Borrowing external information to improve Bayesian confidence propagation neural network.

Journal: European journal of clinical pharmacology
Published Date: Jun 1, 2020

Abstract

PURPOSE: A Bayesian confidence propagation neural network (BCPNN) is a signal detection method used by the World Health Organization Uppsala Monitoring Centre to analyze spontaneous reporting system databases. We modify the BCPNN to increase its sensitivity for detecting potential adverse drug reactions (ADRs).

Authors

  • Keisuke Tada
    Biostatistics & Programming, Sanofi K.K, Tokyo Opera City Tower, 3-20-2, Nishi Shinjuku, Shinjuku-ku, Tokyo, 163-1488, Japan. Keisuke.Tada@sanofi.com.
  • Kazushi Maruo
    Department of Biostatistics, Faculty of Medicine, University of Tsukuba, Tennodai, 1-1-1, Tsukuba-shi, Ibaraki, 305-8575, Japan.
  • Naoki Isogawa
    Clinical Statistics, Pfizer R&D Japan, Shinjuku Bunka Quint Building, 3-22-7, Yoyogi, Shibuya-ku, Tokyo, 151-8589, Japan.
  • Yusuke Yamaguchi
    Data Science, Development, Astellas Pharma Inc., 2-5-1, Nihonbashi-Honcho, Chuo-ku, Tokyo, 103-8411, Japan.
  • Masahiko Gosho
    Department of Clinical Trial and Clinical Epidemiology, Faculty of Medicine, University of Tsukuba, Tsukuba Ibaraki, Japan.

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