e.g. mhealth
Search Results (1 to 10 of 43 Results)
Download search results: CSV END BibTex RIS
Skip search results from other journals and go to results- 16 JMIR Medical Informatics
- 13 JMIR Formative Research
- 10 Journal of Medical Internet Research
- 2 JMIR Mental Health
- 1 JMIR AI
- 1 JMIR Infodemiology
- 0 Medicine 2.0
- 0 Interactive Journal of Medical Research
- 0 iProceedings
- 0 JMIR Research Protocols
- 0 JMIR Human Factors
- 0 JMIR Public Health and Surveillance
- 0 JMIR mHealth and uHealth
- 0 JMIR Serious Games
- 0 JMIR Rehabilitation and Assistive Technologies
- 0 JMIR Preprints
- 0 JMIR Bioinformatics and Biotechnology
- 0 JMIR Medical Education
- 0 JMIR Cancer
- 0 JMIR Challenges
- 0 JMIR Diabetes
- 0 JMIR Biomedical Engineering
- 0 JMIR Data
- 0 JMIR Cardio
- 0 Journal of Participatory Medicine
- 0 JMIR Dermatology
- 0 JMIR Pediatrics and Parenting
- 0 JMIR Aging
- 0 JMIR Perioperative Medicine
- 0 JMIR Nursing
- 0 JMIRx Med
- 0 JMIRx Bio
- 0 Transfer Hub (manuscript eXchange)
- 0 JMIR Neurotechnology
- 0 Asian/Pacific Island Nursing Journal
- 0 Online Journal of Public Health Informatics
- 0 JMIR XR and Spatial Computing (JMXR)
They applied 3 variants of the pretrained bidirectional encoder representations from transformers (BERT) model, namely m BERT, Sci BERT, and Bio BERT, to generate embeddings used as features for various ML classifiers, including decision trees (DTs) and DL models. Model performance was assessed using precision, recall, and F1-score metrics.
JMIR Infodemiology 2025;5:e70525
Download Citation: END BibTex RIS
A range of models has been explored, such as convolutional neural networks [19]; transformer-based architectures such as bidirectional encoder representations from transformers (BERT), robustly optimized BERT approach, and XLNet [20-22]; and the Siamese network [23]. Despite their promising performance, most of these models have been limited to single-language settings—predominantly English [20-22] or Chinese [19,23].
JMIR Med Inform 2025;13:e71687
Download Citation: END BibTex RIS
Med-BERT [11] introduced a length of stay auxiliary prediction task to enrich contextual information about the severity of patients’ health conditions. CEHRT-BERT [16] and Claim-PT [27] incorporated visit type predictions (eg, inpatient and outpatient visits) to represent external domain knowledge into the model, mitigating the effect of sparse codes based on the observation that different medical concepts are associated with different visit types.
JMIR Med Inform 2025;13:e68138
Download Citation: END BibTex RIS
API: application programming interface; BERT: Bidirectional Encoder Representations from Transformers.
The analyzed video comments are from a series of 360° videos titled “A Walk-Through Dementia,” the most viewed videos under the search terms “dementia” and “Alzheimer disease.” Developed by Alzheimer’s Research United Kingdom, this series aims to raise awareness about the impact of dementia on individuals’ lives by engaging the public, health care professionals, and caregivers.
JMIR Form Res 2025;9:e67755
Download Citation: END BibTex RIS
On the other hand for methods using BERT based models, the models are limited by the inherent limitations of BERT models such as their noncausal nature and limited context length. BERT-based models, despite their effectiveness in text classification tasks, have two key architectural limitations that constrain their performance in radiology report labeling. First, BERT’s bidirectional nature focuses on context aggregation but lacks the ability to model causal relationships in sequential data.
JMIR Med Inform 2025;13:e68618
Download Citation: END BibTex RIS
Identifying Patient-Reported Care Experiences in Free-Text Survey Comments: Topic Modeling Study
We found that researchers have taken different approaches to topic modeling of patient-reported experience, including latent Dirichlet allocation (LDA), nonnegative matrix factorization, Top2 Vec, and BERT (bidirectional encoder representations from transformers). Many of these studies trained new models or otherwise involved what could be considered a high degree of model tuning.
JMIR Med Inform 2025;13:e63466
Download Citation: END BibTex RIS
Teenager Substance Use on Reddit: Mixed Methods Computational Analysis of Frames and Emotions
Initially, the BERT (Bidirectional Encoder Representations from Transformers)–based topic modeling algorithm identified topic clusters by capturing semantic relationships and patterns within the text. These clusters were provisionally labeled based on the most representative terms and phrases extracted by the algorithm.
J Med Internet Res 2025;27:e59338
Download Citation: END BibTex RIS
Coutinho and Martins [14] proposed a BERT model with a fine-tuning method for automatic ICD-10 coding of death certificates based on free-text descriptions and associated documents. Additionally, Yan et al [15] introduced Rad BERT, an ensemble model combining BERT-base, Clinical-BERT, the robustly optimized BERT pretraining approach (Ro BERTa), and Bio Med-Ro BERTa adapted for radiology.
JMIR Med Inform 2025;13:e63020
Download Citation: END BibTex RIS
Large Language Models for Mental Health Applications: Systematic Review
This criterion encompasses models such as GPT (Open AI) and Bidirectional Encoder Representations from Transformers (BERT; Google AI). Although the standard BERT model, with only 0.34 billion parameters [29], does not meet the traditional criteria for “large,” its sophisticated bidirectional design and pivotal role in establishing new natural language processing (NLP) benchmarks justify its inclusion among notable LLMs [30].
JMIR Ment Health 2024;11:e57400
Download Citation: END BibTex RIS
To address this issue, we developed Korean Medical BERT (KM-BERT), a medical domain–specific pretrained BERT model, which was trained on a corpus of 6 million sentences from medical textbooks, health information news, and medical research papers [17]. Furthermore, we developed the fine-tuned KM-BERT model capable of recommending medical specialties based on general user queries [18].
Comparing these models can reveal which types of tasks each model is better suited to in the health care domain.
JMIR Form Res 2024;8:e47814
Download Citation: END BibTex RIS