Head Trauma

Uncertainty-aware spatio-temporal contrastive graph neural networks for cyber financial fraud detection and risk management.

Financial fraud detection requires screening massive transaction networks where evolving topologies, extreme label sparsity, and asymmetric misclassification costs make traditional classification paradigms ineffective. We propose ST-CGNN, a spatio-temporal contrastive graph neural network that frames operational screening as a multi-task learning problem in which a shared encoder is supervised by ...

Falls in Assisted Living Facilities: Can Artificial Intelligence Improve Documentation and Reduce Injury?

PURPOSE: To evaluate whether an artificial intelligence (AI)-assisted surveillance device, AUGi, improves documentation of falls and injury rates in assisted living facilities (ALFs). METHOD: The current study represents a secondary analysis of existing facility fall documentation data. An interrupted time series design analyzed monthly fall data from 9 months before and 4 months after AUGi instal...

Synthetic dataset of PLGA and liposome nanocarrier formulations for brain-cancer-relevant drug delivery, release, blood-brain-barrier transport, and paired cell-viability proxies.

This data article describes an original synthetic/simulated dataset designed to support materials-informatics and comparative formulation analysis of ...

Development and validation of a machine learning model predicting post-tonsillectomy hemorrhage.

PURPOSE: To develop and validate machine learning models to predict post-tonsillectomy hemorrhage. METHODS: This was a machine learning analysis of a ...

Investigating the utilization of LIME-enhanced AI and generated data for evaluating the risks associated with radial artery puncture procedures.

Radial artery puncture, a routine arterial cannulation procedure for perioperative and critical care settings, is limited by high first-attempt failur...

A cannabis use Reddit dataset for aspect-based sentiment analysis.

Using publicly accessible Reddit posts, we developed a manually annotated dataset for traditional and aspect-based sentiment analysis (ABSA) of cannab...

Quality of AI-generated post-operative patient education after minimally invasive gynecologic surgery: A comparative analysis.

STUDY OBJECTIVE: To compare the quality of AI-generated responses to gynecologic post-operative questions with educational materials published by prof...

Prediction models for postpartum post-traumatic stress disorder: a systematic review and meta-analysis.

BACKGROUND: Despite the increasing number of studies on prediction models for identifying the risk of postpartum post-traumatic stress disorder (PP-PT...

Proteomic profiling of bone for the estimation of post-mortem interval and post-mortem submersion interval: a systematic review.

Accurate estimation of the Post-Mortem Interval (PMI) and Post-Mortem Submersion Interval (PMSI) remains a persistent challenge in forensic science, e...

Decoding the role of H19 in cholestatic liver injury using snRNA-seq, spatial transcriptomics, and machine learning-based disease prediction.

BACKGROUND: Despite recent advances, Primary Sclerosing Cholangitis (PSC)-a chronic obstructive biliary disease-still lacks effective therapies to pre...

Post-quantum cognitive zero trust architecture for healthcare IoT devices.

Healthcare IoT systems increasingly rely on interconnected, resource-constrained devices that are vulnerable to both classical and emerging quantum-en...

Machine Learning-Based Periprocedural Prediction Model for No-Reflow Risk After Percutaneous Coronary Intervention in Patients With Acute Coronary Syndrome.

Coronary no-reflow (NR) after percutaneous coronary intervention (PCI) predicts adverse prognosis in patients with acute coronary syndrome (ACS). This...

Importance ranking and predictive model construction of WMSDs risk factors among shift-working nurses based on multiple machine-learning algorithms.

BACKGROUND AND OBJECTIVE: Nurses are essential for safeguarding public health, and their physical condition directly affects care quality and patient ...

State-dependent neuromodulation reveals link between online and offline corticospinal excitability.

The post-intervention effects of non-invasive neuromodulation techniques are critical for their translational potential in neurorehabilitation and dep...

Machine learning-based prediction of a high-risk kidney function trajectory class after acute kidney injury.

OBJECTIVES: Monitoring kidney function after acute kidney injury (AKI) hospitalisation is essential for identifying patients at risk of rapid progress...

The solute carrier transporter SLCO5A1 as a novel therapeutic target for the treatment of myocardial infarction-induced heart failure.

Heart failure (HF) following myocardial infarction (MI) remains a major threat to health worldwide. While transcriptomics has revealed numerous genes ...

Gray matter brain age predicts cognitive outcome one year after ischemic stroke.

BackgroundPredicting post-stroke cognitive impairment (PSCI) remains challenging.ObjectiveThis study validated two brain age metrics-Gray Matter Brain...

Large Language Model-Generated Patient Instructions for Prescriptions in Primary Health Care: Preclinical Algorithm Validation.

BACKGROUND: The application of generative artificial intelligence to simplify medication use instructions has the potential to enhance people's health...

Navigating the regulatory landscape of herbal medicines: A global comparison of India, China, Europe, the United States and Japan.

The use of herbal medicines is expanding rapidly worldwide, but regional regulatory systems vary greatly, leading to variations in quality, safety and...

Automatic Localization and Classification of Crohn's Disease Activity in Computed Tomography Enterography Images Using Deep Learning.

Rapid and accurate localization and activity grading of Crohn's disease (CD) lesions on computed tomography enterography (CTE) images enhance the diag...