FAIRVASC: A semantic web approach to rare disease registry integration
McGlinn, Kris ; Rutherford, Matthew ; Gisslander, Karl LU
; Hederman, Lucy
; Little, Mark A.
and O'Sullivan, Declan
(2022)
In Computers in Biology and Medicine
145.
- Abstract
- Rare disease data is often fragmented within multiple heterogeneous siloed regional disease registries, each containing a small number of cases. These data are particularly sensitive, as low subject counts make the identification of patients more likely, meaning registries are not inclined to share subject level data outside their registries. At the same time access to multiple rare disease datasets is important as it will lead to new research opportunities and analysis over larger cohorts. To enable this, two major challenges must therefore be overcome. The first is to integrate data at a semantic level, so that it is possible to query over registries and return results which are comparable. The second is to enable queries which do not... (More)
- Rare disease data is often fragmented within multiple heterogeneous siloed regional disease registries, each containing a small number of cases. These data are particularly sensitive, as low subject counts make the identification of patients more likely, meaning registries are not inclined to share subject level data outside their registries. At the same time access to multiple rare disease datasets is important as it will lead to new research opportunities and analysis over larger cohorts. To enable this, two major challenges must therefore be overcome. The first is to integrate data at a semantic level, so that it is possible to query over registries and return results which are comparable. The second is to enable queries which do not take subject level data from the registries. To meet the first challenge, this paper presents the FAIRVASC ontology to manage data related to the rare disease anti-neutrophil cytoplasmic antibody (ANCA) associated vasculitis (AAV), which is based on the harmonisation of terms in seven European data registries. It has been built upon a set of key clinical questions developed by a team of experts in vasculitis selected from the registry sites and makes use of several standard classifications, such as Systematized Nomenclature of Medicine - Clinical Terms (SNOMED-CT) and Orphacode. It also presents the method for adding semantic meaning to AAV data across the registries using the declarative Relational to Resource Description Framework Mapping Language (R2RML). To meet the second challenge a federated querying approach is presented for accessing aggregated and pseudonymized data, and which supports analysis of AAV data in a manner which protects patient privacy. For additional security the federated querying approach is augmented with a method for auditing queries (and the uplift process) using the provenance ontology (PROV-O) to track when queries and changes occur and by whom. The main contribution of this work is the successful application of semantic web technologies and federated queries to provide a novel infrastructure that can readily incorporate additional registries, thus providing access to harmonised data relating to unprecedented numbers of patients with rare disease, while also meeting data privacy and security concerns. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/5184b347-e10d-4c9a-9f7b-a8ecb5a82d49
- author
- McGlinn, Kris
; Rutherford, Matthew
; Gisslander, Karl
LU
; Hederman, Lucy
; Little, Mark A.
and O'Sullivan, Declan
- organization
- publishing date
- 2022-03-01
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Computers in Biology and Medicine
- volume
- 145
- article number
- 105313
- publisher
- Elsevier
- external identifiers
-
- pmid:35405400
- scopus:85127798577
- ISSN
- 0010-4825
- DOI
- 10.1016/j.compbiomed.2022.105313
- language
- English
- LU publication?
- yes
- id
- 5184b347-e10d-4c9a-9f7b-a8ecb5a82d49
- date added to LUP
- 2022-04-19 13:17:24
- date last changed
- 2022-04-28 00:00:03
@article{5184b347-e10d-4c9a-9f7b-a8ecb5a82d49,
abstract = {{Rare disease data is often fragmented within multiple heterogeneous siloed regional disease registries, each containing a small number of cases. These data are particularly sensitive, as low subject counts make the identification of patients more likely, meaning registries are not inclined to share subject level data outside their registries. At the same time access to multiple rare disease datasets is important as it will lead to new research opportunities and analysis over larger cohorts. To enable this, two major challenges must therefore be overcome. The first is to integrate data at a semantic level, so that it is possible to query over registries and return results which are comparable. The second is to enable queries which do not take subject level data from the registries. To meet the first challenge, this paper presents the FAIRVASC ontology to manage data related to the rare disease anti-neutrophil cytoplasmic antibody (ANCA) associated vasculitis (AAV), which is based on the harmonisation of terms in seven European data registries. It has been built upon a set of key clinical questions developed by a team of experts in vasculitis selected from the registry sites and makes use of several standard classifications, such as Systematized Nomenclature of Medicine - Clinical Terms (SNOMED-CT) and Orphacode. It also presents the method for adding semantic meaning to AAV data across the registries using the declarative Relational to Resource Description Framework Mapping Language (R2RML). To meet the second challenge a federated querying approach is presented for accessing aggregated and pseudonymized data, and which supports analysis of AAV data in a manner which protects patient privacy. For additional security the federated querying approach is augmented with a method for auditing queries (and the uplift process) using the provenance ontology (PROV-O) to track when queries and changes occur and by whom. The main contribution of this work is the successful application of semantic web technologies and federated queries to provide a novel infrastructure that can readily incorporate additional registries, thus providing access to harmonised data relating to unprecedented numbers of patients with rare disease, while also meeting data privacy and security concerns.}},
author = {{McGlinn, Kris and Rutherford, Matthew and Gisslander, Karl and Hederman, Lucy and Little, Mark A. and O'Sullivan, Declan}},
issn = {{0010-4825}},
language = {{eng}},
month = {{03}},
publisher = {{Elsevier}},
series = {{Computers in Biology and Medicine}},
title = {{FAIRVASC: A semantic web approach to rare disease registry integration}},
url = {{http://dx.doi.org/10.1016/j.compbiomed.2022.105313}},
doi = {{10.1016/j.compbiomed.2022.105313}},
volume = {{145}},
year = {{2022}},
}