# A Digital Twins Machine Learning Model for Forecasting Disease Progression in Stroke Patients
## 6 Discussion  
In this paper, we present a variational autoencoder model to create a \"digital twin\" of data for patients who experienced an ischemic stroke. This method is capable of accurately capturing the cross-sectional and longitudinal properties of real patient data such that the stream of simulated data is statistically indistinguishable from real patient data, as shown by the results of the time-lagged correlation and projected next measure experiments. This work builds upon past developing digital twin models for forecasting of Alzheimer's disease and multiple sclerosis progression [3, 6]. In particular, 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 ML discriminator. To ensure that the model was accurately learning the unimodal properties of the clinical features, \(t\)-tests comparing real vs. synthetic features were run (Figure 2). The resulting \(p\)-values were larger than 0.6 for each feature, suggesting replication of these distributions. Linear regression to the means and standard deviations of real vs. synthetic data yielded strong fitting (Figure 3A,B), with slope nearly 1.0 (R\({}^{2}\geq\) 0.99); a model that perfectly replicates the real data would have a slope of exactly 1. In addition, statistical dependence between features in the generated data was measured using correlation between pairs of features in the generated and real trajectories. Linear regression to the paired data demonstrated strong fitting with a slope of 0.99 (R\({}^{2}\geq\) 0.99). (Figure 3C), indicating fidelity between synthetic data and actual data. Plots of the covariance structures of the feature values between real and synthetic data were virtually indistinguishable (Figure 4), which also indicates accuracy in the trajectory of simulated data when compared against real patient data. Regression to real vs. synthetic time-lagged correlation values of feature pairs (Figure 3D,E) indicates that the model replicates the statistical dependencies in the patients' generated timelines as time progresses. To visualize the model's effectiveness when generating long-term forecasts, clinical feature projections over 10 years into the future (using only the first 270 days of measurements) were plotted to further substantiate this finding (Figure 5). This research fills a gap in knowledge regarding a tool that has the potential to be transformative for personalized medicine and patient care in that is presents a scalable solution that is operationally nimble and relies only upon readily available EHR data to overcome constraints that other digital twins models have reported due to data availability and practical operating requirements [20, 21]. Digital twin models have marked value in the study of progressive neurological conditions like [PERSON]'s disease and multiple sclerosis. Neurological disorders are notoriously difficult to study due to the difficulty in determining onset time [22], slow and unpredictable disease progression [23], and the long follow-up times necessary to ascertain patient outcomes [24]. These factors necessitate large and expensive trials [24, 25, 26] and clearly illuminate the value of digital twin models for facilitating more efficient study of these diseases. Digital twin models also have applications for disease study beyond the setting of rare and slowly progressive conditions. Such models can be used to eliminate the need for control arms in clinical trials, allowing a digital twin of each patient to serve as their own control, thereby allowing a greater number of patients to benefit from treatment and increasing study power. In the case of ischemic stroke, in particular, a digital twin model could be used to assess a patient's risk of incident or recurrent ischemic stroke with and without the use of prophylactic anticoagulant therapy [27, 28].