Enhancing Rheumatoid Arthritis Diagnosis: Combining Case-Based Reasoning on EHR Data with Deep Learning on Medical Images 

Abstract

The diagnosis of rheumatoid arthritis (RA) diagnosis demands precise detection methods due to its complex symptomatology. This study presents a novel hybrid diagnostic framework that is the first to integrates Case-Based Reasoning (CBR) with deep learning and introduce three key innovations: (i) a dual-pathway architecture that combine electronic health records with imaging analysis, (ii) an Enhanced Clustering-Based K-nearest neighbors (ECB KNN) model for optimal feature selection, and (iii) a dynamic K-means clustering approach for handling class imbalance. We evaluated our framework using two comprehensive datasets: MIMIC-IV-Hosp, containing clinical data and MIMIC-CXR containing 377,110 chest X-ray images. The model employs a VGG16-based CNN for radiological feature extraction, with a particular focus on pulmonary manifestations, which is combined with our ECB KNN algorithm for patient-specific clinical data analysis. Using five-fold cross-validation, our framework is shown to achieve superior performance metrics (precision: 0.90-0.95, recall: 0.89-0.93, F1-score: 0.91) compared to baseline methods (traditional CNN: precision 0.82, recall 0.79; standard CBR: precision 0.85, recall 0.83). This significant improvement in diagnostic accuracy demonstrates the potential of our framework in terms of enhancing early RA detection and clinical decision support. The architecture of the model architecture is designed to allow for extensibility to other rheumatic conditions, thereby offering a comprehensive solution for multi-disease diagnosis in rheumatology.