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采用多方法途径对自杀及自杀未遂的描述

Suicide and Suicide Attempt Descriptors by Multimethod Approach.

作者信息

Zalar Bojan, Kores Plesničar Blanka, Zalar Ina, Mertik Matej

机构信息

University Psychiatric Clinic Ljubljana, Studenec 48, 1260 Ljubljana, Slovenia,

出版信息

Psychiatr Danub. 2018 Sep;30(3):317-322. doi: 10.24869/psyd.2018.317.

Abstract

BACKGROUND

Suicide is a complex action of suicidal methods and peripheral factors with seemingly threatening components representing actual cause for the suicidal actions. It is especially those, apparently unimportant factors that represent a crucial milestone in the network of all the other, personal, cultural, genetic and biochemical factors, forming the method of action consequently deciding between life and death.

SUBJECTS AND METHODS

Based on the Register of Suicides in the Republic of Slovenia kept by the University Psychiatric Clinic Ljubljana, we used a combination of attributes varying within a variable and between variables. Due to limited application of standard statistical methods and analyses in such cases, we used the Machine learning method, Multimethod hybrid approach, which allows combining of different approaches to machine learning (decision trees, genetic algorithms and supplementary vectors). The research included 56712 persons attempting suicide and 21913 persons committing suicide. We chose a form of a suicide action with both possible results: attempted suicide and suicide.

RESULTS

Based on the analysis of machine learning, we defined attributes of the action regarding their lethal effect: attempted suicide and suicide commitment. The suicide register kept for the last 40 years shows hanging as the most commonly used suicidal method, used by men with the purpose of causing suicidal death rather than a suicidal attempt. On the other hand, use of medicaments is linked to the suicidal attempt and mostly used by females.

CONCLUSIONS

All methods of suicidal actions cannot predict suicidal death, thus we examined different methods of suicide to most accurately predict the link between the method and its effect in terms of suicide attempt or suicide. The Machine learning method confirmed the attributes of suicide methods in connection with their different outcomes. This analytical method is useful in processing large databases since it enables one variable's intensity to affect other variables in terms of result and meaning. The identification of the most decisive risk factors for suicidal behaviour can serve as basis for planning an effective prevention strategies, timely identification and adequate proffessional help to the high risk persons.

摘要

背景

自杀是一种涉及自杀方式和周边因素的复杂行为,其中看似具有威胁性的因素代表了自杀行为的实际成因。尤其是那些看似无关紧要的因素,在所有其他个人、文化、遗传和生化因素构成的网络中,却是一个关键的转折点,进而形成决定生死的行动方式。

对象与方法

基于卢布尔雅那大学精神病诊所保存的斯洛文尼亚共和国自杀登记册,我们使用了变量内部和变量之间变化的属性组合。由于在这种情况下标准统计方法和分析的应用有限,我们采用了机器学习方法——多方法混合方法,该方法允许结合不同的机器学习方法(决策树、遗传算法和支持向量)。研究包括56712名自杀未遂者和21913名自杀身亡者。我们选择了一种具有两种可能结果的自杀行为形式:自杀未遂和自杀。

结果

基于机器学习分析,我们根据其致命效果定义了该行为的属性:自杀未遂和自杀身亡。过去40年保存的自杀登记册显示,上吊是最常用的自杀方式,男性使用这种方式的目的是导致自杀死亡而非自杀未遂。另一方面,使用药物与自杀未遂有关,且大多为女性使用。

结论

所有自杀行为方式都无法预测自杀死亡,因此我们研究了不同的自杀方式,以最准确地预测自杀方式与其在自杀未遂或自杀方面的效果之间的联系。机器学习方法证实了自杀方式与其不同结果相关的属性。这种分析方法在处理大型数据库时很有用,因为它能使一个变量的强度在结果和意义方面影响其他变量。识别自杀行为最具决定性的风险因素可为规划有效的预防策略、及时识别高危人群并给予适当的专业帮助提供依据。

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