Search Results (1 to 10 of 3142 Results)
Download search results: CSV END BibTex RIS
Skip search results from other journals and go to results- 801 Journal of Medical Internet Research
- 597 JMIR Research Protocols
- 365 JMIR Formative Research
- 216 JMIR mHealth and uHealth
- 173 JMIR Public Health and Surveillance
- 118 Online Journal of Public Health Informatics
- 109 JMIR Mental Health
- 97 JMIR Medical Informatics
- 83 JMIR Human Factors
- 73 JMIR Cancer
- 70 JMIR Pediatrics and Parenting
- 52 JMIR Aging
- 50 Iproceedings
- 45 JMIR Medical Education
- 42 JMIR Diabetes
- 40 JMIR Serious Games
- 37 Interactive Journal of Medical Research
- 34 JMIR Dermatology
- 26 JMIR Cardio
- 25 JMIR Rehabilitation and Assistive Technologies
- 17 JMIR Infodemiology
- 17 Journal of Participatory Medicine
- 14 JMIRx Med
- 11 JMIR AI
- 8 JMIR Biomedical Engineering
- 4 JMIR Bioinformatics and Biotechnology
- 4 JMIR Nursing
- 4 JMIR XR and Spatial Computing (JMXR)
- 3 JMIR Perioperative Medicine
- 2 Asian/Pacific Island Nursing Journal
- 2 JMIR Neurotechnology
- 2 JMIRx Bio
- 1 JMIR Data
- 0 Medicine 2.0
- 0 iProceedings
- 0 JMIR Preprints
- 0 JMIR Challenges
- 0 Transfer Hub (manuscript eXchange)
Go back to the top of the page Skip and go to footer section
Go back to the top of the page Skip and go to footer section
Capturing Community Perspectives in a Statewide Cancer Needs Assessment: Online Focus Group Study
JMIR Cancer 2025;11:e63717
Download Citation: END BibTex RIS
Go back to the top of the page Skip and go to footer section
Go back to the top of the page Skip and go to footer section
Go back to the top of the page Skip and go to footer section
Generative AI–Powered Mental Wellness Chatbot for College Student Mental Wellness: Open Trial
JMIR Form Res 2025;9:e71923
Download Citation: END BibTex RIS
Go back to the top of the page Skip and go to footer section
The model is based on bidirectional encoder representations from transformers, which takes the number of emotion classes (|C|=10) and a sequence “s” as inputs formatted with standard tokens (start_of_token [CLS] and separator_token [SEP]) as [CLS] + [C] + [SEP] + s. The encoding of emotion classed in the input makes the model learn the association between the emotion classes and the words in the input sentence, which is why it outperforms existing emotion classifiers.
JMIR Infodemiology 2025;5:e67333
Download Citation: END BibTex RIS
Go back to the top of the page Skip and go to footer section