scholarly journals Gender-specific factors related to suicidal ideation among community-dwelling stroke survivors: The 2013 Korean Community Health Survey

PLoS ONE ◽  
2018 ◽  
Vol 13 (8) ◽  
pp. e0201717 ◽  
Author(s):  
Mina Kim ◽  
Young-Hoon Lee
Keyword(s):  
Suicidal Ideation ◽  
Community Health ◽  
Health Survey ◽  
Community Dwelling ◽  
Stroke Survivors ◽  
Community Health Survey ◽  
Specific Factors ◽  
Gender Specific

Mo1120 Suicidal Ideation and Attempts in Cancer Survivors: Community Health Survey 2008

2013 ◽  
Vol 144 (5) ◽  
pp. S-583
Author(s):  
Mi Ah Han ◽  
Myueng Guen Oh ◽  
Jong Park ◽  
So Yeon Ryu ◽  
Seong Woo Choi
Keyword(s):  
Suicidal Ideation ◽  
Cancer Survivors ◽  
Community Health ◽  
Health Survey ◽  
Community Health Survey

scholarly journals Prediction of Suicidal Ideation in the Canadian Community Health Survey - Mental Health Component Using Deep Learning

2019 ◽  
Author(s):  
Sneha Desai ◽  
Myriam Tanguay-Sela ◽  
David Benrimoh ◽  
Robert Fratila ◽  
Eleanor Brown ◽  
...  
Keyword(s):  
Mental Health ◽  
Deep Learning ◽  
Suicidal Ideation ◽  
Community Health ◽  
Health Survey ◽  
Canadian Community Health Survey ◽  
Feature Model ◽  
Model Parameters ◽  
Community Health Survey ◽  
Patients At Risk

AbstractIntroductionSuicidal ideation (SI) is prevalent in the general population, and is a prominent risk factor for suicide. However, predicting which patients are likely to have SI remains a challenge. Deep Learning (DL) may be a useful tool in this context, as it can be used to find patterns in complex, heterogeneous, and incomplete psychiatric datasets. An automated screening system for SI could help prompt clinicians to be more attentive to patients at risk for suicide.MethodsUsing the Canadian Community Health Survey - Mental Health Component, we trained a DL model based on 23,859 survey responses to predict lifetime SI on an individual patient basis. Models were created to predict both lifetime and last 12 month SI. We reduced 582 possible model parameters captured by the survey to 96 and 21 feature versions of the models. Models were trained using an undersampling procedure that balanced the training set between SI and non-SI respondents; validation was done on held-out data.ResultsAUC was used as the main model metric. For lifetime SI, the 96 feature model had an AUC of 0.79 and the 21 feature model had an AUC of 0.75. For SI in the last 12 months the 96 feature model had an AUC of 0.76 and the 21 feature model had an AUC of 0.69. DL outperformed random forest classifiers.DiscussionAlthough requiring further study to ensure clinical relevance and sample generalizability, this study is a proof-of-concept for the use of DL to improve prediction of SI. This kind of model would help start conversations with patients which could lead to improved care and, it is hoped, a reduction in suicidal behavior.

scholarly journals Identification of Suicidal Ideation in the Canadian Community Health Survey—Mental Health Component Using Deep Learning

2021 ◽  
Vol 4 ◽  
Author(s):  
Sneha Desai ◽  
Myriam Tanguay-Sela ◽  
David Benrimoh ◽  
Robert Fratila ◽  
Eleanor Brown ◽  
...  
Keyword(s):  
Mental Health ◽  
Deep Learning ◽  
Suicidal Ideation ◽  
Community Health ◽  
Health Survey ◽  
Canadian Community Health Survey ◽  
Feature Model ◽  
Sensitivity Analyses ◽  
Receiver Operating Curve ◽  
Community Health Survey

Introduction: Suicidal ideation (SI) is prevalent in the general population, and is a risk factor for suicide. Predicting which patients are likely to have SI remains challenging. Deep Learning (DL) may be a useful tool in this context, as it can be used to find patterns in complex, heterogeneous, and incomplete datasets. An automated screening system for SI could help prompt clinicians to be more attentive to patients at risk for suicide.Methods: Using the Canadian Community Health Survey—Mental Health Component, we trained a DL model based on 23,859 survey responses to classify patients with and without SI. Models were created to classify both lifetime SI and SI over the last 12 months. From 582 possible parameters we produced 96- and 21-feature versions of the models. Models were trained using an undersampling procedure that balanced the training set between SI and non-SI; validation was done on held-out data.Results: For lifetime SI, the 96 feature model had an Area under the receiver operating curve (AUC) of 0.79 and the 21 feature model had an AUC of 0.77. For SI in the last 12 months the 96 feature model had an AUC of 0.71 and the 21 feature model had an AUC of 0.68. In addition, sensitivity analyses demonstrated feature relationships in line with existing literature.Discussion: Although further study is required to ensure clinical relevance and sample generalizability, this study is an initial proof of concept for the use of DL to improve identification of SI. Sensitivity analyses can help improve the interpretability of DL models. This kind of model would help start conversations with patients which could lead to improved care and a reduction in suicidal behavior.

scholarly journals Individual and regional factors associated with suicidal ideation among Korean elderly: a multilevel analysis of the Korea Community Health Survey

2019 ◽  
Vol 41 ◽  
pp. e2019022 ◽  
Author(s):  
Sang Hee Jeong ◽  
Byung Chul Chun
Keyword(s):  
Suicidal Ideation ◽  
Community Health ◽  
Multilevel Analysis ◽  
Health Survey ◽  
Personal Factors ◽  
Subjective Health Status ◽  
Community Factors ◽  
Community Health Survey ◽  
Regional Deprivation ◽  
Korean Elderly

OBJECTIVES: This study aimed to identify the individual and regional characteristics that influence suicidal ideation among the Korean elderly population.METHODS: Using data collected from the 2013 Korea Community Health Survey, a multilevel analysis was performed to establish an understanding of individual behavioral patterns and regional influences on suicidal ideation.RESULTS: Among the 77,407 individuals sampled, 11,236 (14.5%) elderly people over 60 years of age experienced suicidal ideation. Among individual factors, age, frequency of communication with friends, religious activity, social activity, leisure activity, trust in neighbors, subjective stress level, depressive symptoms, and subjective health status were significantly associated with suicidal ideation. The results showed that the lower the regional deprivation level, the higher the suicidal ideation odds ratio. In terms of regional size, the most significant effects were found in rural areas.CONCLUSIONS: This study suggested that suicidal ideation in the elderly is associated with community factors, such as the regional deprivation index, as well as personal factors.

Sign in / Sign up

Export Citation Format

Share Document