We aimed to build up multiple machine learning models to predict 30-days mortality, and 3 complications including septic shock, thrombocytopenia, and liver dysfunction after open-heart surgery. Patients who underwent coronary artery bypass surgery, aortic valve replacement, or other heart-related surgeries between 2001 and 2012 were extracted from MIMIC-III databases. Extreme gradient boosting, random forest, artificial neural network, and logistic regression were employed to build models by utilizing fivefold cross-validation and grid search. Receiver operating characteristic curve, area under curve (AUC), decision curve analysis, test accuracy, F1 score, precision, and recall were applied to access the performance. Among 6844 patients enrolled in this study, 215 patients (3.1%) died within 30 days after surgery, part of patients appeared liver dysfunction (248; 3.6%), septic shock (32; 0.5%), and thrombocytopenia (202; 2.9%). XGBoost, selected to be our final model, achieved the best performance with highest AUC and F1 score. AUC and F1 score of XGBoost for 4 outcomes: 0.88 and 0.58 for 30-days mortality, 0.98 and 0.70 for septic shock, 0.88 and 0.55 for thrombocytopenia, 0.89 and 0.40 for liver dysfunction. We developed a promising model, presented as software, to realize monitoring for patients in ICU and to improve prognosis.

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Colorization for medical images helps make medical visualizations more engaging, provides better visualization in 3D reconstruction, acts as an image enhancement technique for tasks such as segmentation, and makes it easier for non-specialists to perceive tissue changes and texture details in medical images in diagnosis and teaching. However, colorization algorithms have been hindered by limited semantic understanding. In addition, current colorization methods still rely on paired data, which is often not available for specific fields such as medical imaging. To address the texture detail of medical images and the scarcity of paired data, we propose a self-supervised colorization framework based on CycleGAN(Cycle-Consistent Generative Adversarial Networks), treating the colorization problem of medical images as a cross-modal domain transfer problem in color space. The proposed framework focuses on global edge features and semantic information by introducing edge-aware detectors, multi-modal discriminators, and a semantic feature fusion module. Experimental results demonstrate that our method can generate high-quality color medical images.

Most Android mobile anti-virus software in the industry is checked at the application level, and users familiar with the Android operating system are well aware that the use of virtual clicks, function execution, or shell commands can force the application to stop, which poses a threat to the real-time monitoring of anti-virus software. Moreover, the current mainstream anti-virus software in the industry can only let users manually uninstall or deactivate malicious apps when detected, which also makes the anti-virus software in Android mobile lose the ability of mobile anti-virus software to remove or delete viruses and Trojans automatically. To solve the problems above, in this paper, we train a mobile anti-virus model based on Resnet50 and proposes an Android mobile anti-virus method using remote thread injection - overriding the execution of malicious code by RTI means such as hook API, nulling related functions, rewriting related classes or functions to preserve the app as much as possible. In contrast, The model can identify malicious code with the highest accuracy. The model's recognition accuracy is up to 98.14%, and the malicious code blocking rate is up to 99.70% after recognition.

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