Machine learning prediction of suicidal ideation, planning, and attempt among Korean adults: A population-based study

Lee, Jeongyoon; Pak, Tae‐Young

doi:10.1016/j.ssmph.2022.101231

Cited by 11 publications

(6 citation statements)

References 62 publications

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“…A study found that ML for suicide risk prediction in children and adolescents with electronic health records was able to detect 53% to 62% of suicide-positive participants with 90% specificity [ 27 ], and a case-control study of first-time suicide attempts with a cohort of >45,000 patients demonstrated accurate and robust first-time suicide attempt prediction [ 28 ], with the best predicting model achieving an AUROC of 0.932. A study that used the Korea Welfare Panel Study to develop an ML algorithm determined that >80% of individuals at risk of suicide-related behaviors could be predicted by various mental and socioeconomic characteristics of the respondents [ 29 ]. In addition, ML together with in-person screening has been found to result in the best suicide risk prediction [ 30 ], illustrating its potential to be used by clinicians in the medical field.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning–Based Prediction of Suicidality in Adolescents With Allergic Rhinitis: Derivation and Validation in 2 Independent Nationwide Cohorts

Lee,

Cho,

Park

et al. 2024

J Med Internet Res

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Background Given the additional risk of suicide-related behaviors in adolescents with allergic rhinitis (AR), it is important to use the growing field of machine learning (ML) to evaluate this risk. Objective This study aims to evaluate the validity and usefulness of an ML model for predicting suicide risk in patients with AR. Methods We used data from 2 independent survey studies, Korea Youth Risk Behavior Web-based Survey (KYRBS; n=299,468) for the original data set and Korea National Health and Nutrition Examination Survey (KNHANES; n=833) for the external validation data set, to predict suicide risks of AR in adolescents aged 13 to 18 years, with 3.45% (10,341/299,468) and 1.4% (12/833) of the patients attempting suicide in the KYRBS and KNHANES studies, respectively. The outcome of interest was the suicide attempt risks. We selected various ML-based models with hyperparameter tuning in the discovery and performed an area under the receiver operating characteristic curve (AUROC) analysis in the train, test, and external validation data. Results The study data set included 299,468 (KYRBS; original data set) and 833 (KNHANES; external validation data set) patients with AR recruited between 2005 and 2022. The best-performing ML model was the random forest model with a mean AUROC of 84.12% (95% CI 83.98%-84.27%) in the original data set. Applying this result to the external validation data set revealed the best performance among the models, with an AUROC of 89.87% (sensitivity 83.33%, specificity 82.58%, accuracy 82.59%, and balanced accuracy 82.96%). While looking at feature importance, the 5 most important features in predicting suicide attempts in adolescent patients with AR are depression, stress status, academic achievement, age, and alcohol consumption. Conclusions This study emphasizes the potential of ML models in predicting suicide risks in patients with AR, encouraging further application of these models in other conditions to enhance adolescent health and decrease suicide rates.

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Section: Discussionmentioning

confidence: 99%

Machine Learning–Based Prediction of Suicidality in Adolescents With Allergic Rhinitis: Derivation and Validation in 2 Independent Nationwide Cohorts

Lee,

Cho,

Park

et al. 2024

J Med Internet Res

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“…Four machine learning classifiers were used, with extreme gradient boosting showing superior performance. The research found that over 80% of individuals at risk for suicidal thoughts and planning could be accurately predicted [7]. The study uses machine learning and Natural Language Processing (NLP) techniques to analyze depression and suicide thoughts in Reddit comments and posts.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of naïve bayes, logistic regression and support vector machine for predicting suicidal tendency from social media content

Rai,

Prasun,

Sharma

et al. 2024

IJRP

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The objective of this study is to employ Machine Learning methodologies in order to forecast the chances of depression and suicide among individuals in Nepal by analyzing their social media engagement. The dataset consisting of 2200 entries was subjected to analysis using the Naïve Bayes, Logistic Regression, and Support Vector Machine techniques. The accuracy of the Support Vector Machine was found to be 95.45%. The timely identification of suicidal and depressive incidents had a crucial role in addressing depression and diminishing suicide rates. This study assesses the sensitivity and accuracy of various Machine Learning algorithms in the context of early and late detection.

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“…In a systematic review by Gualtieri et al (2018), home-delivering meal plans enhanced lower-class older adults' diet and eating habits in parts of the USA [144]. Meal plans also help decrease stress [145] and anxiety linked to accessing food and decrease social isolation as older adults are able to communicate with meal-plan delivery volunteers [146].…”

Section: Conclusion and Policy Recommendationsmentioning

confidence: 99%

The impact of food insecurity on mental health among older adults residing in low- and middle-income countries: A systematic review

Osei-Owusu,

Dhillon,

Luginaah

2024

PLoS ONE

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Over the past few years, food insecurity has been increasing globally due to the COVID-19 pandemic, climate change, economic downturns and conflict and a number of other intersecting factors. Older adults residing in low- and middle-income countries are more vulnerable to food insecurity. While the impacts of food insecurity on physical health outcomes have been thoroughly researched, the effect on mental health outcomes remains under-researched, especially among older adults. Hence, this systematic review aims to investigate existing literature to assess how food insecurity impacts the mental health of older persons residing in LMICs. A systematic search of six databases and Google for studies was conducted. The search was limited to studies written in English and published between 2000 to the present. We identified 725 studies, out of which 40 studies were selected for a full-text review and 12 studies were included for a final analysis. The significant finding in all the included studies was that food insecurity is associated with the worsening mental health of older adults. We also found a complex interplay of factors such as gender, age, rural/urban and health conditions associated with the aggravation of several mental health outcomes. The findings of this study illuminate the need for improved food programs to improve food security and, consequently, mental health among older adults.

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Machine learning prediction of suicidal ideation, planning, and attempt among Korean adults: A population-based study

Cited by 11 publications

References 62 publications

Machine Learning–Based Prediction of Suicidality in Adolescents With Allergic Rhinitis: Derivation and Validation in 2 Independent Nationwide Cohorts

Machine Learning–Based Prediction of Suicidality in Adolescents With Allergic Rhinitis: Derivation and Validation in 2 Independent Nationwide Cohorts

Comparison of naïve bayes, logistic regression and support vector machine for predicting suicidal tendency from social media content

The impact of food insecurity on mental health among older adults residing in low- and middle-income countries: A systematic review

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