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Publication
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| Title of Article |
Discovery of Intentional Self-Harm Patterns from Suicide and Self-Harm Surveillance Reports |
| Date of Acceptance |
7 September 2022 |
| Journal |
| Title of Journal |
Healthcare Informatics Research |
| Standard |
ISI |
| Institute of Journal |
Korean Society of Medical Informatics |
| ISBN/ISSN |
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| Volume |
2022 |
| Issue |
Volume 28 Issue 4 |
| Month |
October |
| Year of Publication |
2022 |
| Page |
319-331 |
| Abstract |
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. |
| Keyword |
Data Adjustment, Machine Learning, Data Analysis, Self-Injurious Behavior, Suicide |
| Author |
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| Reviewing Status |
มีผู้ประเมินอิสระ |
| Status |
ตีพิมพ์แล้ว |
| Level of Publication |
นานาชาติ |
| citation |
false |
| Part of thesis |
true |
| ใช้สำหรับสำเร็จการศึกษา |
ไม่เป็น |
| Attach file |
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| Citation |
0
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Journal Publication
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