Emergency Medicine

Development and Validation of a Machine Learning Model to Predict Oral Anticoagulant Use in Stroke From Prothrombin Time-International Normalized Ratio and Activated Partial Thromboplastin Time.

BACKGROUND: Timely recognition of oral anticoagulant use is critical in acute stroke but is often hampered by impaired consciousness and unavailable medication history. We investigated whether routinely available coagulation tests, prothrombin time-international normalized ratio and activated partial thromboplastin time, paired with machine learning can identify anticoagulant exposure at presentat...

Practical adaptability of a pre-hospital prognostic prediction model for patients following out-of-hospital cardiac arrest during the COVID-19 pandemic.

OBJECTIVES: The overwhelmed situation under the COVID-19 pandemic has worsened the quality of emergency medical care and the mortality rate due to out-of-hospital cardiac arrest (OHCA). However, there has been no research conducted for the validation of prognostic prediction models for OHCA patients using data collected during the pandemic. We sought to develop a pre-hospital prediction model for ...

ABC-YOLO: Automated skin burn depth classification using YOLO architectures.

Accurate classification of skin burn depth is vital for determining appropriate treatment and accelerating the healing process. This study conducts a ...

Development and Validation of Machine Learning Model to Predict Refractory Septic Shock.

This study developed and validated a machine learning model to predict refractory septic shock in patients with sepsis admitted to a tertiary care cen...

FAD-MIL: a weakly supervised fracture detection model based on X-ray images.

Fractures are among the most common presentations to emergency and orthopedic services, yet radiograph interpretation remains inconsistent, particular...

Nanotoxicology meets smart polymers: advancing safety-by-design nanomaterials.

Smart polymers have played great role in enhancing nanomedical application areas in drug delivery, diagnosis and environment sensitive treatment syste...

A computational model for predicting shock in patients at risk of urosepsis after urolithiasis surgery.

Influencing factors and predictive model for in-hospital complications in patients with spontaneous intracerebral hemorrhage: A single-center retrospective study.

Integration of detection and tracking networks for automated rib multiplanar reconstruction: a feasibility study for fracture diagnosis.

OBJECTIVE: Rib fractures are common yet time-consuming to diagnose. This study explores automation via multiplanar reconstruction and intelligent dete...

Diagnostic performance and confidence of an optimized deep-learning algorithm for the detection of intracranial hemorrhages.

OBJECTIVES: To evaluate the performance of an optimized deep-learning-based algorithm (AI) for the detection and subtyping of intracranial hemorrhage ...

Integrating Large Language Models Into Trauma Education for Medical Students: Randomized Controlled Pilot Trial.

BACKGROUND: The exponential growth of medical knowledge presents a paradox for modern medical education. While access to information is immediate, app...

Artificial intelligence in emergency skeletal X-ray: post-deployment monitoring and clinical impact of incorrect AI results.

OBJECTIVES: This study evaluates the impact of the artificial intelligence (AI) application BoneViewTM within a radiographer-supervised clinical workf...

A metabolomics-driven machine learning model for osteoporosis risk prediction.

Osteoporosis, marked by decreased bone mineral density (BMD), poses a major public health concern by increasing fracture risk, lowering quality of lif...

Decoding core molecular mechanisms of heat-stress tolerance in Brassica napus using transcriptomics and machine learning.

Integration of conventional bioinformatics approaches with advanced machine learning and explainable AI identified 45 candidate genes and 21 top featu...

Accuracy of the large language model ChatGPT in adult emergency department triage: a systematic review and meta-analysis.

LAMB1 regulates COL3A1 and RAC1 expression during subchorionic hemorrhage progression.

BACKGROUND: Subchorionic hemorrhage (SCH) is characterized by a fluid-filled hypoechoic area in early pregnancy. This study investigates how laminin s...

Life events extraction from healthcare notes for veteran acute suicide risk prediction.

OBJECTIVE: Predictive models of suicide risk have focused on features extracted from structured data found in electronic health records, with limited ...

Jigsaws soup: Improved laparoscopic surgical gesture classification, and skill level evaluation using model souping.

Laparoscopy has revolutionised surgery, with faster recovery and less trauma for patients. However, extensive training is needed to gain the required ...

Machine Learning in the ICU: Predicting Mortality in Patients with Carbapenem-Resistant Gram-Negative Bacilli Bloodstream Infections.

Background and ObjectiveMultidrug and carbapenem resistant gram-negative bacilli bloodstream infections cause high mortality in intensive care units (...

Artificial Intelligence-powered tiered early warning framework addressing high false alarm rates for in-hospital mortality prediction.

Alert fatigue remains a major barrier to the effective deployment of predictive models in emergency care, particularly in the context of rare but crit...