Citation

Wang R, Yang BX, Ma Y, Wang P, Yu Q, Zong X, Huang Z, Ma S, Hu L, Hwang K, Liu Z. IEEE Internet Things J. 2021; 8(23): 16825-16834.

Copyright

(Copyright © 2021, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/JIOT.2021.3052363

PMID

unavailable

Abstract

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. © 2014 IEEE.

Language: en

Keywords

Risk assessment; Risk analysis; Public health issues; Learning algorithms; Classification performance; Machine learning; Risk perception; Classification of risk; Classification standard; Early warning; Evaluation model; Evaluation modeling; knowledge perception; Medical literatures; Recognition accuracy; suicide risk standard