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BMC Medical Informatics and Decision Making
Published in: BMC Medical Informatics and Decision Making 1/2023

Open Access 01-12-2023 | Alzheimer's Disease | Research article

An ontology-based approach for modelling and querying Alzheimer’s disease data

Authors: Francesco Taglino, Fabio Cumbo, Giulia Antognoli, Ivan Arisi, Mara D’Onofrio, Federico Perazzoni, Roger Voyat, Giulia Fiscon, Federica Conte, Marco Canevelli, Giuseppe Bruno, Patrizia Mecocci, Paola Bertolazzi, for the Alzheimer’s Disease Neuroimaging Initiative

Published in: BMC Medical Informatics and Decision Making | Issue 1/2023

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Abstract

Background

The recent advances in biotechnology and computer science have led to an ever-increasing availability of public biomedical data distributed in large databases worldwide. However, these data collections are far from being “standardized” so to be harmonized or even integrated, making it impossible to fully exploit the latest machine learning technologies for the analysis of data themselves. Hence, facing this huge flow of biomedical data is a challenging task for researchers and clinicians due to their complexity and high heterogeneity. This is the case of neurodegenerative diseases and the Alzheimer’s Disease (AD) in whose context specialized data collections such as the one by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) are maintained.

Methods

Ontologies are controlled vocabularies that allow the semantics of data and their relationships in a given domain to be represented. They are often exploited to aid knowledge and data management in healthcare research. Computational Ontologies are the result of the combination of data management systems and traditional ontologies. Our approach is i) to define a computational ontology representing a logic-based formal conceptual model of the ADNI data collection and ii) to provide a means for populating the ontology with the actual data in the Alzheimer Disease Neuroimaging Initiative (ADNI). These two components make it possible to semantically query the ADNI database in order to support data extraction in a more intuitive manner.

Results

We developed: i) a detailed computational ontology for clinical multimodal datasets from the ADNI repository in order to simplify the access to these data; ii) a means for populating this ontology with the actual ADNI data. Such computational ontology immediately makes it possible to facilitate complex queries to the ADNI files, obtaining new diagnostic knowledge about Alzheimer’s disease.

Conclusions

The proposed ontology will improve the access to the ADNI dataset, allowing queries to extract multivariate datasets to perform multidimensional and longitudinal statistical analyses. Moreover, the proposed ontology can be a candidate for supporting the design and implementation of new information systems for the collection and management of AD data and metadata, and for being a reference point for harmonizing or integrating data residing in different sources.
Appendix
Available only for authorised users
Footnotes
1
Searching on Google Scholar with "Alzheimer’s Disease Neuroimaging Initiative" the number of retrieved citations is about 100K.
 
3
Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (https://​adni.​loni.​usc.​edu).
 
4
According to the UML notation, an arrow representing an association can be enriched with a label and cardinality constraints. For instance, the cardinality constraints attached to the undergoes relation says that one subject can be linked to zero or more events
 
5
Note that, in UML more sophisticated constraints can be modeled by using the Object Constraint Language (OCL)
 
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Metadata
Title
An ontology-based approach for modelling and querying Alzheimer’s disease data
Authors
Francesco Taglino
Fabio Cumbo
Giulia Antognoli
Ivan Arisi
Mara D’Onofrio
Federico Perazzoni
Roger Voyat
Giulia Fiscon
Federica Conte
Marco Canevelli
Giuseppe Bruno
Patrizia Mecocci
Paola Bertolazzi
for the Alzheimer’s Disease Neuroimaging Initiative
Publication date
01-12-2023
Publisher
BioMed Central
Published in
BMC Medical Informatics and Decision Making / Issue 1/2023
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/s12911-023-02211-6

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