Integrative Data Semantics through a Model-enabled Data Stewardship

TL;DR

This paper introduces the Data Steward Tool (DST), which facilitates semi-automatic semantic integration of heterogeneous clinical datasets into a unified data model, enhancing interoperability in dementia research.

Contribution

The paper presents a novel semi-automatic tool and a disease-specific data model that improve data integration and interoperability across diverse clinical datasets.

Findings

DST enables semantic integration of clinical data.

The dementia-specific data model covers 277 variables.

Interoperability between multiple datasets is achieved.

Abstract

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.