Rule-Based DSL for Continuous Features and ML Models Selection in Multiple Sclerosis Research

Abstract

Machine learning (ML) has emerged as a powerful tool in multiple sclerosis (MS) research, enabling more accurate diagnosis, prognosis prediction, and treatment optimization. However, the complexity of developing and deploying ML models poses challenges for domain experts without extensive programming knowledge. We propose a novel domain-specific language (DSL) that simplifies the process of selecting features, choosing appropriate ML models, and defining training rules for MS research. The DSL offers three approaches: AutoML for automated model and feature selection, manual selection for expert-guided customization, and a customizable mode allowing for fine-grained control. The DSL was implemented and evaluated using real-world MS data. By establishing task-specific DSLs, we have successfully identified workflows that enhance the filtering of ML models and features. This method is crucial in determining the T2-related MRI features that accurately predict both process speed time and walk speed. We assess the effectiveness of using our DSL to enhance ML models and identify feature importance within our private data, aiming to reveal the relationships between features. The proposed DSL empowers domain experts to leverage ML in MS research without extensive programming knowledge. By integrating MLOps practices, it streamlines the ML lifecycle, promoting trustworthy AI through explainability, interpretability, and collaboration. This work demonstrates the potential of DSLs in democratizing ML in MS and paves the way for future research in adaptive and evolving DSL architectures.