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Enhancing the Predictions of Cytomegalovirus Infection in Severe Ulcerative Colitis Using a Deep Learning Ensemble Model: Development and Validation Study

Enhancing the Predictions of Cytomegalovirus Infection in Severe Ulcerative Colitis Using a Deep Learning Ensemble Model: Development and Validation Study

The effectiveness of this ensemble was evaluated using a range of performance metrics meticulously selected to comprehensively assess the classification performance of our model ensemble, ensuring a comprehensive understanding of its ability to distinguish between cases of with and those without CMV complications (Figure 1).

Jeong Heon Kim, A Reum Choe, Ju Ran Byeon, Yehyun Park, Eun Mi Song, Seong-Eun Kim, Eui Sun Jeong, Rena Lee, Jin Sung Kim, So Hyun Ahn, Sung Ae Jung

JMIR Med Inform 2025;13:e64987

Convolutional Neural Network Models for Visual Classification of Pressure Ulcer Stages: Cross-Sectional Study

Convolutional Neural Network Models for Visual Classification of Pressure Ulcer Stages: Cross-Sectional Study

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].

Changbin Lei, Yan Jiang, Ke Xu, Shanshan Liu, Hua Cao, Cong Wang

JMIR Med Inform 2025;13:e62774

Machine Learning Analysis of Engagement Behaviors in Older Adults With Dementia Playing Mobile Games: Exploratory Study

Machine Learning Analysis of Engagement Behaviors in Older Adults With Dementia Playing Mobile Games: Exploratory Study

Reference 21: Classification of maize genotype using logistic regression Reference 25: An approach for sentiment analysis using gini index with random forest classificationclassification

Melika Torabgar, Mathieu Figeys, Shaniff Esmail, Eleni Stroulia, Adriana M Ríos Rincón

JMIR Serious Games 2025;13:e54797

Development and Feasibility Study of HOPE Model for Prediction  of Depression Among Older Adults Using Wi-Fi-based Motion Sensor Data: Machine Learning Study

Development and Feasibility Study of HOPE Model for Prediction of Depression Among Older Adults Using Wi-Fi-based Motion Sensor Data: Machine Learning Study

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.

Shayan Nejadshamsi, Vania Karami, Negar Ghourchian, Narges Armanfard, Howard Bergman, Roland Grad, Machelle Wilchesky, Vladimir Khanassov, Isabelle Vedel, Samira Abbasgholizadeh Rahimi

JMIR Aging 2025;8:e67715

Discrimination of Radiologists' Experience Level Using Eye-Tracking Technology and Machine Learning: Case Study

Discrimination of Radiologists' Experience Level Using Eye-Tracking Technology and Machine Learning: Case Study

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.

Stanford Martinez, Carolina Ramirez-Tamayo, Syed Hasib Akhter Faruqui, Kal Clark, Adel Alaeddini, Nicholas Czarnek, Aarushi Aggarwal, Sahra Emamzadeh, Jeffrey R Mock, Edward J Golob

JMIR Form Res 2025;9:e53928

Business Venturing in Regulated Markets—Taxonomy and Archetypes of Digital Health Business Models in the European Union: Mixed Methods Descriptive and Exploratory Study

Business Venturing in Regulated Markets—Taxonomy and Archetypes of Digital Health Business Models in the European Union: Mixed Methods Descriptive and Exploratory Study

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.

Sascha Noel Weimar, Rahel Sophie Martjan, Orestis Terzidis

J Med Internet Res 2025;27:e65725

Leveraging Machine Learning to Identify Subgroups of Misclassified Patients in the Emergency Department: Multicenter Proof-of-Concept Study

Leveraging Machine Learning to Identify Subgroups of Misclassified Patients in the Emergency Department: Multicenter Proof-of-Concept Study

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.

Sage Wyatt, Dagfinn Lunde Markussen, Mounir Haizoune, Anders Strand Vestbø, Yeneabeba Tilahun Sima, Maria Ilene Sandboe, Marcus Landschulze, Hauke Bartsch, Christopher Martin Sauer

J Med Internet Res 2024;26:e56382

Artificial Intelligence–Aided Diagnosis System for the Detection and Classification of Private-Part Skin Diseases: Decision Analytical Modeling Study

Artificial Intelligence–Aided Diagnosis System for the Detection and Classification of Private-Part Skin Diseases: Decision Analytical Modeling Study

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.

Wei Wang, Xiang Chen, Licong Xu, Kai Huang, Shuang Zhao, Yong Wang

J Med Internet Res 2024;26:e52914

Machine Learning Driven by Magnetic Resonance Imaging for the Classification of Alzheimer Disease Progression: Systematic Review and Meta-Analysis

Machine Learning Driven by Magnetic Resonance Imaging for the Classification of Alzheimer Disease Progression: Systematic Review and Meta-Analysis

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.

Gopi Battineni, Nalini Chintalapudi, Francesco Amenta

JMIR Aging 2024;7:e59370