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Machine-Learning Model Identifies Patients With Alpha-1 Antitrypsin Deficiency Using Claims Records.
COPD
Sep 23, 2024
Identifying patients with rare diseases like alpha-1 antitrypsin deficiency (AATD) is challenging. Machine-learning models may be trained to identify patients with rare diseases using large-scale, real-world databases, whereas electronic medical reco...
Interpretable machine learning model predicting immune checkpoint inhibitor-induced hypothyroidism: A retrospective cohort study.
Cancer science
Sep 23, 2024
Hypothyroidism is a known adverse event associated with the use of immune checkpoint inhibitors (ICIs) in cancer treatment. This study aimed to develop an interpretable machine learning (ML) model for individualized prediction of hypothyroidism in pa...
A Pilot Survey of Patient Perspectives on an Artificial Intelligence-Generated Presenter in a Patient Information Video about Face-Down Positioning after Vitreoretinal Surgery.
Ophthalmic research
Sep 23, 2024
INTRODUCTION: Video education is a commonly used patient education tool. However, the impact of integrating artificial intelligence (AI) into video education remains unexplored. This study aimed to examine the acceptability of an AI-generated present...
High accuracy in lower limb alignment analysis using convolutional neural networks, with improvements needed for joint-level metrics.
Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA
Sep 22, 2024
PURPOSE: Evaluation of long-leg standing radiographs (LSR) is a standardised procedure for analysis of primary or secondary deformities of the lower limbs. Deep-learning convolutional neural networks (CNN) offer the potential to enhance radiological ...
Deep learning enables accurate brain tissue microstructure analysis based on clinically feasible diffusion magnetic resonance imaging.
NeuroImage
Sep 22, 2024
Diffusion magnetic resonance imaging (dMRI) allows non-invasive assessment of brain tissue microstructure. Current model-based tissue microstructure reconstruction techniques require a large number of diffusion gradients, which is not clinically feas...
Optimizing Real-Time MI-BCI Performance in Post-Stroke Patients: Impact of Time Window Duration on Classification Accuracy and Responsiveness.
Sensors (Basel, Switzerland)
Sep 22, 2024
Brain-computer interfaces (BCIs) are promising tools for motor neurorehabilitation. Achieving a balance between classification accuracy and system responsiveness is crucial for real-time applications. This study aimed to assess how the duration of ti...
Deep learning-based reconstruction improves the image quality of low-dose CT enterography in patients with inflammatory bowel disease.
Abdominal radiology (New York)
Sep 21, 2024
PURPOSE: Lifelong re-examination of CT enterography (CTE) in patients with inflammatory bowel disease (IBD) may be necessary, and reducing radiation exposure during CT examinations is crucial. We investigated the potential application of deep learnin...
Predicting overall survival in anaplastic thyroid cancer using machine learning approaches.
European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
Sep 21, 2024
PURPOSE: Anaplastic thyroid carcinoma (ATC) is a highly aggressive and lethal thyroid cancer subtype with a poor prognosis. Recent advancements in machine learning (ML) have the potential to improve survival predictions. This study aimed to develop a...
Characterizing daily physical activity patterns with unsupervised learning via functional mixture models.
Journal of behavioral medicine
Sep 21, 2024
Physical inactivity is a significant public health concern. Consideration of inter-individual variations in physical activity (PA) trends can provide additional information about the groups under study to aid intervention design. This study aims to i...
Integration of an electronic hand hygiene auditing system with electronic health records using machine learning to predict hospital-acquired infection in a health care setting.
American journal of infection control
Sep 21, 2024
BACKGROUND: Hospital-acquired infections (HAIs) increase morbidity, mortality, and health care costs. Effective hand hygiene (HH) is crucial for prevention, but achieving high compliance remains challenge. This study explores using machine learning t...
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