The impact of COVID-19 epidemic on emergency department visits of older patients in Taiwan

Machine learning models for predicting unscheduled return visits of patients with abdominal pain at emergency department and validation during COVID-19 pandemic: A retrospective cohort study

Machine learning (ML) models for predicting 72-hour unscheduled return visits (URVs) for patients with abdominal pain in the emergency department (ED) were developed in a previous study. This study refined the data to adjust previous prediction models and evaluated the model performance in future data validation during the COVID-19 era. We aimed to evaluate the practicality of the ML models and compare the URVs before and during the COVID-19 pandemic. We used electronic health records from Chang Gung Memorial Hospital from 2018 to 2019 as a training dataset, and various machine learning models, including logistic regression (LR), random forest (RF), extreme gradient boosting (XGB), and voting classifier (VC) were developed and subsequently used to validate against the 2020 to 2021 data. The models highlighted several determinants for 72-hour URVs, including patient age, prior ER visits, specific vital signs, and medical interventions. The LR, XGB, and VC models exhibited the same AUC of 0.71 in the testing set, whereas the VC model displayed a higher F1 score (0.21). The XGB model demonstrated the highest specificity (0.99) and precision (0.64) but the lowest sensitivity (0.01). Among these models, the VC model showed the most favorable, balanced, and comprehensive performance. Despite the promising results, the study illuminated challenges in predictive modeling, such as the unforeseen influences of global events, such as the COVID-19 pandemic. These findings not only highlight the significant potential of machine learning in augmenting emergency care but also underline the importance of iterative refinement in response to changing real-world conditions.

Analyzing COVID-19 and Air Pollution Effects on Pediatric Asthma Emergency Room Visits in Taiwan

(1) Background: An asthma exacerbation that is not relieved with medication typically requires an emergency room visit (ERV). The coronavirus disease 2019 (COVID-19) pandemic began in Taiwan in January of 2020. The influence of the COVID-19 pandemic on pediatric ERVs in Taiwan was limited. Our aim was to survey pediatric asthma ERVs in the COVID-19 era; (2) Methods: Data were collected from the health quality database of the Taiwanese National Health Insurance Administration from 2019 to 2021. Air pollution and climatic factors in Taipei were used to evaluate these relationships. Changes in the rates of pediatric asthma ERVs were assessed using logistic regression analysis. Poisson regression was used to evaluate the impact of air pollution and climate change; (3) Results: The rate of pediatric asthma ERVs declined in different areas and at different hospital levels including medical centers, regional and local hospitals. Some air pollutants (particulate matter ≤ 2.5 µm, particulate matter ≤ 10 µm, nitrogen dioxide, and carbon monoxide) reduced during the COVID-19 lockdown. Ozone increased the relative risk (RR) of pediatric asthma ERVs during the COVID-19 period by 1.094 (95% CI: 1.095-1.12) per 1 ppb increase; (4) Conclusions: The rate of pediatric asthma ERVs declined during the COVID-19 pandemic and ozone has harmful effects. Based on these results, the government could reduce the number of pediatric asthma ERVs through healthcare programs, thereby promoting children's health.