Severe/critical COVID-19 early warning system based on machine learning algorithms using novel imaging scores

BACKGROUND Early identification of severe/critical coronavirus disease 2019(COVID-19)is crucial for timely treatment and intervention.Chest computed tomography(CT)score has been shown to be a significant factor in the diagnosis and treatment of pneumonia,however,there is currently a lack of effective early warning systems for severe/critical COVID-19 based on dynamic CT evolution.AIM To develop a severe/critical COVID-19 prediction model using a combination of imaging scores,clinical features,and biomarker levels.METHODS This study used an improved scoring system to extract and describe the chest CT characteristics of COVID-19 patients.The study also took into consideration the general clinical indicators such as dyspnea,oxygen saturation,alternative lengthening of telomeres(ALT),and androgen suppression treatment(AST),which are commonly associated with severe/critical COVID-19 cases.The study employed lasso regression to evaluate and rank the significance of different disease characteristics.RESULTS The results showed that blood oxygen saturation,ALT,IL-6/IL-10,combined score,ground glass opacity score,age,crazy paving mode score,qsofa,AST,and overall lung involvement score were key factors in predicting severe/critical COVID-19 cases.The study established a COVID-19 severe/critical early warning system using various machine learning algorithms,including XGBClassifier,Logistic Regression,MLPClassifier,RandomForestClassifier,and AdaBoost Classifier.The study concluded that the prediction model based on the improved CT score and machine learning algorithms is a feasible method for early detection of severe/critical COVID-19 evolution.CONCLUSION The findings of this study suggest that a prediction model based on improved CT scores and machine learning algorithms is effective in detecting the early warning signals of severe/critical COVID-19.

Three-Dimensional Finite Element Analysis of Phase Change Memory Cell with Thin TiO2 Film

A thin TiO2 layer inserted in a phase change memory （PCM） cell to form a deep sub-micro bottom electrode （DBE） is proposed and its electro-thermal characteristics are investigated with the three-dimensional finite element analysis. Compared with the conventional PCM cell with a SiN stop layer, the reset threshold current of the PCM cell with the TiO2 layer is reduced from 1.8 mA to 1.2 mA and the ratio of the amorphous resistance and crystalline resistive increases from 65 to 100. The optimum thickness of the TiO2 layer and the optimum height of DBE are 10nm and 200nm, respectively. Therefore, the PCM cell with the TiO2 layer can decrease the programming power consumption and increase heating efficiency. The TiO2 film is a better candidate for the SiN film in the PCM cell structure to prepare DBE and to reduce programming power in the reset operation.