Open AccessIdentifying the Medical Lethality of Suicide Attempts Using Network Analysis and Deep Learning: Nationwide Study
Author(s) -
Bora Kim,
Young-Hoon Kim,
C. Hyung Keun Park,
Sang Jin Rhee,
Young S. Kim,
Bennett L. Leventhal,
Yong Min Ahn,
Hyojung Paik
Publication year - 2020
Publication title -
jmir medical informatics
Language(s) - English
Resource type - Journals
ISSN - 2291-9694
DOI - 10.2196/14500
Subject(s) - antecedent (behavioral psychology) , suicide methods , suicide prevention , poison control , injury prevention , human factors and ergonomics , suicide attempt , lethality , psychology , medicine , clinical psychology , psychiatry , medical emergency , developmental psychology , biology , suicide rates , genetics
Background Suicide is one of the leading causes of death among young and middle-aged people. However, little is understood about the behaviors leading up to actual suicide attempts and whether these behaviors are specific to the nature of suicide attempts. Objective The goal of this study was to examine the clusters of behaviors antecedent to suicide attempts to determine if they could be used to assess the potential lethality of the attempt. To accomplish this goal, we developed a deep learning model using the relationships among behaviors antecedent to suicide attempts and the attempts themselves. Methods This study used data from the Korea National Suicide Survey. We identified 1112 individuals who attempted suicide and completed a psychiatric evaluation in the emergency room. The 15-item Beck Suicide Intent Scale (SIS) was used for assessing antecedent behaviors, and the medical outcomes of the suicide attempts were measured by assessing lethality with the Columbia Suicide Severity Rating Scale (C-SSRS; lethal suicide attempt >3 and nonlethal attempt ≤3). Results Using scores from the SIS, individuals who had lethal and nonlethal attempts comprised two different network nodes with the edges representing the relationships among nodes. Among the antecedent behaviors, the conception of a method’s lethality predicted suicidal behaviors with severe medical outcomes. The vectorized relationship values among the elements of antecedent behaviors in our deep learning model (E-GONet) increased performances, such as F1 and area under the precision-recall gain curve (AUPRG), for identifying lethal attempts (up to 3% for F1 and 32% for AUPRG), as compared with other models (mean F1: 0.81 for E-GONet, 0.78 for linear regression, and 0.80 for random forest; mean AUPRG: 0.73 for E-GONet, 0.41 for linear regression, and 0.69 for random forest). Conclusions The relationships among behaviors antecedent to suicide attempts can be used to understand the suicidal intent of individuals and help identify the lethality of potential suicide attempts. Such a model may be useful in prioritizing cases for preventive intervention.