172 related articles for article (PubMed ID: 32213803)
1. Prediction of Suicidal Ideation among Korean Adults Using Machine Learning: A Cross-Sectional Study.
Oh B; Yun JY; Yeo EC; Kim DH; Kim J; Cho BJ
Psychiatry Investig; 2020 Apr; 17(4):331-340. PubMed ID: 32213803
[TBL] [Abstract][Full Text] [Related]
2. Prediction models for high risk of suicide in Korean adolescents using machine learning techniques.
Jung JS; Park SJ; Kim EY; Na KS; Kim YJ; Kim KG
PLoS One; 2019; 14(6):e0217639. PubMed ID: 31170212
[TBL] [Abstract][Full Text] [Related]
3. An Autoencoder and Machine Learning Model to Predict Suicidal Ideation with Brain Structural Imaging.
Weng JC; Lin TY; Tsai YH; Cheok MT; Chang YE; Chen VC
J Clin Med; 2020 Feb; 9(3):. PubMed ID: 32121362
[TBL] [Abstract][Full Text] [Related]
4. Identifying the suicidal ideation risk group among older adults in rural areas: Developing a predictive model using machine learning methods.
Kim J; Gwak D; Kim S; Gang M
J Adv Nurs; 2023 Feb; 79(2):641-651. PubMed ID: 36534434
[TBL] [Abstract][Full Text] [Related]
5. Machine learning prediction of suicidal ideation, planning, and attempt among Korean adults: A population-based study.
Lee J; Pak TY
SSM Popul Health; 2022 Sep; 19():101231. PubMed ID: 36263295
[TBL] [Abstract][Full Text] [Related]
6. Application of machine learning approaches for osteoporosis risk prediction in postmenopausal women.
Shim JG; Kim DW; Ryu KH; Cho EA; Ahn JH; Kim JI; Lee SH
Arch Osteoporos; 2020 Oct; 15(1):169. PubMed ID: 33097976
[TBL] [Abstract][Full Text] [Related]
7. Risk factors of suicide attempt among people with suicidal ideation in South Korea: a cross-sectional study.
Choi SB; Lee W; Yoon JH; Won JU; Kim DW
BMC Public Health; 2017 Jun; 17(1):579. PubMed ID: 28619107
[TBL] [Abstract][Full Text] [Related]
8. Linguistic Analysis for Identifying Depression and Subsequent Suicidal Ideation on Weibo: Machine Learning Approaches.
Pan W; Wang X; Zhou W; Hang B; Guo L
Int J Environ Res Public Health; 2023 Feb; 20(3):. PubMed ID: 36768053
[TBL] [Abstract][Full Text] [Related]
9. Association between Suicidal Ideation and Causes of Primary Care Visits: A Cross-Sectional Study to Identify Patients with Suspected Suicidal Ideation.
Lee G; Kim JH
Psychiatry Investig; 2020 Jul; 17(7):667-673. PubMed ID: 32571001
[TBL] [Abstract][Full Text] [Related]
10. Prediction Models for Suicide Attempts among Adolescents Using Machine Learning Techniques.
Lim JS; Yang CM; Baek JW; Lee SY; Kim BN
Clin Psychopharmacol Neurosci; 2022 Nov; 20(4):609-620. PubMed ID: 36263637
[TBL] [Abstract][Full Text] [Related]
11. Predicting suicidal ideation in primary care: An approach to identify easily assessable key variables.
Jordan P; Shedden-Mora MC; Löwe B
Gen Hosp Psychiatry; 2018; 51():106-111. PubMed ID: 29428582
[TBL] [Abstract][Full Text] [Related]
12. Prediction of suicidal ideation in shift workers compared to non-shift workers using machine learning techniques.
Park H; Lee K
J Affect Disord; 2022 Jun; 307():125-132. PubMed ID: 35390350
[TBL] [Abstract][Full Text] [Related]
13. Influence of parental stress, depressed mood, and suicidal ideation on adolescents' suicidal ideation : The 2008-2013 Korean National Health and Nutrition Examination Survey.
Lee YJ; Lee SI; Han K
J Affect Disord; 2019 Mar; 246():571-577. PubMed ID: 30605875
[TBL] [Abstract][Full Text] [Related]
14. Using Machine Learning Algorithms to Enhance the Management of Suicide Ideation.
Colic S; J Richardson D; James Reilly P; Gary Hasey M
Annu Int Conf IEEE Eng Med Biol Soc; 2018 Jul; 2018():4936-4939. PubMed ID: 30441450
[TBL] [Abstract][Full Text] [Related]
15. Osteoporosis risk prediction using machine learning and conventional methods.
Kim SK; Yoo TK; Oh E; Kim DW
Annu Int Conf IEEE Eng Med Biol Soc; 2013; 2013():188-91. PubMed ID: 24109656
[TBL] [Abstract][Full Text] [Related]
16. Use of Non-invasive Parameters and Machine-Learning Algorithms for Predicting Future Risk of Type 2 Diabetes: A Retrospective Cohort Study of Health Data From Kuwait.
Farran B; AlWotayan R; Alkandari H; Al-Abdulrazzaq D; Channanath A; Thanaraj TA
Front Endocrinol (Lausanne); 2019; 10():624. PubMed ID: 31572303
[No Abstract] [Full Text] [Related]
17. Mental health service use in adults with suicidal ideation within a nationally representative sample of the Korean population.
Han KM; Won E; Paik JW; Lee MS; Lee HW; Ham BJ
J Affect Disord; 2016 Mar; 193():339-47. PubMed ID: 26796234
[TBL] [Abstract][Full Text] [Related]
18. Prediction of adolescent suicidal ideation after the COVID-19 pandemic: A nationwide survey of a representative sample of Korea.
Byeon H
Front Pediatr; 2022; 10():951439. PubMed ID: 35958177
[TBL] [Abstract][Full Text] [Related]
19. Association between high sensitivity CRP and suicidal ideation in the Korean general population.
Park RJ; Kim YH
Eur Neuropsychopharmacol; 2017 Sep; 27(9):885-891. PubMed ID: 28663123
[TBL] [Abstract][Full Text] [Related]
20. The impact of health-related quality of life on suicidal ideation and suicide attempts among Korean older adults.
Kim JH; Kwon JW
J Gerontol Nurs; 2012 Nov; 38(11):48-59. PubMed ID: 23066679
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
