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KMID : 1100520220280040319
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Healthcare Informatics Research
2022 Volume.28 No. 4 p.319 ~ p.331
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Discovery of Intentional Self-Harm Patterns from Suicide and Self-Harm Surveillance Reports
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Vichianchai Vuttichai
Kasemvilas Sumonta
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Abstract
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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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KEYWORD
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Data Adjustment, Machine Learning, Data Analysis, Self-Injurious Behavior, Suicide
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