AIMC Topic: Emergency Service, Hospital
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Effect of deep learning-based assistive technology use on chest radiograph interpretation by emergency department physicians: a prospective interventional simulation-based study.
BMC medical informatics and decision making
Nov 8, 2021
BACKGROUND: Interpretation of chest radiographs (CRs) by emergency department (ED) physicians is inferior to that by radiologists. Recent studies have investigated the effect of deep learning-based assistive technology on CR interpretation (DLCR), al...
Development and Validation of a Deep Learning Strategy for Automated View Classification of Pediatric Focused Assessment With Sonography for Trauma.
Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
Nov 6, 2021
OBJECTIVE: Pediatric focused assessment with sonography for trauma (FAST) is a sequence of ultrasound views rapidly performed by clinicians to diagnose hemorrhage. A technical limitation of FAST is the lack of expertise to consistently acquire all re...
Machine learning versus traditional methods for the development of risk stratification scores: a case study using original Canadian Syncope Risk Score data.
Internal and emergency medicine
Nov 3, 2021
Artificial Intelligence and machine learning (ML) methods are promising for risk-stratification, but the added benefit over traditional statistical methods remains unclear. We compared predictive models developed using machine learning (ML) methods t...
Using explainable machine learning to identify patients at risk of reattendance at discharge from emergency departments.
Scientific reports
Nov 2, 2021
Short-term reattendances to emergency departments are a key quality of care indicator. Identifying patients at increased risk of early reattendance could help reduce the number of missed critical illnesses and could reduce avoidable utilization of em...
Machine learning techniques for mortality prediction in emergency departments: a systematic review.
BMJ open
Nov 2, 2021
OBJECTIVES: This systematic review aimed to assess the performance and clinical feasibility of machine learning (ML) algorithms in prediction of in-hospital mortality for medical patients using vital signs at emergency departments (EDs).
Advances in Appropriate Postoperative Triage and the Role of Real-time Machine-Learning Models: The Goldilocks Dilemma.
JAMA network open
Nov 1, 2021
Improving Machine Learning 30-Day Mortality Prediction by Discounting Surprising Deaths.
The Journal of emergency medicine
Oct 27, 2021
BACKGROUND: Machine learning (ML) is an emerging tool for predicting need of end-of-life discussion and palliative care, by using mortality as a proxy. But deaths, unforeseen by emergency physicians at time of the emergency department (ED) visit, mig...
Early prediction of in-hospital death of COVID-19 patients: a machine-learning model based on age, blood analyses, and chest x-ray score.
eLife
Oct 18, 2021
An early-warning model to predict in-hospital mortality on admission of COVID-19 patients at an emergency department (ED) was developed and validated using a machine-learning model. In total, 2782 patients were enrolled between March 2020 and Decembe...
The feasibility of deep learning-based synthetic contrast-enhanced CT from nonenhanced CT in emergency department patients with acute abdominal pain.
Scientific reports
Oct 14, 2021
Our objective was to investigate the feasibility of deep learning-based synthetic contrast-enhanced CT (DL-SCE-CT) from nonenhanced CT (NECT) in patients who visited the emergency department (ED) with acute abdominal pain (AAP). We trained an algorit...
Machine learning-assisted screening for cognitive impairment in the emergency department.
Journal of the American Geriatrics Society
Oct 13, 2021
BACKGROUND/OBJECTIVES: Despite a high prevalence and association with poor outcomes, screening to identify cognitive impairment (CI) in the emergency department (ED) is uncommon. Identification of high-risk subsets of older adults is a critical chall...
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