These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

250 related articles for article (PubMed ID: 36231935)

  • 1. Detecting and Analyzing Suicidal Ideation on Social Media Using Deep Learning and Machine Learning Models.
    Aldhyani THH; Alsubari SN; Alshebami AS; Alkahtani H; Ahmed ZAT
    Int J Environ Res Public Health; 2022 Oct; 19(19):. PubMed ID: 36231935
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Detecting Potentially Harmful and Protective Suicide-Related Content on Twitter: Machine Learning Approach.
    Metzler H; Baginski H; Niederkrotenthaler T; Garcia D
    J Med Internet Res; 2022 Aug; 24(8):e34705. PubMed ID: 35976193
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Detection of Suicidal Ideation on Social Media: Multimodal, Relational, and Behavioral Analysis.
    Ramírez-Cifuentes D; Freire A; Baeza-Yates R; Puntí J; Medina-Bravo P; Velazquez DA; Gonfaus JM; Gonzàlez J
    J Med Internet Res; 2020 Jul; 22(7):e17758. PubMed ID: 32673256
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Detecting Suicidal Ideation on Forums: Proof-of-Concept Study.
    Aladağ AE; Muderrisoglu S; Akbas NB; Zahmacioglu O; Bingol HO
    J Med Internet Res; 2018 Jun; 20(6):e215. PubMed ID: 29929945
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Leveraging Reddit for Suicidal Ideation Detection: A Review of Machine Learning and Natural Language Processing Techniques.
    Yeskuatov E; Chua SL; Foo LK
    Int J Environ Res Public Health; 2022 Aug; 19(16):. PubMed ID: 36011981
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Using weak supervision and deep learning to classify clinical notes for identification of current suicidal ideation.
    Cusick M; Adekkanattu P; Campion TR; Sholle ET; Myers A; Banerjee S; Alexopoulos G; Wang Y; Pathak J
    J Psychiatr Res; 2021 Apr; 136():95-102. PubMed ID: 33581461
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Detection of Suicidality Among Opioid Users on Reddit: Machine Learning-Based Approach.
    Yao H; Rashidian S; Dong X; Duanmu H; Rosenthal RN; Wang F
    J Med Internet Res; 2020 Nov; 22(11):e15293. PubMed ID: 33245287
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Prediction of suicidal ideation in children and adolescents using machine learning and deep learning algorithm: A case study in South Korea where suicide is the leading cause of death.
    Shin S; Kim K
    Asian J Psychiatr; 2023 Oct; 88():103725. PubMed ID: 37595385
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Proactive Suicide Prevention Online (PSPO): Machine Identification and Crisis Management for Chinese Social Media Users With Suicidal Thoughts and Behaviors.
    Liu X; Liu X; Sun J; Yu NX; Sun B; Li Q; Zhu T
    J Med Internet Res; 2019 May; 21(5):e11705. PubMed ID: 31344675
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Detecting Suicidal Ideation in Social Media: An Ensemble Method Based on Feature Fusion.
    Liu J; Shi M; Jiang H
    Int J Environ Res Public Health; 2022 Jul; 19(13):. PubMed ID: 35805856
    [TBL] [Abstract][Full Text] [Related]  

  • 11. A Hybrid Deep Learning Model Using Grid Search and Cross-Validation for Effective Classification and Prediction of Suicidal Ideation from Social Network Data.
    Chadha A; Kaushik B
    New Gener Comput; 2022; 40(4):889-914. PubMed ID: 36267123
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Machine learning and deep learning-based approach to categorize Bengali comments on social networks using fused dataset.
    Mohi Uddin KM; Hamim H; Mim MNT; Akhter A; Uddin MA
    PLoS One; 2024; 19(10):e0308862. PubMed ID: 39361557
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 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]  

  • 14. Convolutional Neural Network-Based Deep Learning Model for Predicting Differential Suicidality in Depressive Patients Using Brain Generalized q-Sampling Imaging.
    Chen VC; Wong FT; Tsai YH; Cheok MT; Chang YE; McIntyre RS; Weng JC
    J Clin Psychiatry; 2021 Feb; 82(2):. PubMed ID: 33988925
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Identifying health related occupations of Twitter users through word embedding and deep neural networks.
    Zainab K; Srivastava G; Mago V
    BMC Bioinformatics; 2022 Sep; 22(Suppl 10):630. PubMed ID: 36171569
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Predicting the Transition From Depression to Suicidal Ideation Using Facebook Data Among Indian-Bangladeshi Individuals: Protocol for a Cohort Study.
    Turjo MD; Mundada KS; Haque NJ; Ahmed N
    JMIR Res Protoc; 2024 Oct; 13():e55511. PubMed ID: 39374059
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Robust suicide risk assessment on social media via deep adversarial learning.
    Sawhney R; Joshi H; Gandhi S; Jin D; Shah RR
    J Am Med Inform Assoc; 2021 Jul; 28(7):1497-1506. PubMed ID: 33779728
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Suicidality Detection on Social Media Using Metadata and Text Feature Extraction and Machine Learning.
    Jung W; Kim D; Nam S; Zhu Y
    Arch Suicide Res; 2023; 27(1):13-28. PubMed ID: 34319221
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Building a challenging medical dataset for comparative evaluation of classifier capabilities.
    Bozkurt B; Coskun K; Bakal G
    Comput Biol Med; 2024 Aug; 178():108721. PubMed ID: 38901188
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Predicting Age Groups of Reddit Users Based on Posting Behavior and Metadata: Classification Model Development and Validation.
    Chew R; Kery C; Baum L; Bukowski T; Kim A; Navarro M
    JMIR Public Health Surveill; 2021 Mar; 7(3):e25807. PubMed ID: 33724195
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 13.