial research data

et al. 2018

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ObjectivesIntegrating Electronic Health Record (EHR) systems into the field of clinical trials still contains several challenges and obstacles. Heterogeneous standards and specifications are used to represent healthcare and clinical trial information. Therefore, this work investigates the mapping and data interoperability between healthcare and research standards: EN13606 used for the EHRs and the Clinical Data Interchange Standards Consortium Operational Data Model (CDISC ODM) used for clinical research.MethodsBased on the specifications of CDISC ODM 1.3.2 and EN13606, a mapping between the structure and components of both standards has been performed. Archetype Definition Language (ADL) forms built with the EN13606 editor were transformed to ODM XML and reviewed. As a proof of concept, clinical sample data has been transformed into ODM and imported into an electronic data capture system. Reverse transformation from ODM to ADL has also been performed and finally reviewed concerning map-ability.ResultsThe mapping between EN13606 and CDISC ODM shows the similarities and differences between the components and overall record structure of the two standards. An EN13606 archetype corresponds with a group of items within CDISC ODM. Transformations of element names, descriptions, different languages, datatypes, cardinality, optionality, units, value range and terminology codes are possible from EN13606 to CDISC ODM and vice versa.ConclusionIt is feasible to map data elements between EN13606 and CDISC ODM and transformation of forms between ADL and ODM XML format is possible with only minor limitations. EN13606 can accommodate clinical information in a more structured manner with more constraints, whereas CDISC ODM is more suitable and specific for clinical trials and studies. It is feasible to transform EHR data in the EN13606 form to ODM to transfer it into research database. The attempt to use EN13606 to build a study protocol (that was already built with CDISC ODM) also suggests the possibility of using EN13606 standard in place of CDISC ODM if needed to avoid transformations.

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ODMSummary: A Tool for Automatic Structured Comparison of Multiple Medical Forms Based on Semantic Annotation with the Unified Medical Language System

Cited by 11 publications

References 16 publications

Europe's Largest Research Infrastructure for Curated Medical Data Models with Semantic Annotations

Europe's Largest Research Infrastructure for Curated Medical Data Models with Semantic Annotations

EMR-integrated minimal core dataset for routine health care and multiple research settings: A case study for neuroinflammatory demyelinating diseases

Comparison and transformation between CDISC ODM and EN13606 EHR standards in connecting EHR data with clinical trial research data

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