The Prediction of Alcohol Use Disorder: A Scoping Review The prediction of Alcohol Use Disorder (AUD) may help to alleviate the number of deaths caused by alcohol related diseases, which had amounted to 3.3 million in 2014, worldwide. This article reports on the results of a scoping review of literature which focused on the prediction of AUD. A search in the academic databases including Medline, Web of Science and EBSCOhost had identified 28 articles which were published from 1980 to 2018, and which fulfilled our inclusion criteria related to the prediction of AUD. The findings suggest that research focusing on the prediction of AUD has been solid, with majority of the investigations focusing on genetics and family history, and psychological factors. It was observed that no study had tried to extract the predictor variables of AUD from their collected samples. Further, while a few studies had applied the machine learning approach in this domain, most investigations were based on statistical methods. Our review also suggests that compared to other regions where the rate of harmful drinking and mortality caused by alcoholism is high, Denmark is a country that has been less explored.

Suicide Prediction Analysis with Generalized Addictive Model The issue of suicide rate has become increasingly rigorous and has received extensive attention in the contemporary society. In order to explore the pattern of suicide rate variation and intrinsic incentive for suicide, the research was conducted to analyze the data involved multifaceted factors since the suicide is rarely caused by only one factor. Establishing statistical models like xe2x80x9cGeneralized Additive Modelxe2x80x9d provides a conceptual framework for the study and is instrumental in predicting the suicide rate in 2017. Based on the past data from year 1985 to 2016, the prediction of suicide rate in 2017 is presented specifically. Multiple invisible results are revealed including the negative linear relationship between GDP and suicide rate, the positive correlation with age and the uneven distribution worldwide. These results presented with functions and graphs show how financial, cultural or regional factors might affect the suicide rate in different areas. The prediction are still affected by factors difficult to estimate, such as socioeconomic relations. However, the results are sufficient to help understand the suicide issue by analyzing itu0027s origin and distribution.