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Weighted Multi-Modal Contrastive Learning Based Hybrid Network for Alzheimer's Disease Diagnosis.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Multiple imaging modalities and specific proteins in the cerebrospinal fluid, providing a comprehensive understanding of neurodegenerative disorders, have been widely used for computer-aided diagnosis of Alzheimer's disease (AD). Given the proven eff...

Enhancing atrial fibrillation detection in PPG analysis with sparse labels through contrastive learning.

Computer methods and programs in biomedicine
BACKGROUND: With the advancements in wearable technology, photoplethysmography (PPG) has emerged as a promising technique for detecting atrial fibrillation (AF) due to its ability to capture cardiovascular information. However, current deep learning-...

Automated detection of arrhythmias using a novel interpretable feature set extracted from 12-lead electrocardiogram.

Computers in biology and medicine
The availability of large-scale electrocardiogram (ECG) databases and advancements in machine learning have facilitated the development of automated diagnostic systems for cardiac arrhythmias. Deep learning models, despite their potential for high ac...

Structured hashing with deep learning for modality, organ, and disease content sensitive medical image retrieval.

Scientific reports
Evidence-based medicine is the preferred procedure among clinicians for treating patients. Content-based medical image retrieval (CBMIR) is widely used to extract evidence from a large archive of medical images. Developing effective CBMIR systems for...

A deep Bi-CapsNet for analysing ECG signals to classify cardiac arrhythmia.

Computers in biology and medicine
- In recent times, the electrocardiogram (ECG) has been considered as a significant and effective screening mode in clinical practice to assess cardiac arrhythmias. Precise feature extraction and classification are considered as essential concerns in...

DCAlexNet: Deep coupled AlexNet for micro facial expression recognition based on double face images.

Computers in biology and medicine
Facial Micro-Expression Recognition (FER) presents challenges due to individual variations in emotional intensity and the complexity of feature extraction. While apex frames offer valuable emotional information, their precise role in FER remains uncl...

Assessing Total Hip Arthroplasty Outcomes and Generating an Orthopedic Research Outcome Database via a Natural Language Processing Pipeline: Development and Validation Study.

JMIR medical informatics
BACKGROUND: Processing data from electronic health records (EHRs) to build research-grade databases is a lengthy and expensive process. Modern arthroplasty practice commonly uses multiple sites of care, including clinics and ambulatory care centers. ...

A semi-supervised learning approach to classify drug attributes in a pharmacy management database: A STROBE-compliant study.

Medicine
With the development of information and communication technology, it has become possible to improve pharmacy management system (PMS) using these technologies. Our study aims to enhance the accuracy of drug attribute classification and recommend appro...

Forecasting trends of rising emergency department chest imaging using machine learning.

Emergency radiology
INTRODUCTION: Imaging studies in the acute care setting, such as the emergency room, have been increasing. In this report, we use the Centers for Medicare and Medicaid services (CMS) database to assess trends in ED chest CT and chest CTA imaging in E...

A Framework for Comparison and Interpretation of Machine Learning Classifiers to Predict Autism on the ABIDE Dataset.

Human brain mapping
Autism is a neurodevelopmental condition affecting ~1% of the population. Recently, machine learning models have been trained to classify participants with autism using their neuroimaging features, though the performance of these models varies in the...