The BRAINTEASER Datasets: Clinical, Wearable and Environmental Data for ALS & MS Progression Modeling

Amyotrophic lateral sclerosis (ALS) and multiple sclerosis (MS) are debilitating diseases with unpredictable progression. Artificial Intelligence-based tools for modelling disease progression could significantly improve the quality of life for patients and caregivers while supporting clinicians in delivering more personalized and timely care. However, the limited availability of data hinders the development, testing, and reproducibility of such predictive tools. To address this challenge, we curated, in the context of the H2020 BRAINTEASER project, four datasets containing clinical data from a total of 2,290 ALS patients and 723 MS patients. These datasets also include environmental data and information collected through wearable devices. Unlike most existing resources, the BRAINTEASER datasets are gathered from clinical practice, offering a more accurate representation of the data that an AI progression prediction tool would encounter in real-world scenarios. In addition to manual and automated data quality checks, the research community has validated the datasets through three editions of the intelligent Disease Progression Prediction challenges held within the Conference and Labs of the Evaluation Forum (CLEF).

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Publication Details

Journal
Scientific Data
Published
2025-11-21
DOI
https://doi.org/10.1038/s41597-025-06095-1
Citations
1
Primary Topic
Amyotrophic Lateral Sclerosis Research
Type
article
Field-Weighted Citation Impact
1.39

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The BRAINTEASER Datasets: Clinical, Wearable and Environmental Data for ALS & MS Progression Modeling

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article

The BRAINTEASER Datasets: Clinical, Wearable and Environmental Data for ALS & MS Progression Modeling

Stefano Marchesin, Aleksandar Jovanović, J.L. Muñoz Blanco, Roberto Bergamaschi, Enrico Longato, Eleonora Tavazzi, Vladimir Urošević, Martina Vettoretti, María Fernanda Cabrera-Umpiérrez, Anca Loredana Alungulese, Sergio González-Martínez, Erica Tavazzi, Mamede de Carvalho, Isotta Trescato, Riccardo Bellazzi, Marta Gromicho, Giorgio Maria Di Nunzio, Umberto Manera, Lara Ahmad, Piero Fariselli, Pietro Bosoni, Alessandro Guazzo, Paola Cavalla, Helena Aidos, Arianna Dagliati, Nicola Ferro, Barbara Di Camillo, Gianmaria Silvello, Guglielmo Faggioli, E. J. Iglesias, José Manuel García‐Domínguez, Sara C. Madeira, Giovanni Birolo, Adriano Chiò, Borko Kostić, Laura Menotti
article en
1 citations

Abstract

Amyotrophic lateral sclerosis (ALS) and multiple sclerosis (MS) are debilitating diseases with unpredictable progression. Artificial Intelligence-based tools for modelling disease progression could significantly improve the quality of life for patients and caregivers while supporting clinicians in delivering more personalized and timely care. However, the limited availability of data hinders the development, testing, and reproducibility of such predictive tools. To address this challenge, we curated, in the context of the H2020 BRAINTEASER project, four datasets containing clinical data from a total of 2,290 ALS patients and 723 MS patients. These datasets also include environmental data and information collected through wearable devices. Unlike most existing resources, the BRAINTEASER datasets are gathered from clinical practice, offering a more accurate representation of the data that an AI progression prediction tool would encounter in real-world scenarios. In addition to manual and automated data quality checks, the research community has validated the datasets through three editions of the intelligent Disease Progression Prediction challenges held within the Conference and Labs of the Evaluation Forum (CLEF).

Scientific DataVol. 12(1)
University of Padua (IT), University of Lisbon (PT), University of Pavia (IT), Hospital General Universitario Gregorio Marañón (ES), Azienda Ospedaliera Citta' della Salute e della Scienza di Torino (IT), Belit (Serbia) (RS), Institute of Cognitive Sciences and Technologies (IT), University of Turin (IT), Universidad Politécnica de Madrid (ES)
European Commission
Openalex Percentile: Top 14%
Amyotrophic Lateral Sclerosis Research
1.39
DOIOpen Access PDFOpenAlex
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