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Machine Learning in Rheumatic Diseases.
Clinical Reviews in Allergy & Immunology ( IF 9.1 ) Pub Date : 2020-07-17 , DOI: 10.1007/s12016-020-08805-6
Mengdi Jiang 1, 2 , Yueting Li 1, 2 , Chendan Jiang 3 , Lidan Zhao 1 , Xuan Zhang 2 , Peter E Lipsky 4
Affiliation  

With advances in information technology, the demand for using data science to enhance healthcare and disease management is rapidly increasing. Among these technologies, machine learning (ML) has become ubiquitous and indispensable for solving complex problems in many scientific fields, including medical science. ML allows the development of guidelines and framing of the evaluation system for complex diseases based on massive data. In the analysis of rheumatic diseases, which are chronic and remarkably heterogeneous, ML can be anticipated to be extremely helpful in deciphering and revealing the inherent interrelationships in disease development and progression, which can further enhance the overall understanding of the disease, optimize patients’ stratification, calibrate therapeutic strategies, and predict prognosis and outcomes. In this review, the basics of ML, its potential clinical applications in rheumatology, together with its strengths and limitations are summarized.



中文翻译:

风湿病中的机器学习。

随着信息技术的进步,使用数据科学来增强医疗保健和疾病管理的需求正在迅速增加。在这些技术中,机器学习 (ML) 已成为解决包括医学在内的许多科学领域的复杂问题的无处不在和不可或缺的技术。ML 允许基于海量数据制定复杂疾病的评估系统的指南和框架。在风湿病这种慢性且异质性显着的疾病的分析中,ML有望极大地帮助破译和揭示疾病发展和进展中的内在相互关系,从而进一步增强对疾病的整体认识,优化患者分层,校准治疗策略,并预测预后和结果。在这次审查中,

更新日期:2020-07-17
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