# A Digital Twins Machine Learning Model for Forecasting Disease Progression in Stroke Patients
## 6 Discussion
The present work demonstrates proof of concept that digital twin models may be useful for forecasting relevant clinical trajectories for acute-onset conditions [29, 30] and simulating disease progression. Estimation of disease progression using ML to simulate data has been examined in several research studies. [PERSON] et al. used an unsupervised ML model to create data simulating the trajectory of Alzheimer's disease in individual patients [3]. This study compared real and generated patient data, including the ADAS-Cog scale [31] and Mini Mental State Exam (MMSE) [32] scores. The authors found that generated patient data was virtually indistinguishable from real patient data both for composite disease severity scores and individual score components, thereby simulating entire disease trajectories [3]. Another recent study by [PERSON] et al. examined the ability of simulated patient data (i.e., the \"digital subject\") to forecast the trajectory of Multiple Sclerosis (MS), a disease similar to Alzheimer's if only in its heterogenous and often complicated nature [1]. Using clinical trial data from control subjects that are made available by the Multiple Sclerosis Outcome Assessments Consortium, the researchers created 1000 digital subjects for each patient dataset included in the study. The results of the study indicated that the actual patient data and simulated data could not be differentiated from one another by a ML model. Importantly, digital twin type simulations have also been used to simulate the immunogenic landscape of SARS-CoV-2 [33], which may encourage the development of universal blueprints for COVID-19 vaccine designs. Personalized medicine presents itself as a profound advancement of medical technology and clinical decision support tools. Potential risks related to the use of digital twins include those related to accuracy, data quality and usefulness [2, 34, 35, 36], health equity [35], the ability to incorporate these high-tech tools into existing infrastructure [34, 36], and privacy [2, 35, 36]. However, the potential value of this tool indicates a need for further exploration to uncover ways to minimize these pitfalls. Using a generative adversarial networks (GAN) model, [PERSON] et al. has offered one potential solution to this issue of privacy by successfully using ML to develop synthetic patient data that mirrored the accuracy of predictive models that used real patient data [37]. One concern may be that the training data can be replicated using the trained model, allowing leakage of patient information. VAEs sample from a distribution for each generated time example, and so have some but no direct coupling between training examples and generated examples. Using a GAN, as suggested in [PERSON] et al., could further decouple the real patient information from the end model due to the different sampling and training mechanisms, potentially leading to greater privacy [37]. There are several limitations of this work. First, the model was trained and tested on a small patient population. Only 244 unique patient encounters were used by the model after data processing and filtering, which may have impacted model performance. In future work it would be beneficial to validate model performance on larger data sets. It is also possible that log-transforming the lab features, while an attempt to bring the feature distributions closer to a normal distribution, may have inadvertently introduced skewness to the data. Another drawback is inherent to the binning process: it may be desirable that measurements occurring close to the boundaries of bins be accounted for by both in some way, with a smoother boundary than the one presented in this work. Additionally, the adversary model trained to distinguish between real and simulated data was developed using logistic regression. While these results suggested that real and simulated data were virtually indistinguishable, it is possible that an adversary model trained using more sophisticated ML methods would be able to better discriminate between the two data types.