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studies not written in English, no real human patient data included, publication prior to 2001, studies that were not peer reviewed, non-autoimmune disease comorbidity research and review papers. 169 (of 702) studies met the criteria for inclusion.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.

自身免疫性疾病是慢性、多因素疾病。通过机器学习（ML）这一更广泛的人工智能领域的一个分支，可以提取患者数据中的模式，并利用这些模式来预测患者的结果，从而改善临床管理。在这里，我们调查了使用机器学习方法来解决自身免疫性疾病的临床问题。使用 MEDLINE、Embase 以及计算机和应用科学完整数据库进行了系统评价。相关论文的标题、摘要或关键词中包含“机器学习”或“人工智能”以及自身免疫性疾病搜索词。排除标准：非英文撰写的研究、不包含真实的人类患者数据、2001 年之前发表的研究、未经同行评审的研究、非自身免疫性疾病合并症研究和评论论文。 702 项研究中有 169 项符合纳入标准。支持向量机和随机森林是最流行的机器学习方法。使用多发性硬化症、类风湿性关节炎和炎症性肠病数据的 ML 模型最为常见。一小部分研究（7.7% 或 13/169）在建模过程中结合了不同的数据类型。 8.3% 的论文 (14/169) 进行了交叉验证，并结合单独的测试集进行更稳健的模型评估。该领域可能会受益于采用机器学习模型的验证、交叉验证和独立测试的最佳实践。许多模型在简单场景（例如病例和对照的分类）中取得了良好的预测结果。通过集成多种数据类型，未来可能可以实现更复杂的预测模型。