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识别自杀行为心理脆弱状态：应用于临床样本的人工智能贝叶斯网络。

Recognizing states of psychological vulnerability to suicidal behavior: a Bayesian network of artificial intelligence applied to a clinical sample.

机构信息

Psychiatry Department, School of Medicine, Pontificia Universidad Católica de Chile, La Reconquista 498, Las Condes, Santiago, Chile.

Millennium Institute for Research in Depression and Personality MIDAP, Santiago, Chile.

出版信息

BMC Psychiatry. 2020 Mar 30;20(1):138. doi: 10.1186/s12888-020-02535-x.

DOI:10.1186/s12888-020-02535-x
PMID:32228548
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7106600/
Abstract

BACKGROUND

This study aimed to determine conditional dependence relationships of variables that contribute to psychological vulnerability associated with suicide risk. A Bayesian network (BN) was developed and applied to establish conditional dependence relationships among variables for each individual subject studied. These conditional dependencies represented the different states that patients could experience in relation to suicidal behavior (SB). The clinical sample included 650 mental health patients with mood and anxiety symptomatology.

RESULTS

Mainly indicated that variables within the Bayesian network are part of each patient's state of psychological vulnerability and have the potential to impact such states and that these variables coexist and are relatively stable over time. These results have enabled us to offer a tool to detect states of psychological vulnerability associated with suicide risk.

CONCLUSION

If we accept that suicidal behaviors (vulnerability, ideation, and suicidal attempts) exist in constant change and are unstable, we can investigate what individuals experience at specific moments to become better able to intervene in a timely manner to prevent such behaviors. Future testing of the tool developed in this study is needed, not only in specialized mental health environments but also in other environments with high rates of mental illness, such as primary healthcare facilities and educational institutions.

摘要

背景

本研究旨在确定导致与自杀风险相关的心理脆弱性的变量的条件依存关系。开发了一个贝叶斯网络（BN），并将其应用于为每个研究的个体对象建立变量之间的条件依存关系。这些条件依赖关系代表了患者在自杀行为（SB）方面可能经历的不同状态。临床样本包括 650 名有情绪和焦虑症状的心理健康患者。

结果

主要表明，贝叶斯网络中的变量是每个患者心理脆弱状态的一部分，有可能影响这些状态，并且这些变量共存且随着时间的推移相对稳定。这些结果使我们能够提供一种工具来检测与自杀风险相关的心理脆弱状态。

结论

如果我们接受自杀行为（脆弱性、意念和自杀企图）处于不断变化和不稳定的状态，我们可以研究个体在特定时刻所经历的事情，以便更及时地进行干预，从而预防这些行为。未来需要对本研究中开发的工具进行测试，不仅在专门的心理健康环境中，而且在其他精神疾病发病率较高的环境中，如初级保健设施和教育机构。

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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4db/7106600/7d28659678c4/12888_2020_2535_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4db/7106600/b1847a5bb69f/12888_2020_2535_Fig1_HTML.jpg
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