AI Medical Compendium Journal:
Scandinavian journal of trauma, resuscitation and emergency medicine

Showing 1 to 10 of 12 articles

Utilization of non-invasive ventilation before prehospital emergency anesthesia in trauma - a cohort analysis with machine learning.

Scandinavian journal of trauma, resuscitation and emergency medicine Mar 3, 2025
BACKGROUND: For preoxygenation, German guidelines consider non-invasive ventilation (NIV) as a possible method in prehospital trauma care in the absence of aspiration, severe head or face injuries, unconsciousness, or patient non-compliance. As data ...
Retrospective Studies Female Adult Male Humans Machine Learning Anesthesia Germany Wounds and Injuries Middle Aged Noninvasive Ventilation Cohort Studies Emergency Medical Services Registries
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Evaluating the accuracy and reliability of AI chatbots in disseminating the content of current resuscitation guidelines: a comparative analysis between the ERC 2021 guidelines and both ChatGPTs 3.5 and 4.

Scandinavian journal of trauma, resuscitation and emergency medicine Sep 26, 2024
AIM OF THE STUDY: Artificial intelligence (AI) chatbots are established as tools for answering medical questions worldwide. Healthcare trainees are increasingly using this cutting-edge technology, although its reliability and accuracy in the context ...
Practice Guidelines as Topic Resuscitation Humans Artificial Intelligence Prospective Studies Reproducibility of Results Information Dissemination
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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 Jan 23, 2024
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...
Pilot Projects Randomized Controlled Trials as Topic Technology Emergency Service, Hospital Prospective Studies Humans Adult Machine Learning Risk Assessment
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Deep-learning model for screening sepsis using electrocardiography.

Scandinavian journal of trauma, resuscitation and emergency medicine Oct 3, 2021
BACKGROUND: Sepsis is a life-threatening organ dysfunction and a major healthcare burden worldwide. Although sepsis is a medical emergency that requires immediate management, screening for the occurrence of sepsis is difficult. Herein, we propose a d...
Retrospective Studies SARS-CoV-2 Deep Learning Sepsis Humans COVID-19 Electrocardiography
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Trends in reasons for emergency calls during the COVID-19 crisis in the department of Gironde, France using artificial neural network for natural language classification.

Scandinavian journal of trauma, resuscitation and emergency medicine Mar 31, 2021
OBJECTIVES: During periods such as the COVID-19 crisis, there is a need for responsive public health surveillance indicators in order to monitor both the epidemic growth and potential public health consequences of preventative measures such as lockdo...
Emergency Service, Hospital Social Isolation France Self-Injurious Behavior Stress, Psychological Public Health Surveillance Natural Language Processing Communicable Disease Control Hotlines Humans Neural Networks, Computer COVID-19 Female SARS-CoV-2 Adult Male
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Development of machine learning models to predict RT-PCR results for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in patients with influenza-like symptoms using only basic clinical data.

Scandinavian journal of trauma, resuscitation and emergency medicine Dec 1, 2020
BACKGROUND: Reverse Transcription-Polymerase Chain Reaction (RT-PCR) for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) diagnosis currently requires quite a long time span. A quicker and more efficient diagnostic tool in emergency depar...
Reverse Transcriptase Polymerase Chain Reaction Sensitivity and Specificity Male Middle Aged Humans Diagnosis, Computer-Assisted Machine Learning Female Software SARS-CoV-2 COVID-19 Aged Retrospective Studies
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Artificial intelligence algorithm for predicting cardiac arrest using electrocardiography.

Scandinavian journal of trauma, resuscitation and emergency medicine Oct 6, 2020
BACKGROUND: In-hospital cardiac arrest is a major burden in health care. Although several track-and-trigger systems are used to predict cardiac arrest, they often have unsatisfactory performances. We hypothesized that a deep-learning-based artificial...
Humans Algorithms Electrocardiography Patient Transfer Aged Retrospective Studies Heart Arrest Male Middle Aged Intensive Care Units Deep Learning Female Hospitalization
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Real-time AI prediction for major adverse cardiac events in emergency department patients with chest pain.

Scandinavian journal of trauma, resuscitation and emergency medicine Sep 11, 2020
BACKGROUND: A big-data-driven and artificial intelligence (AI) with machine learning (ML) approach has never been integrated with the hospital information system (HIS) for predicting major adverse cardiac events (MACE) in patients with chest pain in ...
Models, Statistical Taiwan Emergency Service, Hospital Myocardial Infarction Chest Pain Mortality Sensitivity and Specificity Male Middle Aged Risk Assessment Aged Young Adult Adult Humans Artificial Intelligence Algorithms Female
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Identifying the relative importance of predictors of survival in out of hospital cardiac arrest: a machine learning study.

Scandinavian journal of trauma, resuscitation and emergency medicine Jun 25, 2020
INTRODUCTION: Studies examining the factors linked to survival after out of hospital cardiac arrest (OHCA) have either aimed to describe the characteristics and outcomes of OHCA in different parts of the world, or focused on certain factors and wheth...
Male Age Factors Emergency Medical Services Heart Rate Female Sweden Aged Survival Analysis Time-to-Treatment Cardiopulmonary Resuscitation Out-of-Hospital Cardiac Arrest Humans Machine Learning Algorithms
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Prediction of in-hospital mortality in patients with post traumatic brain injury using National Trauma Registry and Machine Learning Approach.

Scandinavian journal of trauma, resuscitation and emergency medicine May 27, 2020
BACKGROUND: The use of machine learning techniques to predict diseases outcomes has grown significantly in the last decade. Several studies prove that the machine learning predictive techniques outperform the classical multivariate techniques. We aim...
Hospital Mortality Adult Brain Injuries, Traumatic Area Under Curve Sensitivity and Specificity Multivariate Analysis Humans Registries Male Machine Learning Middle Aged Female
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