An AI-based auxiliary empirical antibiotic therapy model for children with bacterial pneumonia using low-dose chest CT images.

PURPOSE: To construct an auxiliary empirical antibiotic therapy (EAT) multi-class classification model for children with bacterial pneumonia using radiomics features based on artificial intelligence and low-dose chest CT images. MATERIALS AND METHODS: Data were retrospectively collected from children with pathogen-confirmed bacterial pneumonia including Gram-positive bacterial pneumonia (122/389, 31%), Gram-negative bacterial pneumonia (159/389, 41%) and atypical bacterial pneumonia (108/389, 28%) from January 1 to June 30, 2019. Nine machine-learning models were separately evaluated based on radiomics features extracted from CT images; three optimal submodels were constructed and integrated to form a multi-class classification model. RESULTS: We selected five features to develop three radiomics submodels: a Gram-positive model, a Gram-negative model and an atypical model. The comprehensive radiomics model using support vector machine method yielded an average area under the curve (AUC) of 0.75 [95% confidence interval (CI), 0.65-0.83] and accuracy (ACC) of 0.58 [sensitivity (SEN), 0.57; specificity (SPE), 0.78] in the training set, and an average AUC of 0.73 (95% CI 0.61-0.79) and ACC of 0.54 (SEN, 0.52; SPE, 0.75) in the test set. CONCLUSION: This auxiliary EAT radiomics multi-class classification model was deserved to be researched in differential diagnosing bacterial pneumonias in children.

Investigation into Information Release of Chinese Government and Departments on COVID-19.

Information release is an important way for governments to deal with public health emergencies, and plays an irreplaceable role in promoting epidemic prevention and control, enhancing public awareness of the epidemic situation and mobilizing social resources. Focusing on the coronavirus disease 2019 (COVID-19) epidemic in China, this investigation chose 133 information release accounts of the Chinese government and relevant departments at the national, provincial, and municipal levels, including Ministries of the State Council, Departments of Hubei Province Government, and Bureaus of Wuhan Government, covering their portals, apps, Weibos, and WeChats. Then, the characteristics such as scale, agility, frequency, originality, and impact of different levels, departments, and channels of the information releases by the Chinese government on the COVID-19 epidemic were analyzed. Finally, the overall situation was concluded by radar map analysis. It was found that the information release on the COVID-19 epidemic was coordinated effectively at different levels, departments, and channels, as evidenced by the complementarity between channels, the synergy between the national and local governments, and the coordination between departments, which guaranteed the rapid success of the epidemic prevention and control process in China. This investigation could be a reference for epidemic prevention and control for governments and international organizations, such as the World Health Organization (WHO), during public health emergencies, e.g. the COVID-19 pandemic.