Wang Xinkai, Shi Yonggang
Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California (USC), Los Angeles, CA 90033, USA.
Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California (USC), Los Angeles, CA 90089, USA.
Med Image Comput Comput Assist Interv. 2024 Oct;15003:46-55. doi: 10.1007/978-3-031-72384-1_5. Epub 2024 Oct 3.
Subtype and Stage Inference (SuStaIn) is a useful Event-based Model for capturing both the temporal and the phenotypical patterns for any progressive disorders, which is essential for understanding the heterogeneous nature of such diseases. However, this model cannot capture subtypes with different progression rates with respect to predefined biomarkers with fixed events prior to inference. Therefore, we propose an adaptive algorithm for learning subtype-specific events while making subtype and stage inference. We use simulation to demonstrate the improvement with respect to various performance metrics. Finally, we provide snapshots of different levels of biomarker abnormality within different subtypes on Alzheimer's Disease (AD) data to demonstrate the effectiveness of our algorithm.
亚型和阶段推断(SuStaIn)是一种有用的基于事件的模型,用于捕捉任何进行性疾病的时间和表型模式,这对于理解此类疾病的异质性至关重要。然而,该模型在推断之前无法根据具有固定事件的预定义生物标志物捕捉具有不同进展率的亚型。因此,我们提出了一种自适应算法,用于在进行亚型和阶段推断的同时学习亚型特异性事件。我们使用模拟来证明在各种性能指标方面的改进。最后,我们在阿尔茨海默病(AD)数据上提供了不同亚型内不同水平生物标志物异常的快照,以证明我们算法的有效性。