Patient demographics in the PRO-ACT [14] dataset.

Amyotrophic lateral sclerosis (ALS) is a degenerative disorder of the motor neurons that causes progressive paralysis in patients. Current treatment options aim to prolong survival and improve quality of life. However, due to the heterogeneity of the disease, it is often difficult to determine the optimal time for potential therapies or medical interventions. In this study, we propose a novel method to predict the time until a patient with ALS experiences significant functional impairment (ALSFRS-R ≤ 2) for each of five common functions: speaking, swallowing, handwriting, walking, and breathing. We formulate this task as a multi-event survival problem and validate our approach in the PRO-ACT dataset () by training five covariate-based survival models to estimate the probability of each event over the 500 days following the baseline visit. We then predict five event-specific individual survival distributions (ISDs) for a patient, each providing an interpretable estimate of when that event is likely to occur. The results show that covariate-based models are superior to the Kaplan-Meier estimator at predicting time-to-event outcomes in the PRO-ACT dataset. Additionally, our method enables practitioners to make individual counterfactual predictions—where certain covariates can be changed—to estimate their effect on the predicted outcome. In this regard, we find that Riluzole has little or no impact on predicted functional decline. However, for patients with bulbar-onset ALS, our model predicts significantly shorter time-to-event estimates for loss of speech and swallowing function compared to patients with limb-onset ALS (log-rank p