Medical-Level Suicide Risk Analysis: A Novel Standard and Evaluation Model

The frequent occurrence of suicides in modern society constitutes a serious public health issue. While the motives, methods, and consequences of suicide are quite complicated, if people at risk of suicide can be identified and intervened in time, the loss of life can be reduced. Through analyses based on combining a large number of suicide texts and professional medical literature, a dictionary of potential suicide risk impact factors has been established in this article. Based on this dictionary, a novel medical-level suicide risk standard is proposed to monitor suicide risk from point-to-surface under the timeline baseline. In order to solve the problem of insufficient Chinese suicide data sets, the manually assisted method based on knowledge perception is adopted to annotate the data set with corresponding to risk level. At the same time, a Bert evaluation model based on knowledge perception was established for the classification of risk level. The experimental results showed that proposed method has a 56% recognition accuracy in the prediction of 10-Label suicide risk level proposed in this article, and the classification performance is better than traditional machine learning algorithms. Therefore, the results showed that the classification standard and evaluation model can be effectively used for the identification and early warning of suicide risk, which can discover high suicide risk groups to reduce the occurrence of suicide. It is of great significance to people's emotion care monitoring.

Automated summary

The study established a dictionary of potential suicide risk impact factors and proposed a new medical-level suicide risk standard for monitoring suicide risk effectively. By adopting a manual-assisted method and a Bert evaluation model, the data set was annotated and classified, addressing the issue of insufficient suicide data sets. Experimental results demonstrated a 56% recognition accuracy in predicting the 10-label suicide risk level, with better classification performance than traditional machine learning algorithms. This classification standard and evaluation model can be effectively utilized for identifying and providing early warnings for suicide risk, ultimately reducing the occurrence of suicide and playing a significant role in emotional care monitoring for individuals.