Translated by WANG Weiwei 1 , ZHANG Ge 2 , YANG Zhirong 3 , MA Xiaojie 4 , SUN Feng 5,6 , ZHAN Siyan 5,6,7 , 译
  • 1. The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing 100088, P.R.China;
  • 2. Zhangfan Information Technology (Shanghai) Co., Ltd, Shanghai 200090, P.R.China;
  • 3. Primary Care Unit, School of Clinical Medicine, University of Cambridge, Cambridge CB1 8RN, UK;
  • 4. Department of Research and Education, Urumqi Fourth People’s Hospital, Urumqi 830002, P.R.China;
  • 5. Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, P.R.China;
  • 6. Center of Evidence-based Medicine and Clinical Research, Peking University, Beijing 100191, P.R.China;
  • 7. Research Center of Clinical Epidemiology, Peking University Third Hospital, Beijing 100191, P.R.China;
SUN Feng, Email: sunfeng@bjmu.edu.cn
药品不良反应; 药物流行病学; 药物警戒; 信号检测; 数据挖掘
10.7507/1672-2531.202011104
Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables References Cited by

近十年，在药品不良反应监测领域，基于医疗保健数据库的安全信号检测方法受到越来越多的关注，已成为弥补自发报告固有局限性的重要手段。目前数据挖掘方法主要基于比值失衡分析法（disproportionality analysis）、传统药物流行病学设计（如自身对照设计）、序列对称分析（sequence symmetry analysis，SSA）、序贯统计检验（sequential statistical testing）、时序关联规则（temporal association rules）、监督机器学习（supervised machine learning，SML）、树状扫描统计量方法（tree-based scan statistic）等。本文从应用场景和实用性角度对医疗保健数据库中安全信号检测方法及其性能进行介绍。

Citation： Translated by WANG Weiwei, ZHANG Ge, YANG Zhirong, MA Xiaojie, SUN Feng, ZHAN Siyan. Methods for safety signal detection in healthcare databases: a literature review. Chinese Journal of Evidence-Based Medicine, 2021, 21(4): 475-481. doi: 10.7507/1672-2531.202011104 Copy

Copyright © the editorial department of Chinese Journal of Evidence-Based Medicine of West China Medical Publisher. All rights reserved

  • Previous Article

    Application of propensity score methods in observational studies

  • Next Article

    Data visualization of multiple linear regression analysis practiced by R Studio software