论文标题
使用概率神经网络生成具有多发性硬化症的数字双胞胎
Generating Digital Twins with Multiple Sclerosis Using Probabilistic Neural Networks
论文作者
论文摘要
多发性硬化症（MS）是一种神经退行性疾病，其特征是一组复杂的临床评估。我们使用一个无监督的机器学习模型，称为条件受限的玻尔兹曼机器（CRBM）来学习通常用于表征受试者的协变量与MS临床试验中的疾病进展。 CRBM能够生成数字双胞胎，这些数字双胞胎是模拟具有与实际受试者相同基线数据的受试者。数字双胞胎允许对疾病进展的主题级统计分析。使用来自MS的三个主要亚型的临床试验的安慰剂组中的239名受试者的数据，对CRBM进行了训练。我们讨论了如何对CRBM进行训练，并表明该模型产生的数字双胞胎在统计学上与他们的实际受试者沿许多措施无法区分。
Multiple Sclerosis (MS) is a neurodegenerative disorder characterized by a complex set of clinical assessments. We use an unsupervised machine learning model called a Conditional Restricted Boltzmann Machine (CRBM) to learn the relationships between covariates commonly used to characterize subjects and their disease progression in MS clinical trials. A CRBM is capable of generating digital twins, which are simulated subjects having the same baseline data as actual subjects. Digital twins allow for subject-level statistical analyses of disease progression. The CRBM is trained using data from 2395 subjects enrolled in the placebo arms of clinical trials across the three primary subtypes of MS. We discuss how CRBMs are trained and show that digital twins generated by the model are statistically indistinguishable from their actual subject counterparts along a number of measures.
