Search Results (1 to 10 of 496 Results)
Download search results: CSV END BibTex RIS
Skip search results from other journals and go to results- 171 Journal of Medical Internet Research
- 72 JMIR Public Health and Surveillance
- 65 JMIR mHealth and uHealth
- 56 JMIR Medical Informatics
- 35 JMIR Formative Research
- 23 JMIR Research Protocols
- 14 JMIR Serious Games
- 7 JMIR Aging
- 5 Interactive Journal of Medical Research
- 5 Online Journal of Public Health Informatics
- 4 JMIR AI
- 4 JMIR Cancer
- 4 JMIR Diabetes
- 4 JMIR Human Factors
- 4 JMIR Medical Education
- 4 JMIR Mental Health
- 3 Iproceedings
- 3 JMIR Pediatrics and Parenting
- 2 Asian/Pacific Island Nursing Journal
- 2 JMIR Dermatology
- 2 JMIR Infodemiology
- 2 JMIRx Med
- 1 JMIR Bioinformatics and Biotechnology
- 1 JMIR Cardio
- 1 JMIR Nursing
- 1 JMIR Perioperative Medicine
- 1 Journal of Participatory Medicine
- 0 Medicine 2.0
- 0 iProceedings
- 0 JMIR Rehabilitation and Assistive Technologies
- 0 JMIR Preprints
- 0 JMIR Challenges
- 0 JMIR Biomedical Engineering
- 0 JMIR Data
- 0 JMIRx Bio
- 0 Transfer Hub (manuscript eXchange)
- 0 JMIR Neurotechnology
- 0 JMIR XR and Spatial Computing (JMXR)
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
canadian landscape: stratégies pour atteindre une meilleure santé cognitive chez les personnes souffrant du
JMIR Serious Games 2025;13:e67501
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
Association Between Risk Factors and Major Cancers: Explainable Machine Learning Approach
JMIR Cancer 2025;11:e62833
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
The study by Jiang et al [27] used an ML method to build a predictive model, but they did not target patients with sepsis, and the model was not externally validated.
In our study, we developed an interpretable ML model in 4 retrospective cohorts and 1 prospective cohort, aimed at early and accurate prediction of persistent sepsis-associated AKI.
J Med Internet Res 2025;27:e62932
Download Citation: END BibTex RIS