Predicting ambulance diversion in an adult Emergency Department using a Gaussian process.

When the Emergency Department (ED) reaches a critical level of overcrowding, it diverts ambulances to other hospitals. We evaluated the accuracy of a Gaussian process for prediction of ambulance diversion using March 1, 2005 November 30, 2005 data. The area under the receiver operating curve (AUC) for 120 minutes in advance was 0.

Predicting hospital admission at triage in an emergency department.

Predicting hospital admission for Emergency Department (ED) patients at the time of triage may improve throughput. To predict admission we created and validated a Bayesian Network from 47,993 encounters (training: n=23,996, validation: n=9,599, test: n=14,398). The area under the receiver operator characteristic curve was 0.

Comparing decision support methodologies for identifying asthma exacerbations.

Objective: To apply and compare common machine learning techniques with an expert-built Bayesian Network to determine eligibility for asthma guidelines in pediatric emergency department patients.

Population: All patients 2-18 years of age presenting to a pediatric emergency department during a 2-month study period.

Methods: We created an artificial neural network, a support vector machine, a Gaussian process, and a learned Bayesian network to compare each method's ability to detect patients eligible for asthma guidelines.

Predicting hospital admission in a pediatric Emergency Department using an Artificial Neural Network.

Hospital admission delays in the Emergency Department (ED) reduce capacity and contribute to the ED's diversion problem. We evaluated the accuracy of an Artificial Neural Network for the early prediction of hospital admission using data from 43,077 pediatric ED encounters. We used 9 variables commonly available in the ED setting.

Impact of different training strategies on the accuracy of a Bayesian network for predicting hospital admission.

Introduction: The healthcare environment is constantly changing. Probabilistic clinical decision support systems need to recognize and incorporate the changing patterns and adjust the decision model to maintain high levels of accuracy.

Methods: Using data from >75,000 ED patients during a 19-month study period we examined the impact of various static and dynamic training strategies on a decision support system designed to predict hospital admission status for ED patients.

Predicting hospital admission for Emergency Department patients using a Bayesian network.

Hospital admission delays in the Emergency Department (ED) reduce volume capacity and contribute to the nation's ED diversion problem. This study evaluated the accuracy of a Bayesian network for the early prediction of hospital admission status using data from 16,900 ED encounters. The final model included nine variables that are commonly available in many ED settings.