An extensible and unifying approach to retrospective clinical data modeling: the BrainTeaser Ontology.

Journal: Journal of biomedical semantics
PMID: 39210467

Abstract

Automatic disease progression prediction models require large amounts of training data, which are seldom available, especially when it comes to rare diseases. A possible solution is to integrate data from different medical centres. Nevertheless, various centres often follow diverse data collection procedures and assign different semantics to collected data. Ontologies, used as schemas for interoperable knowledge bases, represent a state-of-the-art solution to homologate the semantics and foster data integration from various sources. This work presents the BrainTeaser Ontology (BTO), an ontology that models the clinical data associated with two brain-related rare diseases (ALS and MS) in a comprehensive and modular manner. BTO assists in organizing and standardizing the data collected during patient follow-up. It was created by harmonizing schemas currently used by multiple medical centers into a common ontology, following a bottom-up approach. As a result, BTO effectively addresses the practical data collection needs of various real-world situations and promotes data portability and interoperability. BTO captures various clinical occurrences, such as disease onset, symptoms, diagnostic and therapeutic procedures, and relapses, using an event-based approach. Developed in collaboration with medical partners and domain experts, BTO offers a holistic view of ALS and MS for supporting the representation of retrospective and prospective data. Furthermore, BTO adheres to Open Science and FAIR (Findable, Accessible, Interoperable, and Reusable) principles, making it a reliable framework for developing predictive tools to aid in medical decision-making and patient care. Although BTO is designed for ALS and MS, its modular structure makes it easily extendable to other brain-related diseases, showcasing its potential for broader applicability.Database URL  https://zenodo.org/records/7886998 .

Authors

  • Guglielmo Faggioli
    Department of Information Engineering, University of Padova, Padova, Italy. guglielmo.faggioli@unipd.it.
  • Laura Menotti
    Department of Information Engineering, University of Padova, Padova, Italy. laura.menotti@unipd.it.
  • Stefano Marchesin
    Department of Information Engineering, University of Padova, Padova, Italy. stefano.marchesin@unipd.it.
  • Adriano Chiò
    b ALS Center, 'Rita Levi Montalcini' Department of Neuroscience , University of Torino , Torino , Italy.
  • Arianna Dagliati
    1 Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.
  • Mamede de Carvalho
    Faculdade de Medicina, Instituto de Medicina Molecular, Universidade de Lisboa, Lisbon, Portugal.
  • Marta Gromicho
    Faculdade de Medicina, Instituto de Medicina Molecular, Universidade de Lisboa, Lisbon, Portugal.
  • Umberto Manera
    ALS Center, "Rita Levi Montalcini" Department of Neuroscience, University of Turin, 10126, Turin, Italy.
  • Eleonora Tavazzi
    IRCCS Foundation C. Mondino in Pavia, Pavia, Italy.
  • Giorgio Maria Di Nunzio
    Department of Information Engineering, University of Padova, Padova, Italy.
  • Gianmaria Silvello
    Department of Information Engineering, University of Padua, Padua, Italy. gianmaria.silvello@unipd.it.
  • Nicola Ferro
    Department of Information Engineering, University of Padova, Padova, Italy.

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