UMR1227, Lymphocytes B et Autoimmunité, Université de Brest, Inserm, CHU Brest, LabEx IGO, Brest; and LATIM, Laboratoire de Traitement de l'Information Médicale, UMR 1101, IBRBS, Université de Brest, Inserm, CHU, Brest, France.
LATIM, Laboratoire de Traitement de l'Information Médicale, UMR 1101, IBRBS, Université de Brest, Inserm, CHU, Brest, France.
Clin Exp Rheumatol. 2020 Jul-Aug;38(4):776-782. Epub 2020 Feb 14.
Rheumatologists use classification criteria to separate patients with inflammatory rheumatic diseases (IRD). They change over time, and the concepts of the diseases also change. The paradigm is currently moving as the goal of classification in the future will be more to select which patients may be relevant for a specific treatment rather than to describe their characteristics. Therefore, the challenge will be to reclassify multifactorial diseases on the basis of their biological mechanisms rather than their clinical phenotype. Currently, various projects are trying to reclassify diseases using bioinformatics approaches and in the near future the use of advanced machine learning algorithms with large omics datasets could lead to new classification models not only based on a clinical phenotype but also on complex biological profile and common sensitivity to targeted treatment. These models would highlight common biological pathways between patients classified in the same cluster and provide a deep understanding of the mechanisms involved in the patient's clinical phenotype. Such approaches would ultimately lead to classification models that rely more on biological causes than on symptoms. This overview on current classification of subgroups of IRD summarises the classification criteria that we use routinely, and how we will classify IRD in the future using bioinformatics and artificial intelligence techniques.
风湿学家使用分类标准来区分炎症性风湿性疾病(IRD)患者。这些标准随着时间的推移而变化,疾病的概念也在不断变化。目前的模式正在发生变化,因为未来分类的目标将更多地是选择哪些患者可能与特定治疗相关,而不是描述他们的特征。因此,挑战将是根据生物机制而不是临床表型对多因素疾病进行重新分类。目前,许多项目正在尝试使用生物信息学方法对疾病进行重新分类,在不久的将来,使用具有大型组学数据集的先进机器学习算法可能会导致新的分类模型,这些模型不仅基于临床表型,还基于复杂的生物特征和对靶向治疗的共同敏感性。这些模型将突出同一聚类中分类患者之间的共同生物学途径,并深入了解患者临床表型中涉及的机制。这些方法最终将导致更多地依赖生物学原因而不是症状的分类模型。这篇关于炎症性风湿性疾病亚组的分类综述总结了我们常规使用的分类标准,以及我们未来如何使用生物信息学和人工智能技术对 IRD 进行分类。
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