e.g. mhealth
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Skip search results from other journals and go to results- 14 JMIR Medical Informatics
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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
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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
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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
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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
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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
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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
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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
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In this study, we followed the original BERTopic framework, using BERT (Bidirectional Encoder Representations from Transformers) for document embeddings, UMAP (Uniform Manifold Approximation and Projection) for dimensionality reduction, and HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise) for clustering [16]. The process involves 4 main steps.
BERT, a pretrained language model, is used to convert words and documents into meaningful real-value vectors.
J Med Internet Res 2024;26:e52142
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As the embedding model, we chose BERT [1], a transformer-based model for contextualized word embeddings. We used a small version of BERT, named BERT-tiny [30], with 2 transformer layers and a hidden representation size of 128, pretrained on Book Corpus [31] and the English Wikipedia. Smaller models require less computation resources and are therefore more affordable and accessible. Rosin et al [32] have shown that BERT-tiny–based models were comparable to BERT-base in their ability to learn temporal trends.
JMIR AI 2024;3:e49546
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Gao et al [12] found that pretrained BERT models did not outperform simpler methods for medical document classification. The simpler methods consisted of a convolutional neural network and a hierarchical self-attention network, which had similar performance while having fewer learnable parameters. Ilias and Askounis [13] used local interpretable model-agnostic explanations (LIME) to find influential words of BERT classifications of dementia transcripts.
JMIR Ment Health 2024;11:e57362
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