Investigation and analysis of patient outcomes, including in-hospital mortality and length of stay, are crucial for assisting clinicians in determining a patient's result at the outset of their hospitalization and for assisting hospitals in allocating their resources. This paper proposes an approach based on combining the well-known gray wolf algorithm with frequent items extracted by association rule mining algorithms. First, original features are combined with the discriminative extracted frequent items. The best subset of these features is then chosen, and the parameters of the used classification algorithms are also adjusted, using the gray wolf algorithm. This framework was evaluated using a real dataset made up of 2816 patients from the Imam Ali Kermanshah Hospital in Iran. The study's findings indicate that low Ejection Fraction, old age, high CPK values, and high Creatinine levels are the main contributors to patients' mortality. Several significant and interesting rules related to mortality in hospitals and length of stay have also been extracted and presented. Additionally, the accuracy, sensitivity, specificity, and auroc of the proposed framework for the diagnosis of mortality in the hospital using the SVM classifier were 0.9961, 0.9477, 0.9992, and 0.9734, respectively. According to the framework's findings, adding frequent items as features considerably improves classification accuracy.

本文介绍了一种新的框架，以预测全向图像的视觉注意。我们的体系结构的关键设置是同时预测给定刺激的显着图和相应的扫描路径。该框架实现了一个完全编码器 - 解码器卷积神经网络，由注意模块增强以生成代表性显着图。另外，采用辅助网络通过SoftArgMax函数来生成可能的视口中心固定点。后者允许从特征映射派生固定点。为了利用扫描路径预测，然后应用自适应联合概率分布模型来通过利用基于编码器解码器的显着性图和基于扫描路径的显着热图来构建最终的不偏不倚的显着性图。在显着性和扫描路径预测方面评估所提出的框架，并将结果与​​Salient360上的最先进方法进行比较！数据集。结果表明，我们的框架和这种架构的益处的相关性，用于进一步全向视觉注意预测任务。