Staffing & Scheduling

Structure-Informed Comprehensive Screening and Risk Prioritization of Organophosphorus Compounds in Human Serum: Revealing the Hidden Burden and Emerging Risks.

Organophosphorus compounds (OPCs) are widely distributed environmental contaminants for which there are increasing health concerns, yet human internal exposure beyond a few well-studied organophosphate esters (OPEs) remains poorly understood. In this study, a structure-informed integrated screening and risk prioritization framework was developed to systematically characterize the internal burden a...

Comparison of dimensionality reduction and feature selection for cognitive task decoding using functional connectivity.

BACKGROUND: Advances in functional magnetic resonance imaging (fMRI) have led to the ability to study the brain across many contexts. However, the large number of features generated by functional connectivity approaches may overfit the data. These problems can be overcome with either feature selection (FS) or dimensionality reduction (DR), which can be applied to less complex models. We utilize tw...

Practical Integration of Bedside Real-Time Lung Ultrasound and Electrical Impedance Tomography for Guiding Personalized Perioperative Lung Protective Ventilation in Cardiothoracic and Vascular Anesthesia.

Patients undergoing cardiothoracic and vascular surgery are at uniquely high risk for postoperative pulmonary complications due to the confluence of s...

Automated Echocardiographic Detection of Congenital Heart Disease Using Artificial Intelligence.

BACKGROUND: Delayed or missed diagnosis of congenital heart disease (CHD) contributes to excess pediatric mortality worldwide. Echocardiography (echo)...

KSNet: Advancing autism prediction via KAN-based graph convolution and multi-source data fusion.

Integrating functional brain networks and Graph Convolutional Networks (GCN) architecture reveal promising diagnostic capabilities for brain disorders...

Measuring Accuracy (Classification Probabilities, Positive, and Negative Predictive Values) of Executive Function Electroencephalogram Metrics in Attention-Deficit/Hyperactivity Disorder Diagnosis: Protocol for and Perspectives From the SINCRONIA Study.

BACKGROUND: Attention deficit/hyperactivity disorder (ADHD) is the most prevalent neurodevelopmental disorder worldwide, affecting approximately 5%-7%...

Building an intelligent brain platform for small and medium-sized enterprises using ChatGLM and Multi-Agent Systems.

Large language models (LLMs) have demonstrated strong capabilities in semantic understanding and text generation. However, their direct application in...

Artificial intelligence (AI) for virtual reality exposure therapy (VRET): A systematic review.

OBJECTIVE: This systematic review maps what is known about using artificial intelligence (AI) to tailor virtual reality exposure therapy (VRET) to bet...

Characterizing heterogeneity and subphenotyping acute respiratory distress syndrome with computed tomography.

Acute respiratory distress syndrome (ARDS) is a heterogeneous clinical syndrome rather than a single disease. Patients who meet the same diagnostic cr...

Capacity building for ethical use of artificial intelligence in health: protocol for a scoping review of training initiatives and gaps in Africa.

INTRODUCTION: Artificial intelligence (AI) is increasingly embedded in health systems, with applications spanning diagnostic imaging, clinical decisio...

Silence Kills 2.0: How Communication Failures Stifle Innovation and Harm Patients.

BACKGROUND: In health care organizations in which silence is the norm, questionable competence, rule-breaking, and mistakes ("crucial moments") often ...

Super-Resolution enhanced deep learning for efficient and accurate urban flood simulation at the street scale.

Efficient and high-precision flood simulation is a critical technology for supporting urban flood risk management and emergency response. To address t...

A hybrid RL-GA-LSTM-AE framework for energy-aware and SLA-driven task scheduling in cloud computing environments.

Multimodal AI for Alzheimer Disease Diagnosis: Systematic Review of Datasets, Models, and Modalities.

BACKGROUND: Early detection of Alzheimer disease (AD) is essential for timely intervention; yet, diagnostic performance varies widely across modalitie...

A cost-sensitive multiclass machine learning framework for postoperative neurosurgical triage (Neuro-TACTIC).

Postoperative placement of patients into a regular ward, an intermediate-care unit (IMC), or an intensive care unit (ICU) is critical for balancing pa...

Contemporary clinical incident analysis methods used within acute care settings: a scoping review.

OBJECTIVE: The objective of this review was to explore and synthesize the research on the use of contemporary incident analysis methods in acute care ...

Longitudinal and Multimodal Recording System to Capture Real-World Patient-Clinician Conversations for AI and Encounter Research: Protocol for an Observational Study.

BACKGROUND: The promise of artificial intelligence (AI) in medicine depends on its ability to learn from data that reflect what matters to patients an...

Using artificial intelligence to predict sexual health outcomes in endometriosis: a decision tree model algorithm.

BACKGROUND: Endometriosis profoundly impairs sexual function through complex interactions between pain, hormonal disturbances, psychological distress,...

Determining the percentage of recycled plastic content in a plastic product.

As global plastic waste continues to rise, accurately identifying and quantifying recycled content in plastic products is critical for developing a ci...

Chemically functionalized cellulose triboelectret nanogenerator for machine-learning-enabled tactile sensing.

Self-powered energy harvesting technology has attracted significant attention for use in biomedical devices, smart sensors, wearables and implantable ...