Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease where substantial heterogeneity in clinical presentation urgently requires a better stratification of patients for the development of drug trials and clinical care. In this study we explored stratification through a crowdsourcing approach, the DREAM Prize4Life ALS Stratification Challenge. Using data from >10,000 patients from ALS clinical trials and 1479 patients from community-based patient registers, more than 30 teams developed new approaches for machine learning and clustering, outperforming the best current predictions of disease outcome. We propose a new method to integrate and analyze patient clusters across methods, showing a clear pattern of consistent and clinically relevant sub-groups of patients that also enabled the reliable classification of new patients. Our analyses reveal novel insights in ALS and describe for the first time the potential of a crowdsourcing to uncover hidden patient sub-populations, and to accelerate disease understanding and therapeutic development.

Kueffner, R., Zach, N., Bronfeld, M., Norel, R., Atassi, N., Balagurusamy, V., et al. (2019). Stratification of amyotrophic lateral sclerosis patients: A crowdsourcing approach. SCIENTIFIC REPORTS, 9(1), 1-14 [10.1038/s41598-018-36873-4].

Stratification of amyotrophic lateral sclerosis patients: A crowdsourcing approach

Chicco D.
Membro del Collaboration Group
;
2019

Abstract

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease where substantial heterogeneity in clinical presentation urgently requires a better stratification of patients for the development of drug trials and clinical care. In this study we explored stratification through a crowdsourcing approach, the DREAM Prize4Life ALS Stratification Challenge. Using data from >10,000 patients from ALS clinical trials and 1479 patients from community-based patient registers, more than 30 teams developed new approaches for machine learning and clustering, outperforming the best current predictions of disease outcome. We propose a new method to integrate and analyze patient clusters across methods, showing a clear pattern of consistent and clinically relevant sub-groups of patients that also enabled the reliable classification of new patients. Our analyses reveal novel insights in ALS and describe for the first time the potential of a crowdsourcing to uncover hidden patient sub-populations, and to accelerate disease understanding and therapeutic development.
Articolo in rivista - Articolo scientifico
Algorithms; Amyotrophic Lateral Sclerosis; Clinical Trials as Topic; Cluster Analysis; Crowdsourcing; Databases, Factual; Humans; Ireland; Italy; Machine Learning; Organizations, Nonprofit
English
24-gen-2019
2019
9
1
1
14
690
open
Kueffner, R., Zach, N., Bronfeld, M., Norel, R., Atassi, N., Balagurusamy, V., et al. (2019). Stratification of amyotrophic lateral sclerosis patients: A crowdsourcing approach. SCIENTIFIC REPORTS, 9(1), 1-14 [10.1038/s41598-018-36873-4].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/431139
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