Automatic Prediction of Amyotrophic Lateral Sclerosis Progression using Longitudinal Speech Transformer

• URL: http://arxiv.org/abs/2406.18625v1
• Date: Wed, 26 Jun 2024 13:28:24 GMT
• Title: Automatic Prediction of Amyotrophic Lateral Sclerosis Progression using Longitudinal Speech Transformer
• Authors: Liming Wang, Yuan Gong, Nauman Dawalatabad, Marco Vilela, Katerina Placek, Brian Tracey, Yishu Gong, Alan Premasiri, Fernando Vieira, James Glass,
• Abstract summary: We propose ALS longitudinal speech transformer (ALST), a neural network-based automatic predictor of ALS disease progression. By taking advantage of high-quality pretrained speech features and longitudinal information in the recordings, our best model achieves 91.0% AUC. ALST is capable of fine-grained and interpretable predictions of ALS progression, especially for distinguishing between rarer and more severe cases.
• Score: 56.17737749551133
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automatic prediction of amyotrophic lateral sclerosis (ALS) disease progression provides a more efficient and objective alternative than manual approaches. We propose ALS longitudinal speech transformer (ALST), a neural network-based automatic predictor of ALS disease progression from longitudinal speech recordings of ALS patients. By taking advantage of high-quality pretrained speech features and longitudinal information in the recordings, our best model achieves 91.0\% AUC, improving upon the previous best model by 5.6\% relative on the ALS TDI dataset. Careful analysis reveals that ALST is capable of fine-grained and interpretable predictions of ALS progression, especially for distinguishing between rarer and more severe cases. Code is publicly available.

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