Optimizing Treatment Outcomes in Rheumatoid Arthritis Patients: A Data Science Approach to Analysing Multimorbidity

This study aims to explore the data science strategies for new insights into multimorbidity understanding and identifying potential therapy targeting in rheumatoid arthritis patients. Rheumatoid Arthritis (RA) is a chronic inflammatory (auto immune) disease that has the potential to result in co-morbid illnesses, meaning it affects both sides of the equation by making treating patients even tougher than had been previously determined. Although the controversial method of drug-drug interactions remains central to this phenomenon, an even lesser acknowledged mechanism that may exert significant control over disease status and remission rates with RA is the interaction between comorbidities themselves. The presence and types of comorbidities, how they accumulate over time, and their interplay with therapies across the life span remain unknown. To identify comorbidity patterns and predict patient outcomes, we leverage a dataset of patient registries, wearable technology or Electronic Health Records (EHR) using advanced machine learning techniques like clustering and predictive analytics. Image analysis of these data is done by Convolutional Neural Network (CNN). Clinical notes can be particularly informative using random forest, Gradient Boosting Machine (GBM), Support Vector Machine (SVM), by combining these to get an Ensemble Model which improves overall performances of the model and network analysis which enables creating a comprehensive understanding of the relationships between different chronic conditions alongside their treatments. These findings imply that in some cases there may be relationships between specific comorbidity profiles and treatment outcomes, providing a practical basis for investigating individual-level factored medicine. This study analyses these data with different models and highlights the power of data science both in clinical decision-making and uncovering previously unknown patterns of illness to improve patient care.