ExSMART-PreRA: Explainable Survival and Risk Assessment Using Machine Learning for Time Estimation in Preclinical Rheumatoid Arthritis

Salehihafshejani F, Bayat S, Schett G, Kleyer A, Altstidl TR, Eskofier B (2025)

Publication Type: Journal article

Publication year: 2025

Journal

IEEE Journal of Biomedical and Health Informatics Institute of Electrical and Electronics Engineers (IEEE)

DOI: 10.1109/JBHI.2025.3554364

Abstract

Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease affecting peripheral joints. Before clinical diagnosis, individuals may possess certain antibodies and experience discomfort but without specific signs of RA or inflamed joints. This stage is termed “preclinical RA,” as these individuals are at risk of developing the disease. This early stage is difficult to define, necessitating the development of individual risk models. This study aims to estimate the time and risk of RA onset using various survival machine learning models. After identifying the best model, we stratify patients into risk categories and identify key risk factors. Data from 154 anonymized preclinical RA patients were collected and analyzed. Several survival analysis models were evaluated, including Survival Tree, Random Survival Forest, Extreme Gradient Boosting Survival, Linear Multi-Task Model, Neural Multi-Task Model, Support Vector Machines, and Cox Proportional Hazards. The Random Survival Forest model outperformed the others, achieving a mean C-index of 0.798. Using this model, patients were stratified into low-, medium-, and high-risk groups, facilitating personalized scheduling of clinical visits based on RA risk. To enhance model interpretability, SHapley Additive Explanations (SHAP) are employed to identify key risk factors. The baseline level of rheumatoid factor (RF) antibodies is the most significant predictor. Higher levels of anti-cyclic citrullinated peptide (anti-CCP) and RF antibodies at baseline are linked to earlier RA onset. This method provides valuable insights into key factors that might be overlooked in clinical practice and can improve patient management and quality of life for those at risk of developing RA.

Authors with CRIS profile

Fatemeh Salehihafshejani Machine Learning and Data Analytics Lab Sara Bayat Department of Medicine 3 – Rheumatology and Immunology Georg Schett Lehrstuhl für Innere Medizin III Thomas Robert Altstidl Machine Learning and Data Analytics Lab Björn Eskofier Machine Learning and Data Analytics Lab

Additional Organisation(s)

Sonderforschungsbereich 1483/1 - Empathokinästhetische Sensorik - Sensortechniken und Datenanalyseverfahren zur empathokinästhetischen Modellbildung und Zustandsbestimmung (SFB EmpkinS)

Related research project(s)

Bewegungsmuster der Hand aus empathokinästhetischen Sensordaten als diagnostische Größe für Krankheitsaktivität bei Patienten mit rheumatischen Erkrankungen (EmpkinS D01) Empatho-Kinaesthetic Sensor Technology (SFB 1483 EmpkinS) July 1, 2021 - June 30, 2025

Involved external institutions

Charité - Universitätsmedizin Berlin DE Germany (DE)

How to cite

APA:

Salehihafshejani, F., Bayat, S., Schett, G., Kleyer, A., Altstidl, T.R., & Eskofier, B. (2025). ExSMART-PreRA: Explainable Survival and Risk Assessment Using Machine Learning for Time Estimation in Preclinical Rheumatoid Arthritis. IEEE Journal of Biomedical and Health Informatics. https://doi.org/10.1109/JBHI.2025.3554364

MLA:

Salehihafshejani, Fatemeh, et al. "ExSMART-PreRA: Explainable Survival and Risk Assessment Using Machine Learning for Time Estimation in Preclinical Rheumatoid Arthritis." IEEE Journal of Biomedical and Health Informatics (2025).

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