Data processing and analysis in real-world traditional Chinese medicine clinical data: challenges and approaches

被引:78
作者
Liu, Baoyan
Zhou, Xuezhong [1 ]
Wang, Yinhui [2 ]
Hu, Jingqing [2 ]
He, Liyun [3 ]
Zhang, Runshun [2 ]
Chen, Shibo [2 ]
Guo, Yufeng [2 ]
机构
[1] Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing 100044, Peoples R China
[2] China Acad Chinese Med Sci, Guanganmen Hosp, Beijing 100053, Peoples R China
[3] China Acad Chinese Med Sci, Inst Basic Res Clin Med, Beijing 100700, Peoples R China
关键词
traditional Chinese medicine; real-world clinical data; data processing and analysis; KNOWLEDGE DISCOVERY; QUALITY; PNEUMONIA; DISEASE; TRIALS; RECORD;
D O I
10.1002/sim.4417
中图分类号
Q [生物科学];
学科分类号
090105 [作物生产系统与生态工程];
摘要
Traditional Chinese medicine (TCM) is a clinical-based discipline in which real-world clinical practice plays a significant role for both the development of clinical therapy and theoretical research. The large-scale clinical data generated during the daily clinical operations of TCM provide a highly valuable knowledge source for clinical decision making. Secondary analysis of these data would be a vital task for TCM clinical studies before the randomised controlled trials are conducted. In this article, we discuss the challenges and issues, such as structured data curation, data preprocessing and quality, large-scale data management and complex data analysis requirements, in the data processing and analysis of real-world TCM clinical data. Furthermore, we also discuss related state-of-the-art research and solutions in China. We have shown that the clinical data warehouse based on the collection of structured electronic medical record data and clinical terminology would be a promising approach for generating clinical hypotheses and helping the discovery of clinical knowledge from large-scale real-world TCM clinical data. Copyright (c) 2011 John Wiley & Sons, Ltd.
引用
收藏
页码:653 / 660
页数:8
相关论文
共 55 条
[1]
The revised CONSORT statement for reporting randomized trials: Explanation and elaboration [J].
Altman, DG ;
Schulz, KF ;
Moher, D ;
Egger, M ;
Davidoff, F ;
Elbourne, D ;
Gotzsche, PC ;
Lang, T .
ANNALS OF INTERNAL MEDICINE, 2001, 134 (08) :663-694
[2]
[Anonymous], 2007, Introduction to Statistical Relational Learning
[3]
Assessing the quality of clinical data in a computer-based record for calculating the Pneumonia Severity Index [J].
Aronsky, D ;
Haug, PJ .
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION, 2000, 7 (01) :55-65
[4]
Issues associated with secondary analysis of population health data [J].
Bibb, Sandra C. Garmon .
APPLIED NURSING RESEARCH, 2007, 20 (02) :94-99
[5]
Latent Dirichlet allocation [J].
Blei, DM ;
Ng, AY ;
Jordan, MI .
JOURNAL OF MACHINE LEARNING RESEARCH, 2003, 3 (4-5) :993-1022
[6]
Computer-aided diagnosis with potential application to rapid detection of disease outbreaks [J].
Burr, Tom ;
Koster, Frederick ;
Picard, Rick ;
Forslund, Dave ;
Wokoun, Doug ;
Joyce, Ed ;
Brillman, Judith ;
Froman, Phil ;
Lee, Jack .
STATISTICS IN MEDICINE, 2007, 26 (08) :1857-1874
[7]
Castle Jane E, 2003, J Neurosci Nurs, V35, P287
[8]
Uniqueness of medical data mining [J].
Cios, KJ ;
Moore, GW .
ARTIFICIAL INTELLIGENCE IN MEDICINE, 2002, 26 (1-2) :1-24
[9]
Dzeroski S, 2010, DATA MINING AND KNOWLEDGE DISCOVERY HANDBOOK, SECOND EDITION, P887, DOI 10.1007/978-0-387-09823-4_46
[10]
Knowledge discovery in traditional Chinese medicine: State of the art and perspectives [J].
Feng, Yi ;
Wu, Zhaohui ;
Zhou, Xuezhong ;
Zhou, Zhongmei ;
Fan, Weiyu .
ARTIFICIAL INTELLIGENCE IN MEDICINE, 2006, 38 (03) :219-236