Impact of COVID-19 Pandemic on Emergency Department Volume and Acuity in Low Incidence Area: Taiwan's Experience in Three Hospitals.

Background: The decrease in emergency department (ED) patient visits during the COVID-19 pandemic was reported by various studies. Our study aimed to investigate whether a similar trend can be observed in a country with a low incidence of COVID-19 as well as the impact caused by the pandemic on ED patients in different triage levels and categories. Methods: This multicenter retrospective study collected data from three regional hospitals between March 2019 and December 2020. We evaluated the differences between patient volume, disease severity, and patient composition in ED before and after the COVID-19 pandemic among these hospitals. Results: There was a 23% reduction in ED patient volume in the urban hospital (hospital A) as well as a 16% reduction in suburban hospitals (hospitals B and C) during the pandemic period, respectively. The regression analysis showed a high correlation in the change in monthly patient volume among these hospitals. In terms of severity, there was a 24% reduction in ED visits with high severity levels (Taiwan Triage and Acuity Scale [TTAS] I, II) in hospital A, as well as 16% and 12% in hospitals B and C during the pandemic period, respectively. Similarly, there was a 23% reduction in ED visits with low severity levels (TTAS III, IV, V) in hospital A, as well as 20% and 16% in hospitals B and C during the pandemic period, respectively. In terms of patient types, there was a significant decline in non-traumatic adult patients (19%, 17%, and 10%), and pediatric patients (49%, 50%, and 46%) in hospitals A, B, and C, respectively. Conclusions: Despite the low incidence of COVID-19 in Taiwan, a decrease in total ED visits was still found during the pandemic, especially in non-trauma adult visits and pediatric visits. In addition, ED visits in both high and low severity levels decreased in these regional hospitals.

The accuracy of machine learning approaches using non-image data for the prediction of COVID-19: A meta-analysis.

OBJECTIVE: COVID-19 is a novel, severely contagious disease with enormous negative impact on humanity as well as the world economy. An expeditious, feasible tool for detecting COVID-19 remains yet elusive. Recently, there has been a surge of interest in applying machine learning techniques to predict COVID-19 using non-image data. We have therefore undertaken a meta-analysis to quantify the diagnostic performance of machine learning models facilitating the prediction of COVID-19. MATERIALS AND METHODS: A comprehensive electronic database search for the period between January 1st, 2021 and December 3rd, 2021 was undertaken in order to identify eligible studies relevant to this meta-analysis. Summary sensitivity, specificity, and the area under receiver operating characteristic curves were used to assess potential diagnostic accuracy. Risk of bias was assessed by means of a revised Quality Assessment of Diagnostic Studies. RESULTS: A total of 30 studies, including 34 models, met all of the inclusion criteria. Summary sensitivity, specificity, and area under receiver operating characteristic curves were 0.86, 0.86, and 0.91, respectively. The purpose of machine learning models, class imbalance, and feature selection are significant covariates useful in explaining the between-study heterogeneity, in terms of both sensitivity and specificity. CONCLUSIONS: Our study findings show that non-image data can be used to predict COVID-19 with an acceptable performance. Further, class imbalance and feature selection are suggested to be incorporated whenever building models for the prediction of COVID-19, thus improving further diagnostic performance.

The Accuracy of Machine Learning Approaches Using Non-image Data for the Prediction of COVID-19: A Meta-Analysis

Objective COVID-19 is a novel, severely contagious disease with enormous negative impact on humanity as well as the world economy. An expeditious, feasible tool for detecting COVID-19 remains yet elusive. Recently, there has been a surge of interest in applying machine learning techniques to predict COVID-19 using non-image data. We have therefore undertaken a meta-analysis to quantify the diagnostic performance of machine learning models facilitating the prediction of COVID-19. Materials and Methods A comprehensive electronic database search for the period between January 1st, 2021 and December 3rd, 2021 was undertaken in order to identify eligible studies relevant to this meta-analysis. Summary sensitivity, specificity, and the area under receiver operating characteristic curves were used to assess potential diagnostic accuracy. Risk of bias was assessed by means of a revised Quality Assessment of Diagnostic Studies. Results A total of 30 studies, including 34 models, met all of the inclusion criteria. Summary sensitivity, specificity, and area under receiver operating characteristic curves were 0.86, 0.86, and 0.91, respectively. The purpose of machine learning models, class imbalance, and feature selection are significant covariates useful in explaining the between-study heterogeneity, in terms of both sensitivity and specificity. Conclusions Our study findings show that non-image data can be used to predict COVID-19 with an acceptable performance. Further, class imbalance and feature selection are suggested to be incorporated whenever building models for the prediction of COVID-19, thus improving further diagnostic performance.