OBJECTIVES: The primary objective of this study was to identify clinical and socioeconomic predictors of hospital and ED use among children with medical complexity within 1 and 5 years of an initial discharge between 2010 and 2013. A secondary object...
OBJECTIVE: To improve the performance of International Classification of Disease (ICD) code rule-based algorithms for identifying low acuity Emergency Department (ED) visits by using machine learning methods and additional covariates.
International journal of medical informatics
39106773
OBJECTIVES: This study aims to evaluate the fairness performance metrics of Machine Learning (ML) models to predict hospitalization and emergency department (ED) visits in heart failure patients receiving home healthcare. We analyze biases, assess pe...
As opioid-related overdose emergency department visits continue to rise in the United States, there is a need to understand the location and magnitude of the crisis, especially in at-risk rural areas. We analyzed sets of ZIP code level electronic hea...
A medical specialty prediction system for remote diagnosis can reduce the unexpected costs incurred by first-visit patients who visit the wrong hospital department for their symptoms. To develop medical specialty prediction systems, several researche...
BACKGROUND: Dependent older people or those losing their autonomy are at risk of emergency hospitalization. Digital systems that monitor health remotely could be useful in reducing these visits by detecting worsening health conditions earlier. Howeve...