Knowledge graph analysis and visualization of AI technology applied in COVID-19.

With the global outbreak of coronavirus disease (COVID-19) all over the world, artificial intelligence (AI) technology is widely used in COVID-19 and has become a hot topic. In recent 2 years, the application of AI technology in COVID-19 has developed rapidly, and more than 100 relevant papers are published every month. In this paper, we combined with the bibliometric and visual knowledge map analysis, used the WOS database as the sample data source, and applied VOSviewer and CiteSpace analysis tools to carry out multi-dimensional statistical analysis and visual analysis about 1903 pieces of literature of recent 2 years (by the end of July this year). The data is analyzed by several terms with the main annual article and citation count, major publication sources, institutions and countries, their contribution and collaboration, etc. Since last year, the research on the COVID-19 has sharply increased; especially the corresponding research fields combined with the AI technology are expanding, such as medicine, management, economics, and informatics. The China and USA are the most prolific countries in AI applied in COVID-19, which have made a significant contribution to AI applied in COVID-19, as the high-level international collaboration of countries and institutions is increasing and more impactful. Moreover, we widely studied the issues: detection, surveillance, risk prediction, therapeutic research, virus modeling, and analysis of COVID-19. Finally, we put forward perspective challenges and limits to the application of AI in the COVID-19 for researchers and practitioners to facilitate future research on AI applied in COVID-19.

A Ventilator-associated Pneumonia Prediction Model in Patients With Acute Respiratory Distress Syndrome.

BACKGROUND: Mechanical ventilation is crucial for acute respiratory distress syndrome (ARDS) patients and diagnosis of ventilator-associated pneumonia (VAP) in ARDS patients is challenging. Hence, an effective model to predict VAP in ARDS is urgently needed. METHODS: We performed a secondary analysis of patient-level data from the Early versus Delayed Enteral Nutrition (EDEN) of ARDSNet randomized controlled trials. Multivariate binary logistic regression analysis established a predictive model, incorporating characteristics selected by systematic review and univariate analyses. The model's discrimination, calibration, and clinical usefulness were assessed using the C-index, calibration plot, and decision curve analysis (DCA). RESULTS: Of the 1000 unique patients enrolled in the EDEN trials, 70 (7%) had ARDS complicated with VAP. Mechanical ventilation duration and intensive care unit (ICU) stay were significantly longer in the VAP group than non-VAP group (P < .001 for both) but the 60-day mortality was comparable. Use of neuromuscular blocking agents, severe ARDS, admission for unscheduled surgery, and trauma as primary ARDS causes were independent risk factors for VAP. The area under the curve of the model was .744, and model fit was acceptable (Hosmer-Lemeshow P = .185). The calibration curve indicated that the model had proper discrimination and good calibration. DCA showed that the VAP prediction nomogram was clinically useful when an intervention was decided at a VAP probability threshold between 1% and 61%. CONCLUSIONS: The prediction nomogram for VAP development in ARDS patients can be applied after ICU admission, using available variables. Potential clinical benefits of using this model deserve further assessment.