{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/automatic-prediction-of-amyotrophic-lateral","title":"Automatic Prediction of Amyotrophic Lateral Sclerosis Progression using Longitudinal Speech Transformer","arxiv_id":"2406.18625","date":"2024-06-26","proceeding":null,"authors":["Liming Wang","Yuan Gong","Nauman Dawalatabad","Marco Vilela","Katerina Placek","Brian Tracey","Yishu Gong","Alan Premasiri","Fernando Vieira","James Glass"],"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.","url_abs":"https://arxiv.org/abs/2406.18625v1","url_pdf":"https://arxiv.org/pdf/2406.18625v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"automatic-prediction-of-amyotrophic-lateral","repo_url":"https://github.com/cactuswiththoughts/alst","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[{"method_slug":"als","method_name":"ALS"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}