Evidence map›Paper›PMID 39210467›Full record

ArticleJournal of biomedical semantics2024

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

Guglielmo Faggioli, Laura Menotti, Stefano Marchesin, Adriano Chió, Arianna Dagliati, Mamede de Carvalho, Marta Gromicho, Umberto Manera, Eleonora Tavazzi, Giorgio Maria Di Nunzio and 2 more

Abstract read
In one paragraph

Article in Journal of biomedical semantics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 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 ChióRita Levi Montalcini Department of Neuroscience, University of Turin, Turin, Italy.
Arianna DagliatiDepartment of Industrial and Information Engineering, University of Pavia, Pavia, Italy.
Mamede de CarvalhoFaculdade de Medicina, Instituto de Medicina Molecular, Universidade de Lisboa, Lisbon, Portugal.
Marta GromichoFaculdade de Medicina, Instituto de Medicina Molecular, Universidade de Lisboa, Lisbon, Portugal.
Umberto ManeraRita Levi Montalcini Department of Neuroscience, University of Turin, Turin, Italy.
Eleonora TavazziIRCCS Foundation C. Mondino in Pavia, Pavia, Italy.
Giorgio Maria Di NunzioDepartment of Information Engineering, University of Padova, Padova, Italy.
Gianmaria SilvelloDepartment of Information Engineering, University of Padova, Padova, Italy.
Nicola FerroDepartment of Information Engineering, University of Padova, Padova, Italy.

Funding

Horizon 2020 Framework Programme 101017598
6 · The paper itself

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 .

Indexed as

Biological OntologiesAmyotrophic Lateral SclerosisHumansMultiple SclerosisRetrospective StudiesSemanticsAmyotrophic lateral sclerosisClinical data modelingMultiple sclerosisOntology

Identifiers

PMID39210467
PMCPMC11363415

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.