Accurate personalized survival prediction for amyotrophic lateral sclerosis patients.

ABSTRACT: Amyotrophic Lateral Sclerosis (ALS) is a rapidly progressive neurodegenerative disease. Accurately predicting the survival time for ALS patients can help patients and clinicians to plan for future treatment and care. We describe the application of a machine-learned tool that incorporates clinical features and cortical thickness from brain magnetic resonance (MR) images to estimate the time until a composite respiratory failure event for ALS patients, and presents the prediction as individual survival distributions (ISDs). These ISDs provide the probability of survival (none of the respiratory failures) at multiple future time points, for each individual patient. Our learner considers several survival prediction models, and selects the best model to provide predictions. We evaluate our lear

SUBMITTER: Kuan LH 

PROVIDER: S-EPMC10673879 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature