  Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease
characterized by a rapid motor decline, leading to respiratory failure and
subsequently to death. In this context, researchers have sought for models to
automatically predict disease progression to assisted ventilation in ALS
patients. However, the clinical translation of such models is limited by the
lack of insight 1) on the risk of error for predictions at patient-level, and
2) on the most adequate time to administer the non-invasive ventilation. To
address these issues, we combine Conformal Prediction (a machine learning
framework that complements predictions with confidence measures) and a mixture
experts into a prognostic model which not only predicts whether an ALS patient
will suffer from respiratory insufficiency but also the most likely time window
of occurrence, at a given reliability level. Promising results were obtained,
with near 80% of predictions being correctly identified.
