Optimized questionnaire item selection for tracking the progression of motor symptoms in Parkinson's disease

2026-04-13

AI Summary

This study addresses the low clinical adoption of the full-length MDS-UPDRS questionnaire due to its excessive burden. To mitigate this, the authors propose three item selection strategies grounded in Item Response Theory (IRT)—Fisher information ranking, coordinate descent local search, and adaptive selection—aimed at drastically reducing the number of items while minimizing uncertainty in estimating disease severity. Experimental results demonstrate that, when restricted to only five items, these methods reduce the standard deviation of severity estimates by 14%, 26%, and 34%, respectively, compared to random selection. This substantial improvement in estimation precision for ultra-short forms overcomes the limitations of conventional random or heuristic item-selection approaches, offering a more efficient and accurate assessment tool for clinical use.

Technology Category

• Machine Learning: Dimensionality Reduction/Feature Selection
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Long questionnaires increase the response burden for patients and healthcare workers. In the treatment of Parkinson's disease, the MDS-UPDRS questionnaire to track disease progression may be underutilized due to time requirements. While reduced item sets have been studied using Fisher information from Item Response Theory (IRT) models, optimal selection methods remain unclear. We compared three methods for selecting an optimal subset of items, with the aim of minimizing the uncertainty in the estimates of the disease severity: Ranking by the Fisher information, coordinate descent local search to directly minimize estimate uncertainty, and adaptive selection. Whereas item ranking based on the expected Fisher information outperformed random choice of items, we saw further gains with the coordinate descent algorithm that directly minimizes the uncertainty of the disease severity estimate. An adaptive algorithm gave an additional slight gain compared to the coordinate descent method. However, the performance of the adaptive method is a best-case limit as we assume that we find the optimal set for the true latent trait scores. For a 5-item subset, the ranked Fisher information method reduced the expected standard deviation by 14 percent compared to random item selection. The corresponding reductions for coordinate descent and adaptive selection were 26 percent and 34 percent respectively. More sophisticated selection methods substantially improved estimate accuracy for small item sets, with diminishing returns for larger subsets. Because item parameters are retained from the full test, reduced item sets measure the same latent construct as the original test. The choice of method entails a trade-off between methodological complexity and precision.

Problem

• Parkinson's disease
• MDS-UPDRS
• questionnaire item selection
• disease progression tracking
• response burden

Innovation

• item selection optimization
• Parkinson's disease progression
• Item Response Theory
• coordinate descent
• adaptive questionnaire design