Emergency Medicine

Machine learning model to predict sepsis in ICU patients with intracerebral hemorrhage.

Patients with intracerebral hemorrhage (ICH) are highly susceptible to sepsis. This study evaluates the efficacy of machine learning (ML) models in predicting sepsis risk in intensive care units (ICUs) patients with ICH. We conducted a retrospective analysis on ICH patients using the MIMIC-IV database, randomly dividing them into training and validation cohorts. We identified sepsis prognostic fac...

Utilizing Molecular Descriptor Importance to Enhance Endpoint Predictions.

Quantitative structure-activity relationship (QSAR) models are essential for predicting endpoints that are otherwise challenging to estimate using other in silico approaches. Developing interpretable models for endpoint prediction is valuable as interpretable models may provide valuable insights into the relationship between molecular structure and observed biological or toxicological properties o...

Machine learning-based prediction of 90-day prognosis and in-hospital mortality in hemorrhagic stroke patients.

This study aims to predict hemorrhagic stroke outcomes, including 90-day prognosis and in-hospital mortality, using machine learning models and SHaple...

Mobile health apps for skin cancer triage in the general population: a qualitative study on healthcare providers' perspectives.

BACKGROUND: Mobile health (mHealth) applications (apps) integrated with artificial intelligence for skin cancer triage are increasingly available to t...

PaniCar: Securing the Perception of Advanced Driving Assistance Systems Against Emergency Vehicle Lighting

The safety of autonomous cars has come under scrutiny in recent years, especially after 16 documented incidents involving Teslas (with autopilot eng...

An automated hip fracture detection, classification system on pelvic radiographs and comparison with 35 clinicians.

Accurate diagnosis of orthopedic injuries, especially pelvic and hip fractures, is vital in trauma management. While pelvic radiographs (PXRs) are wid...

Machine learning-based survival models for predicting rehospitalization of older hip fracture patients: a retrospective cohort study.

PURPOSE: To evaluate machine learning-based survival model roles in predicting rehospitalization after hip fractures to improve reduce the burden on t...

Machine learning-based predictive modelling of mental health in Rwandan Youth.

Globally, mental disorders are a significant burden, particularly in low- and middle-income countries, with high prevalence in Rwanda, especially amon...

Medical machine learning operations: a framework to facilitate clinical AI development and deployment in radiology.

The integration of machine-learning technologies into radiology practice has the potential to significantly enhance diagnostic workflows and patient c...

Early budget impact analysis of AI to support the review of radiographic examinations for suspected fractures in NHS emergency departments (ED).

OBJECTIVE: To develop an early budget impact analysis of and inform future research on the national adoption of a commercially available AI applicatio...

DISARM++: Beyond scanner-free harmonization

Harmonization of T1-weighted MR images across different scanners is crucial for ensuring consistency in neuroimaging studies. This study introduces ...

Comparison of different AI systems for diagnosing sepsis, septic shock, and cardiogenic shock: a retrospective study.

Sepsis, septic shock, and cardiogenic shock are life-threatening conditions associated with high mortality rates, but differentiating them is complex ...

CFD-Based Quantification of Hemodynamic Variables in Cerebral Aneurysms: How Hemodynamics Shape Aneurysm Fate

Cerebral aneurysms are pathological dilations of intracranial arteries that can rupture with devastating consequences, including subarachnoid hemorr...

Adversarial Robustness of Deep Learning Models for Inland Water Body Segmentation from SAR Images

Inland water body segmentation from Synthetic Aperture Radar (SAR) images is an important task needed for several applications, such as flood mappin...

AI-Based Analysis of Abdominal Ultrasound Images to Support Medical Diagnosis in Emergency Departments.

The goal of segmentation in abdominal imaging for emergency medicine is to accurately identify and delineate organs, as well as to detect and localize...

AI-Enhanced Speech Recognition in Triage.

Triage is used in emergency departments to ensure timely patient care according to urgency of treatment. However, triage accuracy and efficiency remai...

Integrated multimodal hierarchical fusion and meta-learning for enhanced molecular property prediction.

Accurately predicting the pharmacological and toxicological properties of molecules is a critical step in the drug development process. Owing to the h...

Editorial Commentary: Large Language Models Like ChatGPT Show Promise, but Clinical Use of Artificial Intelligence Requires Physician Partnership.

Forcing ChatGPT and other large language models to perform roles reserved for physicians and other health care professionals-namely evaluation, manage...

Evaluation of a BERT Natural Language Processing Model for Automating CT and MRI Triage and Protocol Selection.

To evaluate the accuracy of a Bidirectional Encoder Representations for Transformers (BERT) Natural Language Processing (NLP) model for automating tr...

An inherently interpretable AI model improves screening speed and accuracy for early diabetic retinopathy.

Diabetic retinopathy (DR) is a frequent complication of diabetes, affecting millions worldwide. Screening for this disease based on fundus images has ...