e.g. mhealth
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Skip search results from other journals and go to results- 24 Journal of Medical Internet Research
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Alex Net, a pioneering CNN in image classification, consists of 8 layers: 5 convolutional layers with varying filter numbers and 3 fully connected layers. It employs Re LU activations, max pooling, and dropout for regularization. The introduction of Re LU and dropout layers in Alex Net reduced training times and prevented overfitting, whereas its deep architecture allowed for the learning of complex features, enhancing classification accuracy [18].
JMIR Med Inform 2025;13:e62774
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Reference 21: Classification of maize genotype using logistic regression Reference 25: An approach for sentiment analysis using gini index with random forest classificationclassification
JMIR Serious Games 2025;13:e54797
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As the field of artificial intelligence (AI) advances, machine learning models have emerged as promising tools for depression classification using physical activity data [22]. For instance, Adamczyk and Malawski [23] used data from wearable actigraph watches in 3 classification models: logistic regression (LR), support vector machine (SVM), and random forest (RF) comparing automatic and manual feature engineering for depression classification.
JMIR Aging 2025;8:e67715
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The baseline is used for two main reasons: (1) traditional features are well-known and correlate with radiologist expertise, serving as a necessary reference point to evaluate our proposed method’s effectiveness, and (2) comparing our novel discretized vector encoding method against this baseline demonstrates the added value, improved classification accuracy, and robustness of our new approach. We separated those features based on if they required ground truthing of exams.
JMIR Form Res 2025;9:e53928
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This approach provides a systematic classification of DHT business models and a simplified overview that establishes a common language. In addition, this study contributes to business model theory in complex domains by describing key components, the influence of the regulations, and drivers for business model innovation. In practical terms, this clear structure, real-world cases, and archetypal descriptions can assist digital health entrepreneurs in identifying suitable business models for their DHTs.
J Med Internet Res 2025;27:e65725
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SHapley Additive ex Planations (SHAP)-values for undertriage in the Bergen (A) and Trondheim (B) classification model.
In the Bergen dataset, orthopedics and plastic surgery clinical assignment categories (Figure 4 A) show a shift towards higher SHAP-values, indicating a higher probability of undertriage, while the trauma category shows a shift toward lower SHAP-values, indicating a higher probability of correct triage.
J Med Internet Res 2024;26:e56382
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Because of these two issues, the classification of the clinical images of PPSDs is usually more difficult than that of images in natural scenes [25]. Therefore, it is necessary to develop more effective techniques to improve the classification performance of PPSDs.
In this paper, we make the first attempt at the assistant diagnosis of PPSDs and develop a two-stage AI-aided diagnosis system by simulating the diagnostic process of dermatologists.
J Med Internet Res 2024;26:e52914
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Data were subgrouped into 4 category-based AD classifications namely, 2-group classification, 3-group classification, 4-group classification, and 6-group classification. The 2-group classification involved individuals either without dementia (nondemented, ND) or with dementia (demented, AD). The 3-group classification includes CN, MCI, and AD. The 4-group classification comprises ND, mildly demented (MD), moderately demented (Mo D), and AD.
JMIR Aging 2024;7:e59370
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The list of International Classification of Diseases (ICD)–9 codes and antiretroviral medications used to define indications can be found in Table 1.
International Classification of Diseases (ICD)–9 diagnosis and generic product identifier medication codes used in an algorithm to identify likely uses of antiretroviral medications for HIV prevention indications, United States, 2012-2013.
a CMV: cytomegalovirus.
b GPI: generic product identifier.
c FTC: emtricitabine.
d TDF: tenofovir disoproxil fumarate.
JMIR Form Res 2024;8:e55614
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To tackle false negatives and improve recall, we built a classifier based on a deep learning model: Such models learn a dense vector representation of a news article that can be used for further classification of the article without being limited by the specific choice of words in the text. The classifier assigns to each article a soft probability that it is positive for each of the topics of interest in a multilabel binary classification fashion.
JMIR AI 2024;3:e55059
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