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We conducted comprehensive experiments to validate the effectiveness of the proposed RPS technique in enhancing DL model performance across both CNN and transformer architectures. For our evaluation, we selected 4 established models: Res Net-34 [52], Mobile Net V3 (small variant) [53], Vision Transformer (base-16) [23], and Swin Transformer (tiny version) [29], all initialized with preactivated weights.
JMIR Bioinform Biotech 2025;6:e68848
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To what extent can encoder-based Transformer models support the early identification of HFp EF symptomatology indicative of cardiac amyloidosis in Spanish-language clinical narratives?
J Med Internet Res 2025;27:e76433
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In this study, we used a multihead attention transformer encoder, drawing inspiration from BERT [8,9]. The model architecture, illustrated in Figure 3, includes an embedding module, a multihead attention transformer encoder, a feed-forward layer, and 2 classifier heads. The embedding module integrates medical codes with their associated temporal information.
JMIR Med Inform 2025;13:e68138
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Enhancing Antidiabetic Drug Selection Using Transformers: Machine-Learning Model Development
The field of ML has undergone significant advancement since the introduction of the transformer approach in 2017 [28,29]. Transformers enable contextual interactions for natural language processing tasks and have become a core technology across diverse domains [30-32]. The transformer model incorporates an attention mechanism and has shown remarkable performance in tasks involving the extraction of temporal and semantic relationships, leading to success in tasks such as generation and classification [33].
JMIR Med Inform 2025;13:e67748
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The tokenized input was then processed through the Ko BERT transformer encoder, followed by a fully connected classification layer to predict the sentiment as positive, neutral, or negative. For training, we used the Adam optimizer with a learning rate of 5×10-5, a warmup ratio of 0.1, and gradient clipping with a max norm of 1 to stabilize learning. The model was trained for 3 epochs with a batch size of 64. To evaluate computational performance, the training and inference times were measured.
JMIR Med Inform 2025;13:e65127
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Pretrained transformer language models, like bidirectional encoder representations from transformers (BERT) [16-18], have outperformed the existing deep neural network models, including convolutional neural networks and recurrent neural networks. Examples of transformer-based models trained on biomedical data include Bio BERT [19], Bio Link BERT [20], Blue BERT [21], and Bio Med BERT [22], which are pretrained on biomedical literature and clinical text.
JMIR Med Inform 2025;13:e63267
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The technique of modeling words in a large context has been referred to as transformer-based large language modeling [8]. This may not only facilitate the automatic analysis of large amounts of text data [9,10] but, by modeling words in a large context, also allow the generation of meaningful text and the interactive use of this technology [5,10]. Thus, the application of LLMs may improve efficiency and effectiveness of data processing in various fields—including health care [5].
JMIR Ment Health 2025;12:e57986
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Transformers for Neuroimage Segmentation: Scoping Review
In this review, we focused on the deep transformer–based techniques that have gained more attention recently. From the proposed models, we can find transformer-based, CNN with transformer-based, and generative adversarial network with transformer-based techniques. At the same time, the methods based on Trans BTS, Trans UNet, Sein UNet, and U-Net with transformer are the most used models for neuroimage segmentation. Figure 3 illustrates these models in terms of architecture.
J Med Internet Res 2025;27:e57723
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Zhang and Jankowski [25] proposed average pooling transformer layers handling token-, sentence-, and document-level embeddings for classifying International Classification of Diseases codes. Their model outperformed the BERT-base model by 11 points.
For the reading comprehension task, BERT can be used to determine the answer span within a given text. Pampari et al [26] proposed the electronic medical record question answering (emr QA) dataset to determine the answer span to a question in a clinical context.
JMIR Med Inform 2024;12:e52897
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Since its introduction in 2017, the transformer model has excelled in various time-series predictive tasks, solidifying its position as a core technology across multiple fields [27-32]. The transformer model incorporates an attention mechanism simplifying the extraction of temporal relationships and setting it apart from other models [33-35].
JMIR AI 2024;3:e56700
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