Drug Abuse Research Trend Investigation with Text Mining Drug abuse poses great physical and psychological harm to humans, thereby attracting scholarly attention. It often requires experience and time for a researcher, just entering this field, to find an appropriate method to study drug abuse issue. It is crucial for researchers to rapidly understand the existing research on a particular topic and be able to propose an effective new research method. Text mining analysis has been widely applied in recent years, and this study integrated the text mining method into a review of drug abuse research. Through searches for keywords related to the drug abuse, all related publications were identified and downloaded from PubMed. After removing the duplicate and incomplete literature, the retained data were imported for analysis through text mining. A total of 19,843 papers were analyzed, and the text mining technique was used to search for keyword and questionnaire types. The results showed the associations between these questionnaires, with the top five being the Addiction Severity Index (16.44%), the Quality of Life survey (5.01%), the Beck Depression Inventory (3.24%), the Addiction Research Center Inventory (2.81%), and the Profile of Mood States (1.10%). Specifically, the Addiction Severity Index was most commonly used in combination with Quality of Life scales. In conclusion, association analysis is useful to extract core knowledge. Researchers can learn and visualize the latest research trend.

Drug Abuse Prediction Model Based on Relevance Analysis Based on the data sets of drug use and socioeconomic data from five states in the United States, this paper proposes a prediction model based on correlation analysis. Firstly, the indicators are screened according to the correlation degree, then the degree of drug abuse and the selected indicators are predicted by multiple regression. Secondly, the weighted polynomial regression algorithm is used to predict the degree of drug abuse separately. Finally, the Lagrange multiplier method is used to assign the optimal weight to the two prediction results. The experimental results show that the prediction effect of the model is much better than that of the single prediction result of the target index.