Abstract
Background: Machine learning methods have been developed to predict the likelihood of a given event or classify patients into two or more diagnostic categories. Digital twin models, which forecast entire trajectories of patient health data, have potential applications in clinical trials and patient management. Methods: In this study, we apply a digital twin model based on a variational autoencoder to a population of patients who went on to experience an ischemic stroke. The digital twin’s ability to model patient clinical features was assessed with regard to its ability to forecast clinical measurement trajectories leading up to the onset of the acute medical event and beyond using International Classification of Diseases (ICD) codes for ischemic stroke and lab values as inputs. Results: The simulated patient trajectories were virtually indistinguishable from real patient data, with similar feature means, standard deviations, inter-feature correlations, and covariance structures on a withheld test set. A logistic regression adversary model was unable to distinguish between the real and simulated data area under the receiver operating characteristic (ROC) curve (AUCadversary = 0.51). Conclusion: Through accurate projection of patient trajectories, this model may help inform clinical decision making or provide virtual control arms for efficient clinical trials.
Highlights
Data driven approaches to personalized medicine have the potential to improve patient outcomes while minimizing costs and reducing levels of risk to the patient
Our current work demonstrates that a digital twin model for forecasting the progression of relevant clinical measurements in patients at risk of ischemic stroke was virtually indistinguishable from real patient data under an adversarial machine learning (ML) discriminator
The machine learning model developed in this study shows promise as a necessary precision medicine approach to individualized forecasting of disease progression
Summary
Data driven approaches to personalized medicine have the potential to improve patient outcomes while minimizing costs and reducing levels of risk to the patient. A digital twin of a patient is a simulation of the patient’s trajectory that behaves identically to the patient in terms of outcomes These simulated trajectories can be used to model what is likely to happen to a patient in the future, if no outside intervention changes their clinical course. Digital twins have their roots in the domain of engineering and have been applied by NASA in the development of aerospace vehicles [5] as well as in biomanufacturing [4] and in civil engineering [6]. Digital twin models, which forecast entire trajectories of patient health data, have potential applications in clinical trials and patient management. Conclusion: Through accurate projection of patient trajectories, this model may help inform clinical decision making or provide virtual control arms for efficient clinical trials
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