Data mining in clinical big data: the frequently used databases, steps, and methodological models

Wen-Tao Wu; Yuan-Jie Li; Ao-Zi Feng; Li Li; Tao Huang; An-Ding Xu; Jun Lv

doi:10.1186/s40779-021-00338-z

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Data mining in clinical big data: the frequently used databases, steps, and methodological models

REVIEW | Updated：2023-02-28

- 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
- 解放军医学杂志（英文版） 2021年8卷第4期页码：552-563
- Affiliations：
  
  1.Department of Clinical Research, the First Affiliated Hospital of Jinan University, Guangzhou 510632, China
  2.School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China
  3.Department of Human Anatomy, Histology and Embryology, School of Basic Medical Sciences, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China
  4.Department of Neurology, the First Affiliated Hospital of Jinan University, Guangzhou 510632, China
- Author bio：
  
  * tlil@jnu.edu.cn;
  lyujun2020@jnu.edu.cn
- Funds：
  
  the National Social Science Foundation of China(16BGL183)
- DOI：10.1186/s40779-021-00338-z
  中图分类号：
- 纸质出版：2021-12
- Accepted：
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Data mining in clinical big data: the frequently used databases, steps, and methodological models[J]. 解放军医学杂志（英文版）, 2021,8(4):552-563.

Wu et al.: Data mining in clinical big data: the frequently used databases, steps, and methodological models. Mil Med Res, 2021, 8: 44.
Data mining in clinical big data: the frequently used databases, steps, and methodological models[J]. 解放军医学杂志（英文版）, 2021,8(4):552-563. DOI： 10.1186/s40779-021-00338-z.

Wu et al.: Data mining in clinical big data: the frequently used databases, steps, and methodological models. Mil Med Res, 2021, 8: 44. DOI： 10.1186/s40779-021-00338-z.

摘要

Abstract

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.

关键词

Keywords

references

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