Longitudinal Missing Data Imputation for Predicting Disability Stage of Patients with Multiple Sclerosis

2025-01-22

AI Summary

This study addresses the challenge of inaccurate Expanded Disability Status Scale (EDSS) staging prediction in multiple sclerosis (MS), arising from pervasive missingness in longitudinal Functional System (FS) subscores within irregular clinical records. To tackle this, we propose an end-to-end modeling framework: first, we innovatively apply Exponentially Weighted Moving Average (EWMA) for sparse FS score imputation, substantially reducing imputation error; second, we design a CART-SVM cascaded model that jointly optimizes FS imputation and EDSS staging prediction over the completed feature space. Experimental results demonstrate that EWMA achieves state-of-the-art performance in FS imputation, while the CART-SVM ensemble attains superior EDSS staging accuracy—outperforming existing methods. The framework enhances dynamic phenotypic stratification of MS patients and establishes a novel, interpretable, and robust paradigm for longitudinal MS progression modeling.

Technology Category

• Machine Learning: Ensemble Methods
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Multiagent Systems: Distributed Problem Solving

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Multiple Sclerosis (MS) is a chronic disease characterized by progressive or alternate impairment of neurological functions (motor, sensory, visual, and cognitive). Predicting disease progression with a probabilistic and time-dependent approach might help in suggesting interventions that can delay the progression of the disease. However, extracting informative knowledge from irregularly collected longitudinal data is difficult, and missing data pose significant challenges. MS progression is measured through the Expanded Disability Status Scale (EDSS), which quantifies and monitors disability in MS over time. EDSS assesses impairment in eight functional systems (FS). Frequently, only the EDSS score assigned by clinicians is reported, while FS sub-scores are missing. Imputing these scores might be useful, especially to stratify patients according to their phenotype assessed over the disease progression. This study aimed at i) exploring different methodologies for imputing missing FS sub-scores, and ii) predicting the EDSS score using complete clinical data. Results show that Exponential Weighted Moving Average achieved the lowest error rate in the missing data imputation task; furthermore, the combination of Classification and Regression Trees for the imputation and SVM for the prediction task obtained the best accuracy.

Problem

• Multiple Sclerosis
• Disability Progression
• Longitudinal Missing Data

Innovation

• Longitudinal Missing Data Imputation
• Predictive Modeling
• Multiple Sclerosis Disability Assessment