# Predicting assisted ventilation in Amyotrophic Lateral Sclerosis using a mixture of experts and conformal predictors

## TL;DR

This paper introduces a prognostic model combining conformal prediction and mixture of experts to accurately forecast respiratory failure and its timing in ALS patients, providing confidence measures for clinical decision-making.

## Contribution

The study develops a novel model integrating conformal prediction with mixture of experts to improve ALS progression forecasting with reliability estimates.

## Key findings

- Approximately 80% of predictions were correct.
- The model provides both risk assessment and timing of respiratory failure.
- Confidence measures enhance clinical applicability.

## Abstract

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.