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Ontology-Based Data Preparation in Healthcare: The Case of the AMD-STITCH Project

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Abstract

In the context of healthcare, an AI solution is generally developed for a specific analysis task, based on a relevant dataset, with little attention to reusability and generalizability of its data preparation step. This paper focuses on a different scenario, which can be called context-oriented, where a set of clinical data sources, relevant for a specific context (e.g., a particular disease), is available and can be used for a variety of data analytics tasks, often carried out by different research groups. Therefore, the aim of this research is to present a systematic method, which exploits the Ontology-based Data Management paradigm to enhance data preparation in a context-oriented scenario. The introduced methodology has been applied to a project dealing with big data and regarding the treatment of diabetes and its complications. The peculiarity and challenge of this project lies in the fact that it deals with real world data, extracted from Electronic Medical Records within a 13 years timeframe, and thus not collected for research purposes. The paper focuses on two main steps of data preparation, namely data modeling and data cleaning, and it shows how this approach provides effective techniques for setting up a unified and shared database, to be used in the subsequent data analytics phases as an asset.

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Data availability

The data that support the findings of this study are property of the AMD Foundation. Restrictions apply to the availability of these data.

Notes

  1. We clarify that not all the 320 centres constituting the AMD network included their data in the latest AMD annals.

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Acknowledgements

This work has been partially supported by MUR under the PRIN 2017 project “HOPE” (prot. 2017MMJJRE), by the EU under the H2020-EU.2.1.1 project TAILOR, grant id. 952215, and by the projects FAIR (PE0000013) and SERICS (PE00000014) under the MUR National Recovery and Resilience Plan funded by the European Union - NextGenerationEU. The authors would like to thank the Associazione Medici Diabetologi (AMD), Fondazione AMD and all the scientists involved in the STITCH-AMD initiative for supporting this work. This work would not have been possible without the precious efforts of Dr. Sebastiano Filetti and the expertise of Dr. Antonio Nicolucci and Dr. Giuseppe Lucisano (CORESEARCH S.r.l.) and all the patients who have been cared over the years in the AMD centers.

Funding

This article is funded by Ministero dell'Università e della Ricerca (2017MMJJRE, PE0000013, PE00000014), H2020 Industrial Leadership (952215).

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Author notes

  1. Federico Croce and Riccardo Valentini contributed equally to this work.

Authors and Affiliations

  1. Sapienza Information-Based Technology InnovaTion Center for Health (STITCH), Sapienza University of Rome, Rome, Italy

    Federico Croce, Riccardo Valentini, Marianna Maranghi, Giorgio Grani, Maurizio Lenzerini & Riccardo Rosati

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  1. Federico Croce
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  2. Riccardo Valentini
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  3. Marianna Maranghi
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Correspondence to Federico Croce.

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This article is part of the topical collection “Digital Healthcare and Wellbeing” guest edited by Achilleas Achilleos, George A. Papadopoulos, Edwige Pissaloux and Ramiro Velazquez.

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Croce, F., Valentini, R., Maranghi, M. et al. Ontology-Based Data Preparation in Healthcare: The Case of the AMD-STITCH Project. SN COMPUT. SCI. 5, 437 (2024). https://doi.org/10.1007/s42979-024-02757-w

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