AIMC Topic: Emergency Service, Hospital

Clear Filters Showing 221 to 230 of 479 articles

A Novel Deep Learning-Based System for Triage in the Emergency Department Using Electronic Medical Records: Retrospective Cohort Study.

Journal of medical Internet research Dec 27, 2021
BACKGROUND: Emergency department (ED) crowding has resulted in delayed patient treatment and has become a universal health care problem. Although a triage system, such as the 5-level emergency severity index, somewhat improves the process of ED treat...
Humans Emergency Service, Hospital Hospitalization Deep Learning Prospective Studies Triage Retrospective Studies Electronic Health Records
View on PubMed DOI

Development and validation of a practical machine-learning triage algorithm for the detection of patients in need of critical care in the emergency department.

Scientific reports Dec 15, 2021
Identifying critically ill patients is a key challenge in emergency department (ED) triage. Mis-triage errors are still widespread in triage systems around the world. Here, we present a machine learning system (MLS) to assist ED triage officers bette...
Male Middle Aged ROC Curve Female Aged Adult Critical Care Disease Management Odds Ratio Disease Susceptibility Triage Area Under Curve Emergency Service, Hospital Humans Machine Learning Algorithms Critical Illness Clinical Decision-Making
View on PubMed DOI

Using explainable machine learning to characterise data drift and detect emergent health risks for emergency department admissions during COVID-19.

Scientific reports Nov 26, 2021
A key task of emergency departments is to promptly identify patients who require hospital admission. Early identification ensures patient safety and aids organisational planning. Supervised machine learning algorithms can use data describing historic...
Hospitalization Male Middle Aged Algorithms Female SARS-CoV-2 Machine Learning Humans Emergency Service, Hospital Pandemics Patient Admission COVID-19
View on PubMed DOI

Development and validation of a sensitive and high throughput UPLC-MS/MS method for determination of paraquat and diquat in human plasma and urine: application to poisoning cases at emergency departments of hospitals.

Forensic toxicology Nov 12, 2021
PURPOSE: Paraquat and diquat are well-known toxic herbicides, at least responsible for hundreds of fatal poisoning events worldwide. However, the determination of diquat and paraquat in plasma and urine is very challenging because of their high polar...
Diquat Emergency Service, Hospital Paraquat Chromatography, High Pressure Liquid Tandem Mass Spectrometry Humans Hospitals Chromatography, Liquid
View on PubMed DOI

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...
Emergency Service, Hospital Humans Radiography, Thoracic Physicians Self-Help Devices Deep Learning
View on PubMed DOI

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...
Humans Retrospective Studies Deep Learning Adolescent Child Ultrasonography Emergency Service, Hospital Focused Assessment with Sonography for Trauma
View on PubMed DOI

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...
Risk Factors Risk Assessment Canada Emergency Service, Hospital Syncope Artificial Intelligence Machine Learning Prospective Studies Humans
View on PubMed DOI

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...
Aftercare Patient Discharge Patient Readmission Adolescent Triage Emergency Service, Hospital Hospitalization Aged Young Adult Adult Male Humans Middle Aged Machine Learning Electronic Health Records Algorithms Female Retrospective Studies Risk Factors Critical Illness
View on PubMed DOI

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).
Logistic Models Emergency Service, Hospital Humans Machine Learning Hospitalization
View on PubMed DOI

Advances in Appropriate Postoperative Triage and the Role of Real-time Machine-Learning Models: The Goldilocks Dilemma.

JAMA network open Nov 1, 2021
Machine Learning Humans Triage Emergency Service, Hospital
View on PubMed DOI
Popular Topics
Recent Journals