e.g. mhealth
Search Results (1 to 10 of 15411 Results)
Download search results: CSV END BibTex RIS
Skip search results from other journals and go to results- 4734 Journal of Medical Internet Research
- 1800 JMIR Research Protocols
- 1759 JMIR Formative Research
- 1105 JMIR mHealth and uHealth
- 1053 JMIR Medical Informatics
- 601 JMIR Mental Health
- 564 JMIR Medical Education
- 531 JMIR Serious Games
- 520 JMIR Human Factors
- 505 JMIR Public Health and Surveillance
- 322 JMIR Aging
- 255 JMIR Pediatrics and Parenting
- 197 Interactive Journal of Medical Research
- 168 JMIR Cancer
- 157 JMIR Rehabilitation and Assistive Technologies
- 146 JMIR AI
- 134 JMIR Diabetes
- 129 JMIRx Med
- 118 JMIR Infodemiology
- 105 JMIR Cardio
- 88 JMIR Nursing
- 70 JMIR Dermatology
- 63 JMIR Biomedical Engineering
- 55 Journal of Participatory Medicine
- 49 JMIR Perioperative Medicine
- 38 Iproceedings
- 34 JMIR Bioinformatics and Biotechnology
- 34 Online Journal of Public Health Informatics
- 23 JMIR Neurotechnology
- 19 JMIR XR and Spatial Computing (JMXR)
- 17 Asian/Pacific Island Nursing Journal
- 13 Medicine 2.0
- 3 JMIRx Bio
- 1 JMIR Data
- 1 JMIR XR and Spatial Computing
- 0 iProceedings
- 0 JMIR Preprints
- 0 JMIR Challenges
- 0 Transfer Hub (manuscript eXchange)
Association Between Risk Factors and Major Cancers: Explainable Machine Learning Approach
Machine learning has shown promising potential in cancer prediction by leveraging electronic health record (EHR) data to identify risk factors [17]. Current applications range from developing predictive models for early cancer detection to personalized treatment recommendations and outcome predictions, based on various patient characteristics and biomarkers. Despite these advancements, several challenges remain in cancer prediction using machine learning [18].
JMIR Cancer 2025;11:e62833
Download Citation: END BibTex RIS
Further, the moderate concordance between Chat GPT-4 and Google Gemini suggests similar underlying approaches, and the improved agreement in Chat GPT-4’s second round indicates potential learning and adaptability; however, their limited alignment with nephrologists raises concerns regarding their clinical applicability.
JMIR Diabetes 2025;10:e65846
Download Citation: END BibTex RIS
Reference 28: Dermoscopic lotus of learning: implementation and dissemination of a multimodal dermoscopy
JMIR Cancer 2025;11:e67902
Download Citation: END BibTex RIS
Mobile apps have the advantages of self-directedness, instant connectivity, ubiquity, personalization, and interactive learning through social engagement [7], which may enhance self-monitoring and practice of health behaviors without time and space constraints. However, few mobile apps are equipped with strategies for a 1-stop approach to monitor multiple heart-healthy behaviors that may correlate with each other [8] and target individuals without cardiovascular disease in a community setting.
JMIR Form Res 2025;9:e66108
Download Citation: END BibTex RIS
NLP has been successfully applied to extract symptoms from clinical narratives using rule-based methods [8-14], and machine learning models [15,16]. Early NLP applications relied on rule-based approaches, whereas recent methods leverage advanced transformer-based deep learning models, such as Bidirectional Encoder Representations from Transformers (BERT) [17], which enhance performance through word embeddings and attention mechanisms [18].
JMIR AI 2025;4:e69132
Download Citation: END BibTex RIS
Numerous studies leveraged machine learning techniques to classify tweets as positive, negative, or neutral sentiment toward vaccines, enabling the identification of vaccine hesitancy among communities and social media users [35,54-68]. Most of these studies collected data from Twitter using keywords or hashtags related to vaccinations. Chakraborty et al [56] used deep learning to analyze 226,668 COVID-19 tweets from December 2019 to May 2020, achieving 81% accuracy.
JMIR Infodemiology 2025;5:e70576
Download Citation: END BibTex RIS
In recent years, there has been growing interest in using machine learning and deep learning models to predict metabolic syndrome and related chronic diseases, such as CVD [8,9]. These models aim to facilitate early identification and intervention, thereby potentially reducing the risk of CVD in individuals with metabolic syndrome.
J Med Internet Res 2025;27:e67525
Download Citation: END BibTex RIS
Reference 14: Exploring user satisfaction for e-learning systems via usage-based metrics and system usability Reference 22: Construction of learning objectives and content for newly graduated nurses in tertiary
J Med Internet Res 2025;27:e67948
Download Citation: END BibTex RIS
Reference 39: Effectiveness of e-STORY App in clinical reasoning competency and self-directed learning
JMIR Serious Games 2025;13:e66998
Download Citation: END BibTex RIS
For example, learning about what food to eat to raise a low blood sugar. It’s helping to remind me to take my medications. I think this program is best for older people who have the time and attention to follow the advice. [Male, 63 years old, El Salvador]
In this study, we outline the multistage process used to adapt the REACH text message content [26], which addresses patient-reported barriers to diabetes medication adherence, to a Latino population.
JMIR Hum Factors 2025;12:e66668
Download Citation: END BibTex RIS