# A Digital Twins Machine Learning Model for Forecasting Disease Progression in Stroke Patients
## 1 Introduction  
Data driven approaches to personalized medicine have the potential to improve patient outcomes while minimizing costs and reducing levels of risk to the patient. Probabilistic models, which aim to provide a best assessment of future events, are useful for mapping the trajectory of a patient and for forecasting their likely course of disease [1]. A large body of evidence suggests that interventions are more precise and effective when individualized mathematical models are used to capture the status of a particular subject over time [2, 3, 4]. These mathematical models can be used to forecast the progression of disease and may improve the effectiveness of interventions when applied in real world practice. 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]. Although many applications of digital twins can be found in the context of industry, health care represents a place where digital twins can have a disruptive impact [7]. It is, for example, very difficult to precisely anticipate the efficacy of a drug in an individual patient [2, 8]. Therapies for theaverage patient may not be well adapted to the individual. Predictive models often cannot confidently make individual-level forecasts due to the inherent heterogeneity that exists across patients. Predictive modeling, therefore, benefits from approaches which allow for accurate characterization and forecasting of disease progression at the individual level. As the application of digital twins in healthcare is in its infancy compared to validated predictive modeling tools, there is a need to examine the utility and accuracy from a cross-disciplinary perspective. For example, the DigitTwin Consortium provides a platform for researchers in academia, industry, and government agencies to explore the value of digital twins technology across various sectors, promote such technology, determine best practices for its use, and to advance the technology [9; 10]. The area of digital twins has been described as the marriage of three components-data science, software engineering, and expert knowledge-which culminates in clinical decision support (CDS) tools that are based upon a personalized medicine approach [11]. Unlike traditional engineering models, which are reflective of generic instances, digital twins reflect the individual characteristics of the subject [1]. In health care, the digital twin model provides the opportunity to see many predicted disease trajectories for a patient which has the potential to inform the patient's current state using data derived from that individual to avoid providing a \"black box\" prediction, in which an explanation for the trajectory is not readily apparent [11]. Digital twins are statistically indistinguishable from actual subjects and allow for individual-level statistical analyses of disease progression [1]. The primary advantages of the use of data from digital twins, as opposed to data from actual patients, are that they present no risk of exposing private health information and make it possible to quickly simulate patient cohorts of any size and characteristics [1]. This is particularly useful for informing the design of clinical trials, which are estimated to cost on average between $12-$33 million, though many can far exceed those amounts [8]. Developing methods to simulate data may yield solutions in medical research more efficiently and with less risk to patients. Data simulation methods may also advance the capability of health technology to tailor and thus improve patient outcomes.