AI Medical Compendium Topic

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Emergency Service, Hospital

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A machine learning algorithm-based predictive model for pressure injury risk in emergency patients: A prospective cohort study.

International emergency nursing
OBJECTIVES: To construct pressure injury risk prediction models for emergency patients based on different machine learning algorithms, to optimize the best model, and to provide a suitable assessment tool for preventing the occurrence of pressure inj...

Real-time machine learning-assisted sepsis alert enhances the timeliness of antibiotic administration and diagnostic accuracy in emergency department patients with sepsis: a cluster-randomized trial.

Internal and emergency medicine
Machine learning (ML) has been applied in sepsis recognition across different healthcare settings with outstanding diagnostic accuracy. However, the advantage of ML-assisted sepsis alert in expediting clinical decisions leading to enhanced quality fo...

AI in the ED: Assessing the efficacy of GPT models vs. physicians in medical score calculation.

The American journal of emergency medicine
BACKGROUND AND AIMS: Artificial Intelligence (AI) models like GPT-3.5 and GPT-4 have shown promise across various domains but remain underexplored in healthcare. Emergency Departments (ED) rely on established scoring systems, such as NIHSS and HEART ...

A comparative study of explainable ensemble learning and logistic regression for predicting in-hospital mortality in the emergency department.

Scientific reports
This study addresses the challenges associated with emergency department (ED) overcrowding and emphasizes the need for efficient risk stratification tools to identify high-risk patients for early intervention. While several scoring systems, often bas...

Prediction of emergency department revisits among child and youth mental health outpatients using deep learning techniques.

BMC medical informatics and decision making
BACKGROUND: The proportion of Canadian youth seeking mental health support from an emergency department (ED) has risen in recent years. As EDs typically address urgent mental health crises, revisiting an ED may represent unmet mental health needs. Ac...

Machine learning models for predicting unscheduled return visits to an emergency department: a scoping review.

BMC emergency medicine
BACKGROUND: Unscheduled return visits (URVs) to emergency departments (EDs) are used to assess the quality of care in EDs. Machine learning (ML) models can incorporate a wide range of complex predictors to identify high-risk patients and reduce error...

Machine learning in the prediction of massive transfusion in trauma: a retrospective analysis as a proof-of-concept.

European journal of trauma and emergency surgery : official publication of the European Trauma Society
PURPOSE: Early administration and protocolization of massive hemorrhage protocols (MHP) has been associated with decreases in mortality, multiorgan system failure, and number of blood products used. Various prediction tools have been developed for th...

Machine learning for risk stratification in the emergency department (MARS-ED) study protocol for a randomized controlled pilot trial on the implementation of a prediction model based on machine learning technology predicting 31-day mortality in the emergency department.

Scandinavian journal of trauma, resuscitation and emergency medicine
BACKGROUND: Many prediction models have been developed to help identify emergency department (ED) patients at high risk of poor outcome. However, these models often underperform in clinical practice and their actual clinical impact has hardly ever be...

Repeatability, reproducibility, and diagnostic accuracy of a commercial large language model (ChatGPT) to perform emergency department triage using the Canadian triage and acuity scale.

CJEM
PURPOSE: The release of the ChatGPT prototype to the public in November 2022 drastically reduced the barrier to using artificial intelligence by allowing easy access to a large language model with only a simple web interface. One situation where Chat...