Exploring Visual Complaints through a test battery in Acquired Brain Injury Patients: A Detailed Analysis of the DiaNAH Dataset

TL;DR

The paper tackles how ABI patients' self-reported visual complaints relate to objective visual perceptual tests, leveraging the DiaNAH dataset and an AutoML-based imputation pipeline to address missing data. It constructs singular and clustered complaint profiles from CVS responses and assesses their correlation with test outcomes, reporting minimal linear associations. The AutoML imputation preserves distributional properties and reveals that only a small fraction of the theoretical space of complaint combinations is represented, underscoring data sparsity ($0.07\%$ of $40{,}320$ possible groups). The study provides a dataset-ready foundation for exploring nonlinear relationships and phenotype-driven visual rehabilitation strategies, while highlighting the need for larger samples to draw robust clinical conclusions.

Abstract

This study investigated visual impairment complaints in a sample of 948 Acquired Brain Injury (ABI) patients using the DiaNAH dataset, emphasizing advanced machine learning techniques for managing missing data. Patients completed a CVS questionnaire capturing eight types of visual symptoms, including blurred vision and altered contrast perception. Due to incomplete data, 181 patients were excluded, resulting in an analytical subset of 767 individuals. To address the challenge of missing data, an automated machine learning (AutoML) approach was employed for data imputation, preserving the distributional characteristics of the original dataset. Patients were grouped according to singular and combined complaint clusters derived from the 40,320 potential combinations identified through the CVS questionnaire. A linear correlation analysis revealed minimal to no direct relationship between patient-reported visual complaints and standard visual perceptual function tests. This study represents an initial systematic attempt to understand the complex relationship between subjective visual complaints and objective visual perceptual assessments in ABI patients. Given the limitations of sample size and variability, further studies with larger populations are recommended to robustly explore these complaint clusters and their implications for visual perception following brain injury.

Figures (13)

• Figure 1: Visualization of the Correlations Between the Complaints and Visual Perceptual Functions.
• Figure 2: Visualization of the Correlations Between the Complaints and Visual Perceptual Functions of the Filtered Features.
• Figure 3: Overview of the Missing Values Within the DiaNAH Dataset.
• Figure 4: Overview of Correlations Between Test Outcome Measures Based on Missing Data.
• Figure 5: Histogram of Frequency per Clusters of Complaints.