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  1. Pubblicazioni

Deep learning methods to predict amyotrophic lateral sclerosis disease progression

Articolo
Data di Pubblicazione:
2022
Abstract:
Amyotrophic lateral sclerosis (ALS) is a highly complex and heterogeneous neurodegenerative disease that affects motor neurons. Since life expectancy is relatively low, it is essential to promptly understand the course of the disease to better target the patient’s treatment. Predictive models for disease progression are thus of great interest. One of the most extensive and well-studied open-access data resources for ALS is the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) repository. In 2015, the DREAM-Phil Bowen ALS Prediction Prize4Life Challenge was held on PRO-ACT data, where competitors were asked to develop machine learning algorithms to predict disease progression measured through the slope of the ALSFRS score between 3 and 12 months. However, although it has already been successfully applied in several studies on ALS patients, to the best of our knowledge deep learning approaches still remain unexplored on the ALSFRS slope prediction in PRO-ACT cohort. Here, we investigate how deep learning models perform in predicting ALS progression using the PRO-ACT data. We developed three models based on different architectures that showed comparable or better performance with respect to the state-of-the-art models, thus representing a valid alternative to predict ALS disease progression.
Tipologia CRIS:
03A-Articolo su Rivista
Keywords:
Disease Progression; Humans; Machine Learning; Amyotrophic Lateral Sclerosis; Deep Learning; Neurodegenerative Diseases
Elenco autori:
Pancotti C.; Birolo G.; Rollo C.; Sanavia T.; Di Camillo B.; Manera U.; Chio A.; Fariselli P.
Autori di Ateneo:
BIROLO Giovanni
CHIO' Adriano
FARISELLI Piero
MANERA Umberto
PANCOTTI CORRADO
ROLLO CESARE
SANAVIA Tiziana
Link alla scheda completa:
https://iris.unito.it/handle/2318/1874883
Link al Full Text:
https://iris.unito.it/retrieve/handle/2318/1874883/1038162/s41598-022-17805-9.pdf
Pubblicato in:
SCIENTIFIC REPORTS
Journal
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URL

https://www.nature.com/articles/s41598-022-17805-9
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