Temporal Modeling of Deterioration Patterns and Clustering for Disease Prediction of ALS Patients

Abstract

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease, lasting from the day of onset until death. Factors such as the progression rate and pattern of the disease vary greatly among patients, making it difficult to achieve accurate predictions about ALS. To accurately predict ALS disease state and deterioration, we propose a novel approach that combines: a) sequence clustering based on dynamic time warping for separation among patients with diverse ALS deterioration patterns, b) sequential pattern mining for discovery of deterioration changes that patients of the same type may have in common, and c) deterioration-based patient next-state prediction. Using a clinical dataset, we demonstrate the advantage of the proposed approach in terms of classification accuracy and deterioration detection compared to other classification methods and temporal models such as long short-term memory.