AIMC Topic: Emergency Service, Hospital
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Feasibility of machine learning methods for predicting hospital emergency room visits for respiratory diseases.
Environmental science and pollution research international
Feb 10, 2021
The prediction of hospital emergency room visits (ERV) for respiratory diseases after the outbreak of PM is of great importance in terms of public health, medical resource allocation, and policy decision support. Recently, the machine learning method...
A Machine Learning Prediction Model of Respiratory Failure Within 48 Hours of Patient Admission for COVID-19: Model Development and Validation.
Journal of medical Internet research
Feb 10, 2021
BACKGROUND: Predicting early respiratory failure due to COVID-19 can help triage patients to higher levels of care, allocate scarce resources, and reduce morbidity and mortality by appropriately monitoring and treating the patients at greatest risk f...
Middle Aged
Respiration, Artificial
Hospitals
ROC Curve
SARS-CoV-2
Hospitalization
Aged
Male
Patient Admission
Intubation, Intratracheal
Early Warning Score
Clinical Decision Rules
Humans
Logistic Models
Machine Learning
Triage
COVID-19
Emergency Service, Hospital
Retrospective Studies
Respiratory Insufficiency
Female
Machine learning algorithms to predict seizure due to acute tramadol poisoning.
Human & experimental toxicology
Feb 4, 2021
INTRODUCTION: This study was designed to develop and evaluate machine learning algorithms for predicting seizure due to acute tramadol poisoning, identifying high-risk patients and facilitating appropriate clinical decision-making.
Early identification of patients with acute gastrointestinal bleeding using natural language processing and decision rules.
Journal of gastroenterology and hepatology
Jan 25, 2021
BACKGROUND AND AIM: Guidelines recommend risk stratification scores in patients presenting with gastrointestinal bleeding (GIB), but such scores are uncommonly employed in practice. Automation and deployment of risk stratification scores in real time...
Identification of Gout Flares in Chief Complaint Text Using Natural Language Processing.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Jan 25, 2021
Many patients with gout flares treated in the Emergency Department (ED) often do not receive optimal continuity of care after an ED visit. Thus, developing methods to identify patients with gout flares in the ED and referring them to appropriate outp...
A comparison of machine learning models versus clinical evaluation for mortality prediction in patients with sepsis.
PloS one
Jan 19, 2021
INTRODUCTION: Patients with sepsis who present to an emergency department (ED) have highly variable underlying disease severity, and can be categorized from low to high risk. Development of a risk stratification tool for these patients is important f...
A unified machine learning approach to time series forecasting applied to demand at emergency departments.
BMC emergency medicine
Jan 18, 2021
BACKGROUND: There were 25.6 million attendances at Emergency Departments (EDs) in England in 2019 corresponding to an increase of 12 million attendances over the past ten years. The steadily rising demand at EDs creates a constant challenge to provid...
In Search of an Optimal Subset of ECG Features to Augment the Diagnosis of Acute Coronary Syndrome at the Emergency Department.
Journal of the American Heart Association
Jan 17, 2021
Background Classical ST-T waveform changes on standard 12-lead ECG have limited sensitivity in detecting acute coronary syndrome (ACS) in the emergency department. Numerous novel ECG features have been previously proposed to augment clinicians' decis...
Predicting Progression to Septic Shock in the Emergency Department Using an Externally Generalizable Machine-Learning Algorithm.
Annals of emergency medicine
Jan 15, 2021
STUDY OBJECTIVE: Machine-learning algorithms allow improved prediction of sepsis syndromes in the emergency department (ED), using data from electronic medical records. Transfer learning, a new subfield of machine learning, allows generalizability of...
Predicting adult neuroscience intensive care unit admission from emergency department triage using a retrospective, tabular-free text machine learning approach.
Scientific reports
Jan 14, 2021
Early admission to the neurosciences intensive care unit (NSICU) is associated with improved patient outcomes. Natural language processing offers new possibilities for mining free text in electronic health record data. We sought to develop a machine ...
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