CDEGenerator: an online platform to learn from existing data models to build model registries

Abstract

ObjectiveBest-practice data models harmonize semantics and data structure of medical variables in clinical or epidemiological studies. While there exist several published data sets, it remains challenging to find and reuse published eligibility criteria or other data items that match specific needs of a newly planned study or registry. A novel Internet-based method for rapid comparison of published data models was implemented to enable reuse, customization, and harmonization of item catalogs for the early planning and development phase of research databases.MethodsBased on prior work, a European information infrastructure with a large collection of medical data models was established. A newly developed analysis module called CDEGenerator provides systematic comparison of selected data models and user-tailored creation of minimum data sets or harmonized item catalogs. Usability was assessed by eight external medical documentation experts in a workshop by the umbrella organization for networked medical research in Germany with the System Usability Scale.ResultsThe analysis and item-tailoring module provides multilingual comparisons of semantically complex eligibility criteria of clinical trials. The System Usability Scale yielded “good usability” (mean 75.0, range 65.0–92.5). User-tailored models can be exported to several data formats, such as XLS, REDCap or Operational Data Model by the Clinical Data Interchange Standards Consortium, which is supported by the US Food and Drug Administration and European Medicines Agency for metadata exchange of clinical studies.ConclusionThe online tool provides user-friendly methods to reuse, compare, and thus learn from data items of standardized or published models to design a blueprint for a harmonized research database.

Common Data Elements for Acute Coronary Syndrome: Analysis Based on the Unified Medical Language System

“…All encoded forms were then automatically compared and analyzed using CDEGenerator [30]. The Web application generated a list of all UMLS codes, their absolute and relative frequencies in different documentation contexts, and an overview of original questions and form occurrence.…”

Core Data Elements in Acute Myeloid Leukemia: A Unified Medical Language System–Based Semantic Analysis and Experts’ Review

“…Disagreements in coding were discussed between physicians regarding coding principles [23] and the frequency rate–assisted MDM-Portal ODM editor was used. The coded ODM forms were analyzed by CDEGenerator [13,24], an in-house implemented Java-based Web application. CDEGenerator automatically sorts medical concepts (eg, medication) of the existing data items according to their frequency (by counting identical UMLS codes) and also shows similarity of medical concepts based on the code overlaps of postcoordinated concepts, for example, medication start date is similar to medication end date , as the main concept medication is the same.…”

Comparing SDTM and FHIR® for Real World Data from Electronic Health Records for Clinical Trial Submissions

“…Annotation relied mostly on Unified Medical Language System Concept Unique Identifiers (UMLS [8] CUIs) which are used in the Portal of Medical Data Models (MDM Portal, https://medical-data-models.org/), supplemented by CUIs from UMLS if necessary. ODM files were semantically analyzed with CDEgenerator [9]. Codecleaning (uniform CUI coding) was applied to improve matching of codes between different coders.…”