In the era of precision medicine, interoperability of biomedical data is crucial for facilitating collaborative research and the concept of minimum data set (MDS) has arised as a collection of data elements using a standard approach to allow clinical data sharing and its use for research purposes. Health data are typically voluminous, complex, and sometimes too ambiguous to generate indicators that can provide knowledge and information on health. Our project aims to address this challenge by developing a versatile web-based tool, called ""DataModel Converter"" which enables conversion from cohort information to different biomedical minimum data sets offering an intuitive interface for users to select individuals from a cohort from a clinical database (eg. OMOP CDM, OpenEHR, etc) and effortlessly transform its structured data into various other standard formats, including B1MG Minimal Dataset for Cancer, BBMRI cohort definitions, OMOP cohorts, Phenopackets, beacon v2, etc.
The key objectives of our project include:
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Designing an interactive and user-friendly web application for cohort selection and data conversion.
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Implementing backend functionalities to retrieve and manipulate data from clinical databases.
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Developing semantic mappings between different data models while preserving data integrity and semantics (OMOP CDM, OpenEHR, Phenopackets, B1MG, BBMRI, etc).
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Ensuring scalability, performance of the DataModel Converter platform to handle large-scale datasets.
By providing researchers and healthcare professionals with a flexible and efficient means to harmonize data across disparate data models, our project aims to accelerate biomedical research, enhance collaboration, and ultimately contribute to advancements in personalized medicine and patient care."
Sergi Aguiló-Castillo, Alberto Labarga