# Rule-Based DSL for Continuous Features and ML Models Selection in Multiple Sclerosis Research
## 2 Background
### Multiple Sclerosis  
MS is a complex and degenerative neurological disorder that primarily affects the central nervous system (CNS), which comprises the brain and spinal cord. The core pathology of MS lies in the immune-mediated attack on myelin sheaths--the insulating covers of nerve cells--leading to a disruption in the nervous system's signal transmission capabilities, manifesting in a myriad of physical and mental symptoms [9; 10; 11]. A notable characteristic of MS is its prevalence among young adults, typically manifesting between the ages of 20 to 40 years, making it the most common disabling neurological disease in this age demographic [12]. The diagnosis of MS primarily revolves around the identification of lesions in the CNS using imaging techniques such as MRI, with Gadolinium-enhanced T1-weighted MRI being particularly useful in showing active lesions [9]. The Expanded Disability Status Scale (EDSS) is a fundamental tool for evaluating the progression of MS, measuring the degree of physical disability based on a neurological exam of seven functional systems and an individual's walking ability. The progression of MS, as per the EDSS, is defined as a minimum increase in the EDSS from a baseline level, with the specifics dependent on the initial EDSS score [13]. Walking speed, gauged by the timed 25-foot walk (T25-FW), determines how fast a person with MS can walk 25 feet. It is used alongside the EDSS to evaluate disease progression and impact on daily activities and function [14]. It is notable that a significant correlation exists between EDSS scores and walking disability, reflecting how these measures provide a nuanced understanding of a patient's mobility status and overall disease progression [15]. While there is no known cure for MS, several therapeutic strategies exist to manage the symptoms, expedite recovery post-attacks, reduce inflammatory disease activity, and slow down disease progression [9].  
The prognosis of MS varies significantly among individuals; some experience a mild course with minimal disability, while others face a steadily worsening disease trajectory leading to increased disability over time [11]. Through artificial intelligence-based analysis of several disease parameters--including clinical and para-clinical outcomes, multi-omics, biomarkers, patient-related data, information about the patient's life circumstances and plans, and medical procedures--a digital twin paired to the patient's characteristics can be created, enabling healthcare professionals to handle large amounts of patient data. This can contribute to more personalized and effective care by integrating data from multiple sources in a standardized manner, implementing individualized clinical pathways, supporting physician-patient communication, and facilitating shared decision-making [16]. However, the multifaceted nature of MS and its variable manifestation underline the need for continued research and evolving therapeutic strategies to ameliorate the lives of those affected. In this study, we propose a novel DSL-based method for analyzing MRI-related data along with different forms of clinical data such as walking abilities in patients with MS.