171 related articles for article (PubMed ID: 30301301)
1. Use of a Machine Learning Algorithm to Predict Individuals with Suicide Ideation in the General Population.
Ryu S; Lee H; Lee DK; Park K
Psychiatry Investig; 2018 Nov; 15(11):1030-1036. PubMed ID: 30301301
[TBL] [Abstract][Full Text] [Related]
2. Detection of Suicide Attempters among Suicide Ideators Using Machine Learning.
Ryu S; Lee H; Lee DK; Kim SW; Kim CE
Psychiatry Investig; 2019 Aug; 16(8):588-593. PubMed ID: 31446686
[TBL] [Abstract][Full Text] [Related]
3. Machine Learning-Based Prediction of Suicidality in Adolescents With Allergic Rhinitis: Derivation and Validation in 2 Independent Nationwide Cohorts.
Lee H; Cho JK; Park J; Lee H; Fond G; Boyer L; Kim HJ; Park S; Cho W; Lee H; Lee J; Yon DK
J Med Internet Res; 2024 Feb; 26():e51473. PubMed ID: 38354043
[TBL] [Abstract][Full Text] [Related]
4. Prediction of suicide among 372,813 individuals under medical check-up.
Cho SE; Geem ZW; Na KS
J Psychiatr Res; 2020 Dec; 131():9-14. PubMed ID: 32906052
[TBL] [Abstract][Full Text] [Related]
5. Detecting risk of suicide attempts among Chinese medical college students using a machine learning algorithm.
Shen Y; Zhang W; Chan BSM; Zhang Y; Meng F; Kennon EA; Wu HE; Luo X; Zhang X
J Affect Disord; 2020 Aug; 273():18-23. PubMed ID: 32421600
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. Prediction of Suicidal Behaviors in the Middle-aged Population: Machine Learning Analyses of UK Biobank.
Wang J; Qiu J; Zhu T; Zeng Y; Yang H; Shang Y; Yin J; Sun Y; Qu Y; Valdimarsdóttir UA; Song H
JMIR Public Health Surveill; 2023 Feb; 9():e43419. PubMed ID: 36805366
[TBL] [Abstract][Full Text] [Related]
8. Prediction of myopia development among Chinese school-aged children using refraction data from electronic medical records: A retrospective, multicentre machine learning study.
Lin H; Long E; Ding X; Diao H; Chen Z; Liu R; Huang J; Cai J; Xu S; Zhang X; Wang D; Chen K; Yu T; Wu D; Zhao X; Liu Z; Wu X; Jiang Y; Yang X; Cui D; Liu W; Zheng Y; Luo L; Wang H; Chan CC; Morgan IG; He M; Liu Y
PLoS Med; 2018 Nov; 15(11):e1002674. PubMed ID: 30399150
[TBL] [Abstract][Full Text] [Related]
9. A machine-learning model to predict suicide risk in Japan based on national survey data.
Chou PH; Wang SC; Wu CS; Horikoshi M; Ito M
Front Psychiatry; 2022; 13():918667. PubMed ID: 35990064
[TBL] [Abstract][Full Text] [Related]
10. A Machine-Learning-Based Prediction Method for Hypertension Outcomes Based on Medical Data.
Chang W; Liu Y; Xiao Y; Yuan X; Xu X; Zhang S; Zhou S
Diagnostics (Basel); 2019 Nov; 9(4):. PubMed ID: 31703364
[TBL] [Abstract][Full Text] [Related]
11. Testing Suicide Risk Prediction Algorithms Using Phone Measurements With Patients in Acute Mental Health Settings: Feasibility Study.
Haines-Delmont A; Chahal G; Bruen AJ; Wall A; Khan CT; Sadashiv R; Fearnley D
JMIR Mhealth Uhealth; 2020 Jun; 8(6):e15901. PubMed ID: 32442152
[TBL] [Abstract][Full Text] [Related]
12. Derivation and validation of different machine-learning models in mortality prediction of trauma in motorcycle riders: a cross-sectional retrospective study in southern Taiwan.
Kuo PJ; Wu SC; Chien PC; Rau CS; Chen YC; Hsieh HY; Hsieh CH
BMJ Open; 2018 Jan; 8(1):e018252. PubMed ID: 29306885
[TBL] [Abstract][Full Text] [Related]
13. Classification of suicide attempters in schizophrenia using sociocultural and clinical features: A machine learning approach.
Hettige NC; Nguyen TB; Yuan C; Rajakulendran T; Baddour J; Bhagwat N; Bani-Fatemi A; Voineskos AN; Mallar Chakravarty M; De Luca V
Gen Hosp Psychiatry; 2017 Jul; 47():20-28. PubMed ID: 28807134
[TBL] [Abstract][Full Text] [Related]
14. Development and Validation of an Insulin Resistance Predicting Model Using a Machine-Learning Approach in a Population-Based Cohort in Korea.
Park S; Kim C; Wu X
Diagnostics (Basel); 2022 Jan; 12(1):. PubMed ID: 35054379
[TBL] [Abstract][Full Text] [Related]
15. Ensemble Learning Models Based on Noninvasive Features for Type 2 Diabetes Screening: Model Development and Validation.
Yang T; Zhang L; Yi L; Feng H; Li S; Chen H; Zhu J; Zhao J; Zeng Y; Liu H
JMIR Med Inform; 2020 Jun; 8(6):e15431. PubMed ID: 32554386
[TBL] [Abstract][Full Text] [Related]
16. Accurate Prediction of Coronary Heart Disease for Patients With Hypertension From Electronic Health Records With Big Data and Machine-Learning Methods: Model Development and Performance Evaluation.
Du Z; Yang Y; Zheng J; Li Q; Lin D; Li Y; Fan J; Cheng W; Chen XH; Cai Y
JMIR Med Inform; 2020 Jul; 8(7):e17257. PubMed ID: 32628616
[TBL] [Abstract][Full Text] [Related]
17. Ten-year prediction of suicide death using Cox regression and machine learning in a nationwide retrospective cohort study in South Korea.
Choi SB; Lee W; Yoon JH; Won JU; Kim DW
J Affect Disord; 2018 Apr; 231():8-14. PubMed ID: 29408160
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. Predicting Writing Styles of Web-Based Materials for Children's Health Education Using the Selection of Semantic Features: Machine Learning Approach.
Xie W; Ji M; Liu Y; Hao T; Chow CY
JMIR Med Inform; 2021 Jul; 9(7):e30115. PubMed ID: 34292167
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]