Revolutionizing MS Rehabilitation with Digital Twins and Machine Learning: A Promising Path to Precision Medicine

With the development in technologies such as IoT, Big Data, Data Science, AR/VR and cloud computing, Digital Twin revolutionized the manufacturing and automobile sector as a powerful tool to simulate concept to practice. AI and simulation is incorporated to carry out research in healthcare domain to make decisions on clinical pathway planning, medical resource provision and medical trial prediction. By combining digital twin and healthcare, there will be a new-fangled and effective way to offer more precise and patient-tailored healthcare services to the people. Multiple Sclerosis (MS) is a chronic auto-immune neural disorder that affects the central nervous system, brain and spinal cord which may lead to neurological disability and death if untreated. In this research study, we have developed a Healthcare Digital Twin (HDT) for persons with MS to make predictions about the cognitive, physical and functional disability transition on employing machine learning (ML) algorithms. The experimental results indicate that among the various machine learning algorithms tested, the Gradient Boosted classifier performs the best in predicting the likelihood of MS patients transitioning to a stage of moderate disability within a time frame of 0–12 months. This application of HDT empowered with AI will support Healthcare Professionals (HCPs) to make clinical decisions regarding the planning of rehabilitation based on individual health reports.