A patient's digital twin is a computational model that describes the evolution of their health over time. Digital twins have the potential to revolutionize medicine by enabling individual-level computer simulations of human health, which can be used to conduct more efficient clinical trials or to recommend personalized treatment options. Due to the overwhelming complexity of human biology, machine learning approaches that leverage large datasets of historical patients' longitudinal health records to generate patients' digital twins are more tractable than potential mechanistic models. In this manuscript, we describe a neural network architecture that can learn conditional generative models of clinical trajectories, which we call Digital Twin Generators (DTGs), that can create digital twins of individual patients. We show that the same neural network architecture can be trained to generate accurate digital twins for patients across 13 different indications simply by changing the training set and tuning hyperparameters. By introducing a general purpose architecture, we aim to unlock the ability to scale machine learning approaches to larger datasets and across more indications so that a digital twin could be created for any patient in the world.

翻译：患者的数字孪生是一种计算模型，用于描述其健康状况随时间的变化。数字孪生有潜力通过实现基于个体的计算机模拟人类健康来革新医学，可用于开展更高效的临床试验或推荐个性化治疗方案。由于人类生物学的极端复杂性，利用历史患者纵向健康记录的大规模数据集来生成数字孪生的机器学习方法，比潜在的机理模型更具可行性。在本手稿中，我们描述了一种神经网络架构，该架构能够学习临床轨迹的条件生成模型，我们称之为数字孪生生成器（DTG），可为个体患者创建数字孪生。我们证明，仅通过改变训练集和调整超参数，即可使用相同的神经网络架构为13种不同适应症的患者生成准确的数字孪生。通过引入通用架构，我们旨在解锁将机器学习方法扩展到更大数据集和更多适应症的能力，从而为世界上任何患者创建数字孪生。