Staffing & Scheduling

Uncertainty-aware diabetic retinopathy detection using deep learning enhanced by Bayesian approaches.

Deep learning-based medical image analysis has shown strong potential in disease categorization, segmentation, detection, and even prediction. However, in high-stakes and complex domains like healthcare, the opaque nature of these models makes it challenging to trust predictions, particularly in uncertain cases. This sort of uncertainty can be crucial in medical image analysis; diabetic retinopath...

Revolutionizing surgery: AI and robotics for precision, risk reduction, and innovation.

Artificial intelligence and robotics are revolutionizing surgical practices by enhancing precision, efficiency, and patient outcomes. With global healthcare systems increasingly adopting AI-driven technologies, the integration of robotics in surgery addresses critical challenges such as surgical accuracy, minimally invasive techniques, and healthcare accessibility. However, disparities in access a...

Uncertainty modeling for inductive knowledge graph embedding.

In the process of refining Knowledge Graphs (KGs), new entities emerge, and old entities evolve, which usually updates their attribute information and...

Real-Time Analytics and AI for Managing No-Show Appointments in Primary Health Care in the United Arab Emirates: Before-and-After Study.

BACKGROUND: Primary health care (PHC) services face operational challenges due to high patient volumes, leading to complex management needs. Patients ...

Enhancing Medical Student Engagement Through Cinematic Clinical Narratives: Multimodal Generative AI-Based Mixed Methods Study.

BACKGROUND: Medical students often struggle to engage with and retain complex pharmacology topics during their preclinical education. Traditional teac...

Enhancing lipid identification in LC-HRMS data through machine learning-based retention time prediction.

The comprehensive identification of peaks in untargeted lipidomics using LC-MS/MS remains a significant challenge. Confidence in lipid annotation can ...

Predicting rapid progression in knee osteoarthritis: a novel and interpretable automated machine learning approach, with specific focus on young patients and early disease.

OBJECTIVES: To facilitate the stratification of patients with osteoarthritis (OA) for new treatment development and clinical trial recruitment, we cre...

A Systematic Review of Features Forecasting Patient Arrival Numbers.

Adequate nurse staffing is crucial for quality healthcare, necessitating accurate predictions of patient arrival rates. These forecasts can be determi...

Utilizing Artificial neural networks (ANN) to regulate Smart cities for sustainable Urban Development and Safeguarding Citizen rights.

The advent of smart cities has brought about a paradigm shift in urban management and citizen engagement. By leveraging technological advancements, ci...

Cerebral compliance assessment from intracranial pressure waveform analysis: Is a positional shift-related increase in intracranial pressure predictable?

Real-time monitoring of intracranial pressure (ICP) is a routine part of neurocritical care in the management of brain injury. While mainly used to de...

Empowering the Sports Scientist with Artificial Intelligence in Training, Performance, and Health Management.

Artificial Intelligence (AI) is transforming the field of sports science by providing unprecedented insights and tools that enhance training, performa...

Machine learning-based prediction of non-aeration linear alkylbenzene sulfonate mineralization in an oxygenic microalgal-bacteria biofilm.

Microalgal-bacteria biofilm shows great potential in low-cost greywater treatment. Accurately predicting treated greywater quality is of great signifi...

The analysis of urban collaborative governance in public health emergencies with fuzzy theory based on BP algorithm.

This study seeks to improve urban supply chain management and collaborative governance in the context of public health emergencies (PHEs) by integrati...

The Potential of SHAP and Machine Learning for Personalized Explanations of Influencing Factors in Myopic Treatment for Children.

The rising prevalence of myopia is a significant global health concern. Atropine eye drops are commonly used to slow myopia progression in children, ...

A machine learning prediction model for total shoulder arthroplasty procedure duration: an evaluation of surgeon, patient, and shoulder-specific factors.

BACKGROUND: Operating room efficiency is of paramount importance for scheduling, cost efficiency, and to allow for the high operating volume required ...

Programmable ultrasound-mediated swarms manipulation of bacteria-red blood cell microrobots for tumor-specific thrombosis and robust photothermal therapy.

Despite the excellent advantages of biomicrorobots, such as autonomous navigation and targeting actuation, effective penetration and retention to deep...

An Automatic and End-to-End System for Rare Disease Knowledge Graph Construction Based on Ontology-Enhanced Large Language Models: Development Study.

BACKGROUND: Rare diseases affect millions worldwide but sometimes face limited research focus individually due to low prevalence. Many rare diseases d...

Target informed client recruitment for efficient federated learning in healthcare.

BACKGROUND: Modern machine learning and deep learning methods have been widely incorporated in decision making processes in healthcare in the form of ...

Low-carbohydrate diet score and chronic obstructive pulmonary disease: a machine learning analysis of NHANES data.

BACKGROUND: Recent research has identified the Low-Carbohydrate Diet (LCD) score as a novel biomarker, with studies showing that LCDs can reduce carbo...

Revolutionizing market surveillance: customer relationship management with machine learning.

In the telecommunications industry, predicting customer churn is essential for retaining clients and sustaining profitability. Traditional CRM systems...