e.g. mhealth
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Skip search results from other journals and go to results- 6 JMIR Medical Informatics
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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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We proposed a novel E4-tailored transformer (E4mer) architecture (Figure 2) [43] and showed that SSL is a viable paradigm, outperforming both fully supervised E4mer and classical machine learning (CML) models using handcrafted features in distinguishing acute MD episodes from clinical stability (euthymia in psychiatric parlance), that is, a time-series (binary) classification task.
We investigated what makes SSL successful.
JMIR Mhealth Uhealth 2024;12:e55094
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Although similar, this study differs in the following ways: (1) we survey a wide range of supervised ML algorithms as opposed to only using lasso regularized logistic regression, (2) we use transformer embeddings of sentences to represent information in each patient’s records and aggregate these embeddings with multilayer perceptron (MLP) networks to create a patient vector representation for patient phenotyping, and (3) we performed an ablation study of processing methods to compare the impact on performance
JMIR Form Res 2024;8:e52200
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Deep learning [23,28] and transformer language models [22,23,25,29] are the most commonly used techniques, likely because of their efficiency and highly advanced ability to interpret and understand natural language, which is key to examining content on social media.
JMIR Infodemiology 2023;3:e38390
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In this paper, a novel question-driven abstractive summarization based on transformer is proposed, namely Trans-Att, that incorporates a two-step attention mechanism and an overall integration mechanism to summarize the document with respect to the nonfactoid questions. Concretely, the two-step attention mechanism can learn richer structural dependencies among sentences and the relevance of the question and the document.
JMIR Med Inform 2022;10(8):e38052
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The prescription prediction subsystem, which is a transformer-based deep learning model, automatically generates a prescription based on input vector data. Together, the first 3 subsystems accomplish the task of extracting relevant information from an EHR file to form input variables for the prediction model. Similar representation learning operations were described in our previous paper [17].
JMIR Med Inform 2022;10(5):e35239
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