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Emergency Service, Hospital

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Artificial intelligence to predict needs for urgent revascularization from 12-leads electrocardiography in emergency patients.

PloS one Jan 9, 2019
BACKGROUND: Patient with acute coronary syndrome benefits from early revascularization. However, methods for the selection of patients who require urgent revascularization from a variety of patients visiting the emergency room with chest symptoms is ...
Neural Networks, Computer Prognosis Sensitivity and Specificity Diagnosis, Computer-Assisted Time Factors Humans Chest Pain Acute Coronary Syndrome Datasets as Topic Myocardial Revascularization Decision Making, Computer-Assisted Electrocardiography Diagnosis, Differential Predictive Value of Tests Emergency Service, Hospital Patient Selection
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Machine Learning-Based Prediction of Clinical Outcomes for Children During Emergency Department Triage.

JAMA network open Jan 4, 2019
IMPORTANCE: While machine learning approaches may enhance prediction ability, little is known about their utility in emergency department (ED) triage.
Adolescent Critical Care Outcomes Child, Preschool Machine Learning Humans Child Predictive Value of Tests United States Prognosis Triage Models, Educational Female Emergency Service, Hospital Child, Hospitalized Hospitalization Critical Care Male Quality Improvement
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Study protocol for a randomised controlled trial of humanoid robot-based distraction for venipuncture pain in children.

BMJ open Dec 14, 2018
INTRODUCTION: Intravenous insertion (IVI) is a very common procedure in the emergency department (ED). IVI is often painful and stressful for both children and their families. Currently, distraction therapy is not used as a standard of care for IVI i...
Humans Female Pain Measurement Hospitals, Pediatric Attention Pain Management Adaptation, Psychological Male Child Infusions, Intravenous Robotics Emergency Service, Hospital Phlebotomy Alberta Equivalence Trials as Topic
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Clinical Document Classification Using Labeled and Unlabeled Data Across Hospitals.

AMIA ... Annual Symposium proceedings. AMIA Symposium Dec 5, 2018
Reviewing radiology reports in emergency departments is an essential but laborious task. Timely follow-up of patients with abnormal cases in their radiology reports may dramatically affect the patient's outcome, especially if they have been discharge...
Supervised Machine Learning Neural Networks, Computer Algorithms Information Storage and Retrieval Natural Language Processing Classification Emergency Service, Hospital Radiology Information Systems
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A comparison of logistic regression models with alternative machine learning methods to predict the risk of in-hospital mortality in emergency medical admissions via external validation.

Health informatics journal Nov 29, 2018
We compare the performance of logistic regression with several alternative machine learning methods to estimate the risk of death for patients following an emergency admission to hospital based on the patients' first blood test results and physiologi...
Logistic Models Emergency Service, Hospital Patient Admission Humans Machine Learning Hospitalization Hospital Mortality ROC Curve
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A Machine Learning Approach to Predicting Need for Hospitalization for Pediatric Asthma Exacerbation at the Time of Emergency Department Triage.

Academic emergency medicine : official journal of the Society for Academic Emergency Medicine Nov 29, 2018
OBJECTIVES: Pediatric asthma is a leading cause of emergency department (ED) utilization and hospitalization. Earlier identification of need for hospital-level care could triage patients more efficiently to high- or low-resource ED tracks. Existing t...
Triage Emergency Service, Hospital Status Asthmaticus Child, Preschool Child ROC Curve Logistic Models Humans Machine Learning Retrospective Studies Female Hospitalization Male Risk Assessment Adolescent
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The Sydney Triage to Admission Risk Tool (START2) using machine learning techniques to support disposition decision-making.

Emergency medicine Australasia : EMA Nov 23, 2018
OBJECTIVE: To further develop and refine an Emergency Department (ED) in-patient admission prediction model using machine learning techniques.
Aged, 80 and over Adult Male Middle Aged Adolescent ROC Curve Logistic Models Triage Machine Learning Area Under Curve Humans Forecasting Emergency Service, Hospital Retrospective Studies Bayes Theorem Female Patient Admission Aged New South Wales
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Predicting the risk of emergency admission with machine learning: Development and validation using linked electronic health records.

PLoS medicine Nov 20, 2018
BACKGROUND: Emergency admissions are a major source of healthcare spending. We aimed to derive, validate, and compare conventional and machine learning models for prediction of the first emergency admission. Machine learning methods are capable of ca...
Needs Assessment Emergency Service, Hospital Data Mining Risk Factors Sex Factors Time Factors Aged Health Services Needs and Demand Socioeconomic Factors Humans Aged, 80 and over Patient Admission Health Status Male Machine Learning Young Adult Middle Aged Age Factors Electronic Health Records Adult Risk Assessment Female England Reproducibility of Results Adolescent
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A deep neural network learning algorithm outperforms a conventional algorithm for emergency department electrocardiogram interpretation.

Journal of electrocardiology Nov 16, 2018
BACKGROUND: Cardiologs® has developed the first electrocardiogram (ECG) algorithm that uses a deep neural network (DNN) for full 12‑lead ECG analysis, including rhythm, QRS and ST-T-U waves. We compared the accuracy of the first version of Cardiologs...
Predictive Value of Tests Emergency Service, Hospital Arrhythmias, Cardiac Neural Networks, Computer Algorithms Humans Retrospective Studies Sensitivity and Specificity Electrocardiography
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Defining heatwave thresholds using an inductive machine learning approach.

PloS one Nov 7, 2018
Establishing appropriate heatwave thresholds is important in reducing adverse human health consequences as it enables a more effective heatwave warning system and response plan. This paper defined such thresholds by focusing on the non-linear relatio...
Seasons Hospitalization Male Humans Heat Stress Disorders Middle Aged Aged Meteorology Adolescent Young Adult Extreme Heat Emergency Service, Hospital Adult Supervised Machine Learning
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