ArticleInterspeech2023
Combining Multiple Multimodal Speech Features into an Interpretable Index Score for Capturing Disease Progression in Amyotrophic Lateral Sclerosis.
Article in Interspeech, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
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Authors and funding
3 authors.
Funding
Abstract
Multiple speech biomarkers have been shown to carry useful information regarding Amyotrophic Lateral Sclerosis (ALS) pathology. We propose a two-step framework to compute optimal linear combinations (indexes) of these biomarkers that are more discriminative and noise-robust than the individual markers, which is important for clinical care and pharmaceutical trial applications. First, we use a hierarchical clustering based method to select representative speech metrics from a dataset comprising 143 people with ALS and 135 age- and sex-matched healthy controls. Second, we analyze three methods of index computation that optimize linear discriminability, Youden Index, and sparsity of logistic regression model weights, respectively, and evaluate their performance with 5-fold cross validation. We find that the proposed indexes are generally more discriminative of bulbar vs non-bulbar onset in ALS than their individual component metrics as well as an equally-weighted baseline.
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Registered trials
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