  Motivation: The importance of clinical data in understanding the
pathophysiology of complex disorders has prompted the launch of multiple
initiatives designed to generate patient-level data from various modalities.
While these studies can reveal important findings relevant to the disease, each
study captures different yet complementary aspects and modalities which, when
combined, generate a more comprehensive picture of disease aetiology. However,
achieving this requires a global integration of data across studies, which
proves to be challenging given the lack of interoperability of cohort datasets.
Results: Here, we present the Data Steward Tool (DST), an application that
allows for semi-automatic semantic integration of clinical data into ontologies
and global data models and data standards. We demonstrate the applicability of
the tool in the field of dementia research by establishing a Clinical Data
Model (CDM) in this domain. The CDM currently consists of 277 common variables
covering demographics (e.g. age and gender), diagnostics, neuropsychological
tests, and biomarker measurements. The DST combined with this disease-specific
data model shows how interoperability between multiple, heterogeneous dementia
datasets can be achieved.
