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.

患者的数字孪生是描述其健康状况随时间演变的计算模型。我们描述了一种可以学习临床轨迹条件生成模型的神经网络架构，称为数字孪生生成器（DTGs），通过改变训练集并调整超参数，该架构可以为 13 种不同症状的患者生成准确的数字孪生。通过引入通用的架构，我们旨在解锁机器学习方法在更大的数据集和更多症状上的可扩展性，以便可以为世界上的任何患者创建数字孪生。