How to customize common data models for rare diseases: an OMOP-based implementation and lessons learned.

ABSTRACT:

Background

Given the geographical sparsity of Rare Diseases (RDs), assembling a cohort is often a challenging task. Common data models (CDM) can harmonize disparate sources of data that can be the basis of decision support systems and artificial intelligence-based studies, leading to new insights in the field. This work is sought to support the design of large-scale multi-center studies for rare diseases.

Methods

In an interdisciplinary group, we derived a list of elements of RDs in three medical domains (endocrinology, gastroenterology, and pneumonology) according to specialist knowledge and clinical guidelines in an iterative process. We then defined a RDs data structure that matched all our data elements and built Extract, Transform, Load (ETL) processes to transfer the s

SUBMITTER: Ahmadi N 

PROVIDER: S-EPMC11325822 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature