Deep learning methods to predict amyotrophic lateral sclerosis disease progression

Abstract

Amyotrophic lateral sclerosis (ALS) is a highly complex and heterogeneous neurodegenerative disease that affects motor neurons. Since life expectancy is relatively low, it is essential to promptly understand the course of the disease to better target the patient's treatment. Predictive models for disease progression are thus of great interest. One of the most extensive and well-studied open-access data resources for ALS is the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) repository. In 2015, the DREAM-Phil Bowen ALS Prediction Prize4Life Challenge was held on PRO-ACT data, where competitors were asked to develop machine learning algorithms to predict disease progression measured through the slope of the ALSFRS score between 3 and 12 months. However, although it has already been successfully applied in several studies on ALS patients, to the best of our knowledge deep learning approaches still remain unexplored on the ALSFRS slope prediction in PRO-ACT cohort. Here, we investigate how deep learning models perform in predicting ALS progression using the PRO-ACT data. We developed three models based on different architectures that showed comparable or better performance with respect to the state-of-the-art models, thus representing a valid alternative to predict ALS disease progression.

Figure 1

(a) Convolutional neural network architecture. The dynamic features of the questionnaire dataset flows into the convolutional module constituted by two layers; after the concatenation with the static features, the information is fed into a feed-forward neural network to predict the two outcomes. (b) Recurrent Neural Network architecture. The structure is the same as the convolutional architecture except for the Recurrent module that processes the dynamic features.

Figure 2

Distribution of the 3–12 months ALSFRS slope distribution and fast versus medium-slow progressors.

Figure 3

Feature importance. Top 20 most important features ranked by Random Forest via cross-validation on the training set, using the Gini criterion.

Figure 4

Shapley values for the FFNN architecture; x-axis: the impact on the model output, y-axis: the top 20 most predictive features. The colormap represents the feature values.

Figure 5

Slope and survival. Left: Scatterplot of the experimental slope between months 3 and 12 against the time-to-death of 458 patients. Right: Kaplan–Meier curves for fast and medium-slow progressing patients in the test set. Fast and medium-slow progressors were 21 and 105, respectively. Times start from month 12 after the first visit.