期刊
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
卷 18, 期 19, 页码 -出版社
MDPI
DOI: 10.3390/ijerph181910150
关键词
suicide; the elderly; health screening cohort; machine learning; mental health
资金
- Ministry of Science and ICT in the Republic of Korea [NRF-2019R1F1A1049662]
- Gachon University Gil Medical Center [FRD2019-02-02]
This study developed a machine learning model for predicting suicide risk among the elderly based on a large health screening cohort. The findings showed that individuals who died by suicide were older, predominantly male, had a history of depression, and higher medication usage. Additionally, the suicide group had lower levels of certain biological markers.
Suicide poses a serious problem globally, especially among the elderly population. To tackle the issue, this study aimed to develop a model for predicting suicide by using machine learning based on the elderly population. To obtain a large sample, the study used the big data health screening cohort provided by the National Health Insurance Sharing Service. By applying a machine learning technique, a predictive model that comprehensively utilized various factors was developed to select the elderly aged > 65 years at risk of suicide. A total of 48,047 subjects were included in the analysis. Individuals who died by suicide were older, and the number of men was significantly greater. The suicide group had a more prominent history of depression, with the use of medicaments significantly higher. Specifically, the prescription of benzodiazepines alone was associated with a high suicide risk. Furthermore, body mass index, waist circumference, total cholesterol, and low-density lipoprotein level were lower in the suicide group. We developed a model for predicting suicide by using machine learning based on the elderly population. This suicide prediction model can satisfy the performance to some extent by employing only the medical service usage behavior without subjective reports.
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