Study on Machine Learning and Prediction Model of Adverse Drug Reactions

Based on the free text data of virtual health community, this paper aims to construct a detection model of adverse drug reaction events by using machine learning. Firstly, this paper reviews the research status related to the theme, builds the overall framework of the detection model of adverse drug reaction events, and expounds the basic elements and logical relationship of each layer model. DIKW system is adopted as the model to design the logical framework. Then, considering the dependence of output markers, the extraction model of adverse drug reactions based on BiLSTM and CRF is established. By analyzing the structural data of FAERS, we propose to classify the characteristics according to the differences of drug molecular structure, and construct a multicore function pool. The simulation structure shows that the model can help doctors reduce the risk of potential adverse reactions when prescribing, and assist the adverse drug reaction monitoring institutions to detect the potential adverse drug reactions in advance.