Objective To develop and externally validate models based on neural networks and natural
language processing (NLP) to identify suspected serious infections in emergency …

Background Low-acuity outpatients constitute the majority of emergency department (ED)
patients, and these patients often experience an unpredictable length of stay (LOS). Effective …

Objective To demonstrate the incremental benefit of using free text data in addition to vital
sign and demographic data to identify patients with suspected infection in the emergency …

Background: Despite its high lethality, sepsis can be difficult to detect on initial presentation
to the emergency department (ED). Machine learning–based tools may provide avenues for …

Background Sepsis is a life-threatening condition, causing almost one fifth of all deaths
worldwide. The aim of the current study was to identify variables predictive of 7-and 30-day …

The risk stratification of patients in the emergency department begins at triage. It is vital to
stratify patients early based on their severity, since undertriage can lead to increased …

Background. Artificial neural networks (ANN) have been used in the prediction of several
medical conditions but have not been previously used to predict pneumonia. The authors …

Background Automated surveillance for cardiac arrests would be useful in overcrowded
emergency departments. The purpose of this study is to develop and test artificial neural …

Objectives To evaluate factors affecting performance of influenza detection, including
accuracy of natural language processing (NLP), discriminative ability of Bayesian network …

Chronic Obstructive Pulmonary Disease (COPD) exacerbation exhibits a set of overlapping
symptoms with various forms of cardiovascular disease, which makes its early identification …