Translated by WANG Weiwei 1 , ZHANG Ge 2 , YANG Zhirong 3 , MA Xiaojie 4 , SUN Feng 5,6 , ZHAN Siyan 5,6,7 , 譯
  • 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
藥品不良反應; 藥物流行病學; 藥物警戒; 信號檢測; 數據挖掘
10.7507/1672-2531.202011104
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近十年，在藥品不良反應監測領域，基于醫療保健數據庫的安全信號檢測方法受到越來越多的關注，已成為彌補自發報告固有局限性的重要手段。目前數據挖掘方法主要基于比值失衡分析法（disproportionality analysis）、傳統藥物流行病學設計（如自身對照設計）、序列對稱分析（sequence symmetry analysis，SSA）、序貫統計檢驗（sequential statistical testing）、時序關聯規則（temporal association rules）、監督機器學習（supervised machine learning，SML）、樹狀掃描統計量方法（tree-based scan statistic）等。本文從應用場景和實用性角度對醫療保健數據庫中安全信號檢測方法及其性能進行介紹。

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

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