Machine learning approaches to improve disease management of patients with rheumatoid arthritis: review and future directions

ABSTRACT

Introduction

Although the management of rheumatoid arthritis (RA) has improved in major way over the last decades, this disease still leads to an important burden for patients and society, and there is a need to develop more personalized approaches. Machine learning (ML) methods are more and more used in health-related studies and can be applied to different sorts of data (clinical, radiological, or ‘omics’ data). Such approaches may improve the management of patients with RA.

Areas covered

In this paper, we propose a review regarding ML approaches applied to RA. A scoping literature search was performed in PubMed, in September 2021 using the following MeSH terms: ‘arthritis, rheumatoid’ and ‘machine learning’. Based on this search, the usefulness of ML methods for RA diagnosis, monitoring, and prediction of response to treatment and RA outcomes, is discussed.

Expert opinion

ML methods have the potential to revolutionize RA-related research and improve disease management and patient care. Nevertheless, these models are not yet ready to contribute fully to rheumatologists’ daily practice. Indeed, these methods raise technical, methodological, and ethical issues, which should be addressed properly to allow their implementation. Collaboration between data scientists, clinical researchers, and physicians is therefore required to move this field forward.

KEYWORDS:

• Artificial intelligence
• artificial neural networks
• deep learning
• diagnosis
• machine learning
• monitoring
• prediction
• random forests
• rheumatoid arthritis
• support vector machine

Article highlights

• Machine learning (ML) is a growing field in health-related research; in rheumatic diseases, ML is applied more and more to rheumatoid arthritis (RA).

• ML methods could provide interesting findings to improve disease management in RA, notably to enable an earlier diagnosis, to monitor disease activity and comorbidities, or to predict outcomes and treatment efficacy.

• However, the implementation of ML findings in current practice is limited by technical, methodological, and ethical issues. These limitations could be addressed by regulators and collaborations between clinicians and data scientists in the years to come.

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Additional information

Funding

This paper was not funded.