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....
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| Format: | Article |
| Language: | English |
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The Korean Society of Medical Informatics
2022-10-01
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| Series: | Healthcare Informatics Research |
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| Online Access: | http://www.e-hir.org/upload/pdf/hir-2022-28-4-319.pdf |
| _version_ | 1811216210486886400 |
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| author | Vuttichai Vichianchai Sumonta Kasemvilas |
| author_facet | Vuttichai Vichianchai Sumonta Kasemvilas |
| author_sort | Vuttichai Vichianchai |
| collection | DOAJ |
| description | 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 self-harm. 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. |
| first_indexed | 2024-04-12T06:35:30Z |
| format | Article |
| id | doaj.art-2fecd5aa33404fd1ae70ea0637693ea2 |
| institution | Directory Open Access Journal |
| issn | 2093-3681 2093-369X |
| language | English |
| last_indexed | 2024-04-12T06:35:30Z |
| publishDate | 2022-10-01 |
| publisher | The Korean Society of Medical Informatics |
| record_format | Article |
| series | Healthcare Informatics Research |
| spelling | doaj.art-2fecd5aa33404fd1ae70ea0637693ea22022-12-22T03:43:53ZengThe Korean Society of Medical InformaticsHealthcare Informatics Research2093-36812093-369X2022-10-0128431933110.4258/hir.2022.28.4.3191135Discovery of Intentional Self-Harm Patterns from Suicide and Self-Harm Surveillance ReportsVuttichai Vichianchai0Sumonta Kasemvilas1Hardware-Human Interface and Communications (HI-Comm) Laboratory, College of Computing, Khon Kaen University, Khon Kaen, ThailandHardware-Human Interface and Communications (HI-Comm) Laboratory, College of Computing, Khon Kaen University, Khon Kaen, ThailandObjectives 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 self-harm. 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.http://www.e-hir.org/upload/pdf/hir-2022-28-4-319.pdfdata adjustmentmachine learningdata analysisself-injurious behaviorsuicide |
| spellingShingle | Vuttichai Vichianchai Sumonta Kasemvilas Discovery of Intentional Self-Harm Patterns from Suicide and Self-Harm Surveillance Reports Healthcare Informatics Research data adjustment machine learning data analysis self-injurious behavior suicide |
| title | Discovery of Intentional Self-Harm Patterns from Suicide and Self-Harm Surveillance Reports |
| title_full | Discovery of Intentional Self-Harm Patterns from Suicide and Self-Harm Surveillance Reports |
| title_fullStr | Discovery of Intentional Self-Harm Patterns from Suicide and Self-Harm Surveillance Reports |
| title_full_unstemmed | Discovery of Intentional Self-Harm Patterns from Suicide and Self-Harm Surveillance Reports |
| title_short | Discovery of Intentional Self-Harm Patterns from Suicide and Self-Harm Surveillance Reports |
| title_sort | discovery of intentional self harm patterns from suicide and self harm surveillance reports |
| topic | data adjustment machine learning data analysis self-injurious behavior suicide |
| url | http://www.e-hir.org/upload/pdf/hir-2022-28-4-319.pdf |
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