e.g. mhealth
Search Results (1 to 10 of 208 Results)
Download search results: CSV END BibTex RIS
Skip search results from other journals and go to results- 60 Journal of Medical Internet Research
- 42 JMIR Medical Informatics
- 23 JMIR Formative Research
- 17 JMIR Medical Education
- 12 JMIR AI
- 8 JMIR Cancer
- 7 JMIR Infodemiology
- 7 JMIR Mental Health
- 6 JMIR Public Health and Surveillance
- 4 JMIR Dermatology
- 4 JMIR Human Factors
- 3 JMIR Aging
- 3 JMIR Research Protocols
- 3 JMIRx Med
- 2 Interactive Journal of Medical Research
- 2 JMIR Diabetes
- 1 JMIR Bioinformatics and Biotechnology
- 1 JMIR Neurotechnology
- 1 JMIR Nursing
- 1 JMIR Serious Games
- 1 JMIR mHealth and uHealth
- 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 JMIR Cardio
- 0 Journal of Participatory Medicine
- 0 JMIR Pediatrics and Parenting
- 0 JMIR Perioperative Medicine
- 0 JMIRx Bio
- 0 Transfer Hub (manuscript eXchange)
- 0 Asian/Pacific Island Nursing Journal
- 0 Online Journal of Public Health Informatics
- 0 JMIR XR and Spatial Computing (JMXR)
Go back to the top of the page Skip and go to footer section
Recent advancements in NLP have addressed these challenges by facilitating tasks such as health information retrieval and extraction, text summarization, sentiment and emotion analysis, and the construction of medical ontologies and knowledge graphs. For instance, studies have demonstrated the utility of NLP in analyzing social media data to monitor public health trends, such as SU and mental health discussions during crises [20].
JMIR Infodemiology 2025;5:e67333
Download Citation: END BibTex RIS
Natural Language Processing for Identification of Hospitalized People Who Use Drugs: Cohort Study
NLP has the potential to uncover hospital encounters with PWUD that may have previously been missed. Although NLP had greater PPV than diagnostic codes, its PPV remained low. We found that PWUD from racially and ethnically minoritized communities and those who had low income were more likely to be represented in the minimally documented cohort (ie, entry with NLP-only), rather than the maximally documented cohort.
JMIR AI 2025;4:e63147
Download Citation: END BibTex RIS
Go back to the top of the page Skip and go to footer section
To address these obstacles, natural language processing (NLP), which combines computational linguistics and deep learning models to process narrative data, can be used to automate the review process of clinical notes to detect falls [14].
Several studies have demonstrated the capability of supervised models to detect fall incidents, which have been documented in clinical notes [15-17].
JMIR Med Inform 2025;13:e66973
Download Citation: END BibTex RIS
Subsequently, ADR prediction methodologies using machine learning techniques have been developed [5-7], and with advances in natural language processing (NLP) techniques, attempts have been made to automatically extract and predict drug-side effect relationships from vast amounts of biomedical literature data [8-10].
JMIR Med Inform 2025;13:e67513
Download Citation: END BibTex RIS
Go back to the top of the page Skip and go to footer section
Natural language processing (NLP) tools are instrumental in analyzing social media content, offering deeper insights into public perception. NLP methods enable the analysis of public sentiment toward specific topics, the detection of emerging trends, and the identification of demographic groups participating in these discussions. These tools have been extensively used to assess public acceptance of vaccines [19,20], guide economic investments [21], evaluate innovative products [22,23], and more.
JMIR Form Res 2025;9:e60859
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