| dc.description.abstract | Rheumatoid arthritis is a chronic autoimmune disease characterized by persistent joint
inflammation, gradual joint damage, and recurring painful episodes that significantly
reduce quality of life. Predicting the onset of painful episodes remains challenging due
to complex interactions among biological, psychological, behavioral, and environmental
factors. Traditional clinical assessments are often not enough to capture these
dynamic changes. The objective of this research is to improve understanding of
how multimodal data and machine learning techniques can support the prediction
and management of painful episodes in individuals with rheumatoid arthritis. A
structured literature review was conducted to identify and synthesize recent studies
that integrate clinical records, wearable sensor data, environmental measurements, and
stress indicators derived from eye-based measures. The reviewed research utilizing
advanced machine learning approaches such as Long Short-Term Memory Neural
Networks, hybrid Convolutional Neural Network and Long Short-Term Memory models
and Transformer-based architectures to analyze relationships through heterogeneous
data sources. Findings indicate that multimodal approaches consistently provide more
accurate predictions than single-source models in predictive accuracy and clinical
relevance. Reported benefits include earlier identification of painful episodes, improved
intervention timing, patient self-management, and personalized treatment planning.
However, challenges such as limited dataset sizes, explainability of machine learning
models, privacy concerns, and lack of real-world validation remain. This review
highlights the potential of multimodal machine learning systems to enhance long-term
rheumatoid arthritis management and improve patient outcomes. | en_US |
