Emergency Medicine

Development and validation of a machine learning-based nomogram model integrating LASSO regression and multiple evaluation methods for predicting early sepsis risk in acute abdomen.

BACKGROUND: Sepsis secondary to acute abdomen is a critical driver of clinical deterioration and mortality, with outcomes closely linked to sepsis severity and the timeliness of intervention. However, existing single biomarkers or scoring systems exhibit significant limitations in early sepsis prediction. Using clinical and laboratory data from patients with acute abdomen, this study developed a n...

Development and proof-of-concept of a treatment target recommendation algorithm in the context of cognitive processing therapy.

Background: Cognitive Processing Therapy (CPT) is an effective, widely supported treatment for PTSD, but patient response varies considerably. Optimally targeting maladaptive trauma-related beliefs (stuck points) may significantly enhance treatment outcomes. This study evaluated the feasibility of developing a recommendation algorithm that helps clinicians identify specific patient-endorsed stuck ...

Reimagining intensive care unit recovery: The Critical Illness Survivorship Programme for humanising post-intensive care unit care - A discussion paper.

BACKGROUND: Survivorship after critical illness is often characterised by fragmented recovery and lingering cognitive, psychological, and physical imp...

Deciphering the air pollution-osteoarthritis relationship: insights from network toxicology, integrated machine learning, and molecular docking.

Mounting evidence suggests an association between air pollution and the pathogenesis of osteoarthritis (OA), yet the underlying molecular mechanisms r...

Multidimensional analysis of postoperative nursing challenges in oral cancer surgery: a retrospective study of 245 patients and proposal of the PROTECT mode.

PURPOSE: The quality of postoperative care for oral tumors critically influences patient prognosis and quality of life; however, current nursing syste...

Ensemble Machine Learning Models for Predicting Patients With High Usage: Model Validation and Economic Impact Analysis.

BACKGROUND: Machine learning models are increasingly used to predict patients at risk of high health care usage for targeted interventions. OBJECTIVE:...

Combining multimodal fatigue fracture surface images for analysis with a CNN.

This work uses three different modalities, namely SEM, BSE and scanning white light interference (SWLI) to image fatigue fracture surfaces of Ti-6Al-4...

Interpretable spatiotemporal machine learning reveals hydrogeological and climatic controls on groundwater heavy metal transport in acid mine drainage.

Acid mine drainage (AMD) generated by sulfide-bearing waste rock dumps poses persistent risks to groundwater through acidic leachate and heavy-metal m...

Exploring an Intermediate Colorectal Cancer Screening Test Based on Stool Proteomics and Machine Learning for Optimizing the Selection of Patients for Colonoscopy Identified from FIT.

The fecal immunochemical test (FIT) for detecting fecal occult blood, used alone or in combination with other stool biomarkers, has been demonstrated ...

Multi-level evidence reveals KCTD15 and FAM19A2 as key targets in PFOA/PFOS-mediated PCOS pathogenesis.

BACKGROUND: Polycystic ovary syndrome (PCOS), a common endocrine disorder, has been linked to environmental factors such as exposure to per- and polyf...

Automated Non-Invasive Burn Diagnostic System for Healthcare using Artificial Intelligence: AMBUSH-AI.

OBJECTIVE: Develop technology to predict burn wound depth using a combination of FDA approved ultrasound modalities and interpretation of these images...

Machine Learning for Predicting Stroke Risk Stratification Using Multiomics Data: Systematic Review.

BACKGROUND: Stroke is a complex, multidimensional disorder influenced by interacting inflammatory, immune, coagulation, endothelial, and metabolic pat...

Automated Risk Assessment of Opioid Use: Analysis Using Pre-Trained Transformers on Social Media Data.

BACKGROUND: The illegal use of opioids has emerged as a major global public health concern, contributing to widespread addiction and a growing number ...

HEDGE Index: A Data-Driven Framework Overcoming the Limitations of TEFs for Prioritizing Polycyclic Aromatic Hydrocarbon Risks.

Conventional risk assessment of airborne polycyclic aromatic hydrocarbons (PAHs), based on carcinogenicity-focused toxic equivalency factors (TEFs), l...

Ultra-Fast Sub-Minute Brain MRI with Deep-Learning-reconstruction for Anesthesia-Free Emergency Imaging in Children.

PURPOSE: As MRI frequently requires general anesthesia in pediatric patients, there is an undersupply in clinical routine. This may result in delayed ...

Development of a 3D Convolutional Neural Network for the Triage of High-Priority Oral and Maxillofacial CBCT Scans.

This project aims to establish a triage system for oral and maxillofacial cone-beam computed tomography (CBCT) scans by developing a neural network to...

Advancements in ex vivo bone biomechanics: multimodal technologies and their integration with artificial intelligence.

Bone biomechanics is essential for understanding how bone structure, composition, and mechanical function interact in healthy and diseased states. Con...

Development and validation of an in-hospital cardiogenic shock prediction model for AMI patients based on machine learning.

Prediction of hemorrhagic transformation after intravenous thrombolysis in acute anterior circulation ischemic stroke through radiomics-based machine learning models.

BACKGROUND: Hemorrhagic transformation (HT) and its associated neurological deterioration remain major concerns that limit decision-making in thrombol...

An Artificial Intelligence-Selected Multifunctional Oligopeptide Coated Biphasic Calcium Phosphate (BCP) Scaffold Promotes Bone Defect Repair.

Bone defects caused by inflammation, trauma, tumor resection, or tooth extraction present persistent clinical challenges, primarily because of the lim...