Centro de Investigaciones Endocrino Metabólicas Dr. Félix Gómez, Escuela de Medicina, Universidad de Zulia, Maracaibo, Venezuela.
Hospital General Provincial de Latacunga, Ministerio de Salud Pública, Cotopaxi, Ecuador.
Arch Med Res. 2018 Apr;49(3):213-215. doi: 10.1016/j.arcmed.2018.08.005. Epub 2018 Aug 14.
Data mining consists of using large database analysis to detect patterns, relationships and models in order to describe (or even predict) the appearance of a future event; to accomplish this, it uses classification methods, rules of association, regression patterns, link and cluster analyses. Recently this approach has been used to propose a new diabetes mellitus classification, using information analysis techniques through which the selection bias minimally influences categorization, this new focus that includes data mining previously implemented to predict, identify biomarkers, complications, therapies, health policies, genetic and environmental effects of this disease; it could be generalized in the field of endocrinology, in the classification of other endocrine diseases.
数据挖掘包括使用大型数据库分析来检测模式、关系和模型，以便描述（甚至预测）未来事件的出现；为此，它使用分类方法、关联规则、回归模式、链接和聚类分析。最近，这种方法已被用于提出一种新的糖尿病分类方法，使用信息分析技术，通过该技术，选择偏差对分类的影响最小化，这种新的重点包括以前用于预测、识别生物标志物、并发症、疗法、该疾病的遗传和环境影响的数据挖掘；它可以推广到内分泌学领域，用于分类其他内分泌疾病。
