Characteristics of Suspected COVID-19 Discharged Emergency Department Patients Who Returned During the First Wave.

Introduction: Limited information exists on patients with suspected coronavirus disease 2019 (COVID-19) who return to the emergency department (ED) during the first wave. In this study we aimed to identify predictors of ED return within 72 hours for patients with suspected COVID-19.

Methods: Incorporating data from 14 EDs within an integrated healthcare network in the New York metropolitan region from March 2-April 27, 2020, we analyzed this data on predictors for a return ED visit-including demographics, comorbidities, vital signs, and laboratory results.

Non-linear global optimization via parameterization and inverse function approximation: an artificial neural networks approach.

In this article, a novel technique for non-linear global optimization is presented. The main goal is to find the optimal global solution of non-linear problems avoiding sub-optimal local solutions or inflection points. The proposed technique is based on a two steps concept: properly keep decreasing the value of the objective function, and calculating the corresponding independent variables by approximating its inverse function.

A radial basis function network approach for the computation of inverse continuous time variant functions.

This Paper presents an efficient approach for the fast computation of inverse continuous time variant functions with the proper use of Radial Basis Function Networks (RBFNs). The approach is based on implementing RBFNs for computing inverse continuous time variant functions via an overall damped least squares solution that includes a novel null space vector for singularities prevention. The singularities avoidance null space vector is derived from developing a sufficiency condition for singularities prevention that conduces to establish some characterizing matrices and an associated performance index.