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
{"title":"从自杀和自残监测报告中发现故意自残模式。","authors":"Vuttichai Vichianchai,&nbsp;Sumonta Kasemvilas","doi":"10.4258/hir.2022.28.4.319","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>The purpose of this study was to identify patterns of self-harm risk factors from suicide and self-harm surveillance reports in Thailand.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/e9/d3/hir-2022-28-4-319.PMC9672490.pdf","citationCount":"0","resultStr":"{\"title\":\"Discovery of Intentional Self-Harm Patterns from Suicide and Self-Harm Surveillance Reports.\",\"authors\":\"Vuttichai Vichianchai,&nbsp;Sumonta Kasemvilas\",\"doi\":\"10.4258/hir.2022.28.4.319\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objectives: </strong>The purpose of this study was to identify patterns of self-harm risk factors from suicide and self-harm surveillance reports in Thailand.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>\",\"PeriodicalId\":12947,\"journal\":{\"name\":\"Healthcare Informatics Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2022-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/e9/d3/hir-2022-28-4-319.PMC9672490.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Healthcare Informatics Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4258/hir.2022.28.4.319\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2022/10/31 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q3\",\"JCRName\":\"MEDICAL INFORMATICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Healthcare Informatics Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4258/hir.2022.28.4.319","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2022/10/31 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"MEDICAL INFORMATICS","Score":null,"Total":0}
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。

摘要图片

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Healthcare Informatics Research
Healthcare Informatics Research MEDICAL INFORMATICS-
CiteScore
4.90
自引率
6.90%
发文量
44
期刊最新文献
Status and Trends of the Digital Healthcare Industry. Technology and Access to Healthcare with Different Scheduling Systems: A Scoping Review. Associations between Nicotine Dependence, Smartphone Usage Patterns, and Expected Compliance with a Smoking Cessation Application among Smokers. ChatGPT Predicts In-Hospital All-Cause Mortality for Sepsis: In-Context Learning with the Korean Sepsis Alliance Database. Data Market-related Issues in the Medical Field: Accelerating Digital Healthcare.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1