https://www.selleckchem.com/products/mrtx849.html PURPOSE We aimed to compare chest HRCT lung signs identified in scans of differently aged patients with COVID-19 infections. METHODS Case data of patients diagnosed with COVID-19 infection in Hangzhou City, Zhejiang Province in China were collected, and chest HRCT signs of infected patients in four age groups ( less then 18 years, 18-44 years, 45-59 years, ≥60 years) were compared. RESULTS Small patchy, ground-glass opacity (GGO), and consolidations were the main HRCT signs in 98 patients with confirmed COVID-19 infections. Patients aged 45-59 years and aged ≥60 years had more bilateral lung, lung lobe, and lung field involvement, and greater lesion numbers than patients less then 18 years. GGO accompanied with the interlobular septa thickening or a crazy-paving pattern, consolidation, and air bronchogram sign were more common in patients aged 45-59 years, and ≥60 years, than in those aged less then 18 years, and aged 18-44 years. CONCLUSIONS Chest HRCT manifestations in patients with COVID-19 are related to patient's age, and HRCT signs may be milder in younger patients. Emergency department (ED) overcrowding is a global condition that severely worsens attention to patients, increases clinical risks and affects hospital cost management. A correct and early prediction of ED's admission is of high value and a motivation to adopt machine learning models. However, several of these studies do not consider data collected in textual form, which is a feature set that contains detailed information about patients and presents great potential for medical health care improvement. To this end, we propose and compare predictive models for admission that use both structured and unstructured data available at triage time. In total, our dataset comprised 499,853 pediatric ED's presentations (with an admission rate of 5.76%) of patients with age up to 18 years old observed over 3.5 years. Our best model consists of a 2-stage architec