从自杀和自残监测报告中发现故意自残模式。

IF 2.3 Q3 MEDICAL INFORMATICS
Healthcare Informatics Research Pub Date : 2022-10-01 Epub Date: 2022-10-31 DOI:10.4258/hir.2022.28.4.319
Vuttichai Vichianchai, Sumonta Kasemvilas
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引用次数: 0

摘要

目的:本研究的目的是从泰国的自杀和自残监测报告中确定自残风险因素的模式。方法:本研究分析了提交给泰国Khon Kaen Rajanagarindra精神病院的自杀和自残监测报告的数据。识别自残风险因素模式的过程包括:数据预处理(即数据准备和清理、使用列表删除和期望最大化技术管理缺失数据、因子分组、确定目标因子、数据相关性学习);使用支持向量机、随机森林、多层感知器、决策树、k近邻和集成技术的10倍交叉验证对自残风险(严重或轻度)进行分类;数据过滤;利用分类与回归树(CART)技术进行10倍交叉验证,识别自残风险因素模式;评估自残风险因素的模式。结果:随机森林法对自残风险分类最准确,特异性为92.84%,敏感性为93.12%,f值为91.46%。CART技术能够识别53种自残风险模式,其中16种为重度自残风险模式,37种为轻度自残风险模式,准确率为92.85%。此外,我们发现医院类型是严重自残的一个新的风险因素。结论:本文提出的程序可以识别自残风险因素的模式,并帮助精神科医生在泰国医院就诊的患者中做出与自残有关的决定。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Discovery of Intentional Self-Harm Patterns from Suicide and Self-Harm Surveillance Reports.

Discovery of Intentional Self-Harm Patterns from Suicide and Self-Harm Surveillance Reports.

Discovery of Intentional Self-Harm Patterns from Suicide and Self-Harm Surveillance Reports.

Objectives: The purpose of this study was to identify patterns of self-harm risk factors from suicide and self-harm surveillance reports in Thailand.

Methods: This study analyzed data from suicide and self-harm surveillance reports submitted to Khon Kaen Rajanagarindra Psychiatric Hospital, Thailand. The process of identifying patterns of self-harm risk factors involved: data preprocessing (namely, data preparation and cleaning, missing data management using listwise deletion and expectation-maximization techniques, subgrouping factors, determining the target factors, and data correlation for learning); classifying the risk of self-harm (severe or mild) using 10-fold cross-validation with the support vector machine, random forest, multilayer perceptron, decision tree, k-nearest neighbors, and ensemble techniques; data filtering; identifying patterns of self-harm risk factors using 10-fold cross-validation with the classification and regression trees (CART) technique; and evaluating patterns of self-harm risk factors.

Results: The random forest technique was most accurate for classifying the risk of self-harm, with specificity, sensitivity, and F-score of 92.84%, 93.12%, and 91.46%, respectively. The CART technique was able to identify 53 patterns of self-harm risk, consisting of 16 severe self-harm risk patterns and 37 mild self-harm risk patterns, with an accuracy of 92.85%. In addition, we discovered that the type of hospital was a new risk factor for severe selfharm.

Conclusions: The procedure presented herein could identify patterns of risk factors from self-harm and assist psychiatrists in making decisions related to self-harm among patients visiting hospitals in Thailand.

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来源期刊
Healthcare Informatics Research
Healthcare Informatics Research MEDICAL INFORMATICS-
CiteScore
4.90
自引率
6.90%
发文量
44
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