Integrating administrative health data and machine learning to predict ALS onset

Machine Learning models, particularly stacked approaches, show promising results in predicting ALS diagnoses using administrative health data. Continued research is necessary to improve detection strategies and support their integration into healthcare systems.

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Publication Details

Journal
Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration
Published
2025-12-05
DOI
https://doi.org/10.1080/21678421.2025.2596691
Primary Topic
Amyotrophic Lateral Sclerosis Research
Type
article
Field-Weighted Citation Impact
0.00
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article

Integrating administrative health data and machine learning to predict ALS onset

Toni Mora, David Roche, Mónica Povedano, Alejandro Caravaca Puchades et al.
Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration
Amyotrophic Lateral Sclerosis Research
article

Integrating administrative health data and machine learning to predict ALS onset

Toni Mora, David Roche, Mónica Povedano, Alejandro Caravaca Puchades, Pol Andrés Benito
article en

Abstract

Machine Learning models, particularly stacked approaches, show promising results in predicting ALS diagnoses using administrative health data. Continued research is necessary to improve detection strategies and support their integration into healthcare systems.

Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration
Bellvitge University Hospital (ES), Universitat Internacional de Catalunya (ES), Institut d'Investigació Biomédica de Bellvitge (ES)
Openalex Percentile: Top 47%
Amyotrophic Lateral Sclerosis Research
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Integrating administrative health data and machine learning to predict ALS onset — Toni Mora, David Roche, et al. · Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration (2025) | TGRS Research Map | TGRS