Healthc Inform Res.  2022 Oct;28(4):319-331. 10.4258/hir.2022.28.4.319.

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

• Vichianchai V
• Kasemvilas S

• KMID: 2535807
• DOI: http://doi.org/10.4258/hir.2022.28.4.319

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

Figure

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  Figure 1 Conceptual framework of this research. LD: listwise deletion, EM: expectation-maximization, ML: machine learning, DT: decision tree, RF: random forest, SVM: support vector machine, MLP: multilayer perceptron, kNN: k-nearest neighbor, CART: classification and regression trees.

• 

  Figure 2 Example of a decision tree from the CART technique. CART: classification and regression trees.

Reference

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