A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases

Abstract

Autoimmune diseases are chronic, multifactorial conditions. Through machine learning (ML), a branch of the wider field of artificial intelligence, it is possible to extract patterns within patient data, and exploit these patterns to predict patient outcomes for improved clinical management. Here, we surveyed the use of ML methods to address clinical problems in autoimmune disease. A systematic review was conducted using MEDLINE, embase and computers and applied sciences complete databases. Relevant papers included "machine learning" or "artificial intelligence" and the autoimmune diseases search term(s) in their title, abstract or key words. Exclusion criteria: studies not written in English, no real human patient data included, publication prior to 2001, studies that were not peer reviewed, non-autoimmune disease comorbidity research and review papers. 169 (of 702) studies met the criteria for inclusion. Support vector machines and random forests were the most popular ML methods used. ML models using data on multiple sclerosis, rheumatoid arthritis and inflammatory bowel disease were most common. A small proportion of studies (7.7% or 13/169) combined different data types in the modelling process. Cross-validation, combined with a separate testing set for more robust model evaluation occurred in 8.3% of papers (14/169). The field may benefit from adopting a best practice of validation, cross-validation and independent testing of ML models. Many models achieved good predictive results in simple scenarios (e.g. classification of cases and controls). Progression to more complex predictive models may be achievable in future through integration of multiple data types.

Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review

“…In particular, it is important to determine which evaluation metric(s) should be measured in a given health care context. AUROC is considered to be a superior metric for classification accuracy, particularly when unbalanced datasets are used [ 122 , 123 ] because it is unaffected by unbalanced data, which is typical in health care. However, 36 studies in our review did not report AUROC.…”

“…However, 36 studies in our review did not report AUROC. Evaluation measures such as precision-recall can also reflect model performance accurately [ 123 ]; however, only 11 studies in our review evaluated AI based on precision-recall. Using inappropriate measures to evaluate AI performance might impose a threat to patient safety.…”

Virus database annotations assist in tracing information on patients infected with emerging pathogens

“…Automated clustering using digital annotations should decrease the substantial risk of overlooking a relevant prior study or finding. Artificial intelligence (AI) can further optimize the diagnostic accuracy[ 85 ]. However, AI may confront other risks in overlooking minor trends in rare cases by overfitting errors[ 85 ].…”

“…Artificial intelligence (AI) can further optimize the diagnostic accuracy[ 85 ]. However, AI may confront other risks in overlooking minor trends in rare cases by overfitting errors[ 85 ]. Virus and medical annotation tags in a simple and unified spreadsheet format are preferable for further analyses in the future.…”

We should monitor our patients with wearable technology instead of neurological examination – Commentary

“…In this context, it is of outmost importance the capacity of artificial intelligence and machine learning techniques to identify clinically relevant patterns among an abundance of information coming from the data revolution that medicine and society is currently living. 9 The abundance of data is also the main challenge that researchers must overcome. The current knowledge on the role of wearables technology in MS is still fragmented, limiting the possibility to standardize the use in clinical or research practice.…”