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Identifying and Reporting Dependent Adult abuse

Latest AI and machine learning research in identifying and reporting dependent adult abuse for healthcare professionals.

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Showing 85-105 of 1,915 articles
GPT for RCTs? Using AI to determine adherence to clinical trial reporting guidelines.

OBJECTIVES: Adherence to established reporting guidelines can improve clinical trial reporting stand...

LI-RADS-based hepatocellular carcinoma risk mapping using contrast-enhanced MRI and self-configuring deep learning.

BACKGROUND: Hepatocellular carcinoma (HCC) is often diagnosed using gadoxetate disodium-enhanced mag...

A malware classification method based on directed API call relationships.

In response to the growing complexity of network threats, researchers are increasingly turning to ma...

Unveiling the power of R: a comprehensive perspective for laboratory medicine data analysis.

R language has gained traction in laboratory medicine for its statistical power and dynamic tools li...

scMDCL: A Deep Collaborative Contrastive Learning Framework for Matched Single-Cell Multiomics Data Clustering.

Single-cell multiomics clustering integrates multiple omics data to analyze cellular heterogeneity a...

Decoding Drug Response With Structurized Gridding Map-Based Cell Representation.

A thorough understanding of cell-line drug response mechanisms is crucial for drug development, repu...

Efficient Brain Tumor Detection and Segmentation Using DN-MRCNN With Enhanced Imaging Technique.

This article proposes a method called DenseNet 121-Mask R-CNN (DN-MRCNN) for the detection and segme...

Spatial analysis of air pollutant exposure and its association with metabolic diseases using machine learning.

BACKGROUND: Metabolic diseases (MDs), exemplified by diabetes, hypertension, and dyslipidemia, have ...

Toward a rapid, sensitive, user-friendly, field-deployable artificial intelligence tool for enhancing African swine fever diagnosis and reporting.

OBJECTIVE: African swine fever (ASF) is a lethal and highly contagious transboundary animal disease ...

Reporting Quality of AI Intervention in Randomized Controlled Trials in Primary Care: Systematic Review and Meta-Epidemiological Study.

BACKGROUND: The surge in artificial intelligence (AI) interventions in primary care trials lacks a s...

A systematic review of machine learning-based prognostic models for acute pancreatitis: Towards improving methods and reporting quality.

BACKGROUND: An accurate prognostic tool is essential to aid clinical decision-making (e.g., patient ...

Machine learning or traditional statistical methods for predictive modelling in perioperative medicine: A narrative review.

Prediction of outcomes in perioperative medicine is key to decision-making and various prediction mo...

AI for glaucoma, Are we reporting well? a systematic literature review of DECIDE-AI checklist adherence.

BACKGROUND/OBJECTIVES: This systematic literature review examines the quality of early clinical eval...

EMBANet: A flexible efficient multi-branch attention network.

Recent advances in the design of convolutional neural networks have shown that performance can be en...

Artificial intelligence for direct-to-physician reporting of ambulatory electrocardiography.

Developments in ambulatory electrocardiogram (ECG) technology have led to vast amounts of ECG data t...

ADR-DQPU: A Novel ADR Signal Detection Using Deep Reinforcement and Positive-Unlabeled Learning.

The medical community has grappled with the challenge of analysis and early detection of severe and ...

Spatial heterogeneity effect of built environment on traffic safety using geographically weighted atrous convolutions neural network.

The built environment exerts a significant influence on the frequency and severity of traffic accide...

Around the EQUATOR With Clin-STAR: AI-Based Randomized Controlled Trial Challenges and Opportunities in Aging Research.

The CONSORT 2010 statement is a guideline that provides an evidence-based checklist of minimum repor...

FLANet: A multiscale temporal convolution and spatial-spectral attention network for EEG artifact removal with adversarial training.

Denoising artifacts, such as noise from muscle or cardiac activity, is a crucial and ubiquitous conc...

Deep learning powered single-cell clustering framework with enhanced accuracy and stability.

Single-cell RNA sequencing (scRNA-seq) has revolutionized the field of cellular diversity research. ...

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