Data mining in clinical big data: the frequently used databases, steps, and methodological models.
Data mining in clinical big data: the frequently used databases, steps, and methodological models.
复制标题
DOI：
10.1186/s40779-021-00338-z
复制
发表时间：
2021-08-11
影响因子：
21.1
通讯作者：
Lyu J
中科院分区：
文献类型：
--
作者：
Wu WT;Li YJ;Feng AZ;Li L;Huang T;Xu AD;Lyu J
许多高质量的研究已经从公共数据库中出现，如监测、流行病学和最终结果（SEER）、国家健康和营养检查调查（NHANES）、癌症基因组图谱（TCGA）和重症监护医疗信息集市（MIMIC）；然而，这些数据往往具有高度的维度异质性、时效性、稀缺性、不规则性等特点，导致这些数据的价值没有得到充分利用。数据挖掘技术在评估患者风险、辅助临床决策建立疾病预测模型等方面表现优异，是医学研究的前沿领域。因此，数据挖掘在临床大数据研究中，特别是在大型医疗公共数据库中具有独特的优势。本文介绍了目前主要的医疗公共数据库，用简单的语言描述了数据挖掘的步骤、任务和模型。此外，我们还描述了数据挖掘方法及其实际应用。这项工作的目的是帮助临床研究人员对数据挖掘技术在临床大数据上的应用有一个清晰直观的认识，从而促进产生对医生和患者都有益的研究成果。
Many high quality studies have emerged from public databases, such as Surveillance, Epidemiology, and End Results (SEER), National Health and Nutrition Examination Survey (NHANES), The Cancer Genome Atlas (TCGA), and Medical Information Mart for Intensive Care (MIMIC); however, these data are often characterized by a high degree of dimensional heterogeneity, timeliness, scarcity, irregularity, and other characteristics, resulting in the value of these data not being fully utilized. Data-mining technology has been a frontier field in medical research, as it demonstrates excellent performance in evaluating patient risks and assisting clinical decision-making in building disease-prediction models. Therefore, data mining has unique advantages in clinical big-data research, especially in large-scale medical public databases. This article introduced the main medical public database and described the steps, tasks, and models of data mining in simple language. Additionally, we described data-mining methods along with their practical applications. The goal of this work was to aid clinical researchers in gaining a clear and intuitive understanding of the application of data-mining technology on clinical big-data in order to promote the production of research results that are beneficial to doctors and patients.
登录
查看更多内容
影响因子：
8.8
作者：
Carmona-Bayonas A;Jiménez-Fonseca P;Font C;Fenoy F;Otero R;Beato C;Plasencia JM;Biosca M;Sánchez M;Benegas M;Calvo-Temprano D;Varona D;Faez L;de la Haba I;Antonio M;Madridano O;Solis MP;Ramchandani A;Castañón E;Marchena PJ;Martín M;Ayala de la Peña F;Vicente V
通讯作者：
Vicente V
影响因子：
3.7
作者：
Docampo E;Collado A;Escaramís G;Carbonell J;Rivera J;Vidal J;Alegre J;Rabionet R;Estivill X
通讯作者：
Estivill X
影响因子：
3.3
作者：
Arlot, Sylvain;Celisse, Alain
通讯作者：
Celisse, Alain
影响因子：
14.9
作者：
Davis AP;Grondin CJ;Johnson RJ;Sciaky D;McMorran R;Wiegers J;Wiegers TC;Mattingly CJ
通讯作者：
Mattingly CJ
影响因子：
4.5
作者：
EFRON, B
通讯作者：
EFRON, B