Work

Improving Features for Multiple Sclerosis Disability Progression Prediction Through Temporal Alignment of Hospital Visits

Year: 2024

Type: conference-paper

Abstract: Predicting the disability progression in multiple sclerosis remains a significant challenge. Statistical features extracted from evoked potential signals are often discussed in literature as interesting biomarkers, yet their practical adoption remains limited due to their low effectiveness. Conversely, the use of deep neural network feature encoders, which allow for extracting more expressive biomarkers, is hindered by the limited availability of data. This study proposes a novel method of enhancing statistically extracted features by aligning hospital visits to embed the progression of the disease within the features. The results demonstrate a significant improvement in predicting disability progression using the statistically extracted features. Insight is provided through a study of correlation and importance of the enhanced features.

Source: International Conference on Machine Learning and Applications

DOI: 10.1109/icmla61862.2024.00171

Authors: Karel Fonteyn, Tom Dhaene, Dirk Deschrijver

Institutions: Ghent University, IMEC

Language: English

Topic: Multiple Sclerosis Research Studies

Subfield: Pathology and Forensic Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funder: Vlaamse regering