%0 Journal Article
%A Weinreich, Marcel
%A McDonough, Harry
%A Heverin, Mark
%A Domhnaill, Éanna Mac
%A Yacovzada, Nancy
%A Magen, Iddo
%A Cohen, Yahel
%A Harvey, Calum
%A Elazzab, Ahmed
%A Gornall, Sarah
%A Boddy, Sarah
%A Alix, James J P
%A Kurz, Julian M
%A Kenna, Kevin P
%A Zhang, Sai
%A Iacoangeli, Alfredo
%A Al-Khleifat, Ahmad
%A Snyder, Michael P
%A Hobson, Esther
%A Chio, Adriano
%A Malaspina, Andrea
%A Hermann, Andreas
%A Ingre, Caroline
%A Costa, Juan Vazquez
%A van den Berg, Leonard
%A Panadés, Monica Povedano
%A van Damme, Philip
%A Corcia, Phillipe
%A de Carvalho, Mamede
%A Al-Chalabi, Ammar
%A Hornstein, Eran
%A Elhaik, Eran
%A Shaw, Pamela J
%A Hardiman, Orla
%A McDermott, Christopher
%A Cooper-Knock, Johnathan
%T Optimised machine learning for time-to-event prediction in healthcare applied to timing of gastrostomy in ALS: a multi-centre, retrospective model development and validation study.
%J EBioMedicine
%V 121
%@ 2352-3964
%C Amsterdam [u.a.]
%I Elsevier
%M DZNE-2025-01271
%P 105962
%D 2025
%X Amyotrophic lateral sclerosis (ALS) is invariably fatal but there are large variations in the rate of progression. The lack of predictability can make it difficult to plan clinical interventions. This includes the requirement for gastrostomy where early or late placement can adversely impact quality of life and survival.We designed a model to predict the timing of gastrostomy requirement in ALS as indicated by 5
%K Humans
%K Gastrostomy: methods
%K Amyotrophic Lateral Sclerosis: diagnosis
%K Amyotrophic Lateral Sclerosis: surgery
%K Amyotrophic Lateral Sclerosis: therapy
%K Machine Learning
%K Male
%K Female
%K Retrospective Studies
%K Middle Aged
%K Aged
%K Europe
%K Bayes Theorem
%K Amyotrophic lateral sclerosis (ALS) (Other)
%K Gastrostomy (Other)
%K Machine learning (Other)
%K Personalised medicine (Other)
%K Time-to-event prediction (Other)
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:41075354
%2 pmc:PMC12547709
%R 10.1016/j.ebiom.2025.105962
%U https://pub.dzne.de/record/282301