A Data Mining Model for Serious Adverse Drug Reaction Signals Based on Real-World Data:Taking Nauclea officinalis Pierre ex Pitard Preparations as an Example for Empirical Analysis
A Data Mining Model for Serious Adverse Drug Reaction Signals Based on Real-World Data:Taking Nauclea officinalis Pierre ex Pitard Preparations as an Example for Empirical Analysis
Based on the current signal detection work of Chinese drug regulatory agencies,this paper explores the construction of a new evidence-based model for the timely detection and accurate identification of severe adverse drug reactions(ADRs)utilizing real-world data(RWD)and data mining techniques.This model,based on the spontaneous reporting system database and the electronic medical records system database,divides ADR signal data mining into four stages:initial signal detection,signal screening,signal verification,and signal evaluation.By applying data mining techniques,the model allows for rapid,high-throughput mining of ADR signals,which gradually improves the level of evidence of the signals and improves the efficiency of signal detection.In this paper,an empirical study using this model for the data mining of adverse reactions of Nauclea officinalis Pierre ex Pitard preparations is presented,initially verifying the model's operability and providing a paradigmatic experience for regulatory authorities to carry out data mining on severe ADR signals.
关键词
真实世界数据/严重药品不良反应/数据挖掘/信号检测/循证证据
Key words
real-world data/serious adverse drug reaction/data mining/signal detection/evidence-based evidence
万方数据