AIMC Topic: Emergency Service, Hospital
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Impact of ECG Characteristics on the Performance of an Artificial Intelligence Enabled ECG for Predicting Left Ventricular Dysfunction.
Circulation. Arrhythmia and electrophysiology
May 17, 2021
Development and Validation of Machine Learning Models to Predict Admission From Emergency Department to Inpatient and Intensive Care Units.
Annals of emergency medicine
May 8, 2021
STUDY OBJECTIVE: This study aimed to develop and validate 2 machine learning models that use historical and current-visit patient data from electronic health records to predict the probability of patient admission to either an inpatient unit or ICU a...
Predicting Ambulance Patient Wait Times: A Multicenter Derivation and Validation Study.
Annals of emergency medicine
May 8, 2021
STUDY OBJECTIVE: To derive and internally and externally validate machine-learning models to predict emergency ambulance patient door-to-off-stretcher wait times that are applicable to a wide variety of emergency departments.
Assessment of an AI Aid in Detection of Adult Appendicular Skeletal Fractures by Emergency Physicians and Radiologists: A Multicenter Cross-sectional Diagnostic Study.
Radiology
May 4, 2021
Background The interpretation of radiographs suffers from an ever-increasing workload in emergency and radiology departments, while missed fractures represent up to 80% of diagnostic errors in the emergency department. Purpose To assess the performan...
Humans
Artificial Intelligence
Physicians
Cross-Sectional Studies
Aged, 80 and over
Retrospective Studies
Male
Young Adult
Radiologists
Female
Middle Aged
Adult
Reproducibility of Results
Radiographic Image Interpretation, Computer-Assisted
Adolescent
Fractures, Bone
Sensitivity and Specificity
Aged
Emergency Service, Hospital
Diagnostic and prognostic capabilities of a biomarker and EMR-based machine learning algorithm for sepsis.
Clinical and translational science
May 2, 2021
Sepsis is a major cause of mortality among hospitalized patients worldwide. Shorter time to administration of broad-spectrum antibiotics is associated with improved outcomes, but early recognition of sepsis remains a major challenge. In a two-center ...
Task-sharing with artificial intelligence: a design hypothesis for an Emergency Unit in sub-Saharan Africa.
The Pan African medical journal
Apr 20, 2021
In sub-Saharan Africa, there is a significant unmet need for emergency care, with a shortage of trained providers. One model to increase the number of providers is to task-share: roles traditionally filled by clinicians are shared with lay workers wh...
Machine learning methods to predict mechanical ventilation and mortality in patients with COVID-19.
PloS one
Apr 1, 2021
BACKGROUND: The Coronavirus disease 2019 (COVID-19) pandemic has affected millions of people across the globe. It is associated with a high mortality rate and has created a global crisis by straining medical resources worldwide.
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...
Automated remote decision-making algorithm as a primary triage system using machine learning techniques.
Physiological measurement
Mar 11, 2021
OBJECTIVE: An objective and convenient primary triage procedure is needed for prioritizing patients who need help in mass casualty incident (MCI) situations, where there is a lack of medical staff and available resources. This study aimed to develop ...
Validation of the Artificial Intelligence-Based Predictive Optimal Trees in Emergency Surgery Risk (POTTER) Calculator in Emergency General Surgery and Emergency Laparotomy Patients.
Journal of the American College of Surgeons
Mar 8, 2021
BACKGROUND: The Predictive Optimal Trees in Emergency Surgery Risk (POTTER) tool is an artificial intelligence-based calculator for the prediction of 30-day outcomes in patients undergoing emergency operations. In this study, we sought to assess the ...
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