Published on in Vol 9, No 7 (2021): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/28227, first published .
Multifeature Fusion Attention Network for Suicide Risk Assessment Based on Social Media: Algorithm Development and Validation

Multifeature Fusion Attention Network for Suicide Risk Assessment Based on Social Media: Algorithm Development and Validation

Multifeature Fusion Attention Network for Suicide Risk Assessment Based on Social Media: Algorithm Development and Validation

Journals

  1. Homan S, Gabi M, Klee N, Bachmann S, Moser A, Duri' M, Michel S, Bertram A, Maatz A, Seiler G, Stark E, Kleim B. Linguistic features of suicidal thoughts and behaviors: A systematic review. Clinical Psychology Review 2022;95:102161 View
  2. Priyamvada B, Singhal S, Nayyar A, Jain R, Goel P, Rani M, Srivastava M. Stacked CNN - LSTM approach for prediction of suicidal ideation on social media. Multimedia Tools and Applications 2023;82(18):27883 View
  3. Abimannan S, El-Alfy E, Chang Y, Hussain S, Shukla S, Satheesh D. Ensemble Multifeatured Deep Learning Models and Applications: A Survey. IEEE Access 2023;11:107194 View
  4. Kjell O, Kjell K, Schwartz H. Beyond rating scales: With targeted evaluation, large language models are poised for psychological assessment. Psychiatry Research 2024;333:115667 View
  5. G. X. Suicidal Ideation Detection and Influential Keyword Extraction from Twitter using Deep Learning (SID). EAI Endorsed Transactions on Pervasive Health and Technology 2024;10 View