Digital Twin Generators for Disease Modeling
This work addresses the challenge of personalized medicine by enabling scalable generation of digital twins for more efficient clinical trials and treatment recommendations, though it is incremental as it builds on existing machine learning approaches.
The authors tackled the problem of creating computational models of individual patients' health evolution, known as digital twins, by developing a neural network architecture called Digital Twin Generators (DTGs) that can generate accurate digital twins for patients across 13 different indications.
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.