利用联邦学习方法加速帕金森病药物研发。
Accelerating Parkinson's Disease drug development with federated learning approaches.
作者信息
Khanna Amit, Adams Jamie, Antoniades Chrystalina, Bloem Bastiaan R, Carroll Camille, Cedarbaum Jesse, Cosman Joshua, Dexter David T, Dockendorf Marissa F, Edgerton Jeremy, Gaetano Laura, Goikoetxea Erkuden, Hill Derek, Horak Fay, Izmailova Elena S, Kangarloo Tairmae, Katabi Dina, Kopil Catherine, Lindemann Michael, Mammen Jennifer, Marek Kenneth, McFarthing Kevin, Mirelman Anat, Muller Martijn, Pagano Gennaro, Peterschmitt M Judith, Ren Jie, Rochester Lynn, Sardar Sakshi, Siderowf Andrew, Simuni Tanya, Stephenson Diane, Swanson-Fischer Christine, Wagner John A, Jones Graham B
机构信息
Neuroscience Development, Novartis AG, Basel, Switzerland.
Department of Neurology and Center for Health and Technology, University of Rochester, Rochester, NY, USA.
出版信息
NPJ Parkinsons Dis. 2024 Nov 21;10(1):225. doi: 10.1038/s41531-024-00837-5.
Parkinson's Disease is a progressive neurodegenerative disorder afflicting almost 12 million people. Increased understanding of its complex and heterogenous disease pathology, etiology and symptom manifestations has resulted in the need to design, capture and interrogate substantial clinical datasets. Herein we advocate how advances in the deployment of artificial intelligence models for Federated Data Analysis and Federated Learning can help spearhead coordinated and sustainable approaches to address this grand challenge.
帕金森病是一种影响近1200万人的进行性神经退行性疾病。对其复杂且异质性的疾病病理学、病因和症状表现的深入了解，使得有必要设计、收集和研究大量的临床数据集。在此，我们倡导在联邦数据分析和联邦学习中部署人工智能模型的进展如何有助于率先采取协调一致且可持续的方法来应对这一重大挑战。
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