Predicting amyotrophic lateral sclerosis mortality with machine learning in diverse patient databases

Guo, LingXu, Ian QianNag, SonakshiXu, JingHuei Chai , Josiah YuiSimmons, ZacharyRamasamy, SavithaYeo, Crystal2025-09-152025-10Guo, L, Xu, I Q, Nag, S, Xu, J, Huei Chai , J Y, Simmons, Z, Ramasamy, S & Yeo, C 2025, 'Predicting amyotrophic lateral sclerosis mortality with machine learning in diverse patient databases', Muscle & nerve, vol. 72, no. 4, pp. 653-661. https://doi.org/10.1002/mus.284870148-639Xhttps://hdl.handle.net/2164/26013Open Access via the Wiley agreement We would like to thank Dr. James Berry, Massachusetts General Hospital, and the NEALS consortium for providing the Celecoxib trial database and all contributors to the PRO-ACT database. We would also like to thank all the patients who contributed to the PRO-ACT, Celecoxib, and SG ALS databases.9562934engamyotrophic lateral sclerosisdiverse external validationmachine learningmortality predictionsurvival analysisR Medicine (General)Supplementary DataDASLinkR1Predicting amyotrophic lateral sclerosis mortality with machine learning in diverse patient databasesJournal article10.1002/mus.28487724