PURPOSE: We developed a computerized system for signal detection in spontaneous reporting system (SRS) of Shanghai. Data acquisition, data mining could be carried out automatically and the process of data preprocessing and cleaning could be facilitated. This system was expected to detect signals from SRS after drug licensing with minimum patient exposure. METHODS: This system was developed by Microsoft visual basic (VB) 6.0. Data preprocessing, data cleaning, and data mining were based upon visual basic for application (VBA) in Microsoft Excel 2003. Database of drug generic name and adverse drug reaction (ADR) standard dictionary were set up initially for data cleaning and coding. Algorithms including reporting odds ratio (ROR), proportional reporting ratio (PRR), measure used by the Medicines and Healthcare Products Regulatory Agency (MHRA), Bayesian confidence propagation neural network (BCPNN) were employed in this system. Crude ADR reports submitted to Shanghai ADR SRS from December 2003 to April 2007 were used as a material in this study to test the feasibility and flexibility of this system. RESULTS: Thirty two thousand seven hundred and fourty six crude ADR reports were acquired from the SRS automatically. Two thousand one hundred and fourty seven drug generic name and 621 ADR name were kept in the database after data preprocessing and cleaning. A total of 1430, 1419, 868 and 997 possible drug-ADR signals were generated by ROR, PRR, BCPNN and MHRA, respectively. CONCLUSIONS: The results indicate that this computerized system is a flexible one that can help to detect possible drug-ADR signals intelligently in SRS of Shanghai now. It is a promising system for post-marketing surveillance on both chemical medicine and Chinese traditional medicine.
PURPOSE: We developed a computerized system for signal detection in spontaneous reporting system (SRS) of Shanghai. Data acquisition, data mining could be carried out automatically and the process of data preprocessing and cleaning could be facilitated. This system was expected to detect signals from SRS after drug licensing with minimum patient exposure. METHODS: This system was developed by Microsoft visual basic (VB) 6.0. Data preprocessing, data cleaning, and data mining were based upon visual basic for application (VBA) in Microsoft Excel 2003. Database of drug generic name and adverse drug reaction (ADR) standard dictionary were set up initially for data cleaning and coding. Algorithms including reporting odds ratio (ROR), proportional reporting ratio (PRR), measure used by the Medicines and Healthcare Products Regulatory Agency (MHRA), Bayesian confidence propagation neural network (BCPNN) were employed in this system. Crude ADR reports submitted to Shanghai ADR SRS from December 2003 to April 2007 were used as a material in this study to test the feasibility and flexibility of this system. RESULTS: Thirty two thousand seven hundred and fourty six crude ADR reports were acquired from the SRS automatically. Two thousand one hundred and fourty seven drug generic name and 621 ADR name were kept in the database after data preprocessing and cleaning. A total of 1430, 1419, 868 and 997 possible drug-ADR signals were generated by ROR, PRR, BCPNN and MHRA, respectively. CONCLUSIONS: The results indicate that this computerized system is a flexible one that can help to detect possible drug-ADR signals intelligently in SRS of Shanghai now. It is a promising system for post-marketing surveillance on both chemical medicine and Chinese traditional medicine.