High-Quality Nursing and Intraoperative Cooperation Effectively Reduce the Occurrence of Adverse Reactions for Patients with ST-elevation Myocardial Infarction.

Objective: This study aimed to explore the application effectiveness of intraoperative coordination and nursing in the interventional treatment of patients with ST-segment elevation myocardial infarction (STEMI). Specifically, the study sought to investigate the specific protocols or practices within the systematic nursing collaboration approach and their impact on patient outcomes. Methods: A total of 60 STEMI patients treated in our hospital from April 2019 to June 2020 were randomly assigned to either the routine care group (n=30) or the systematic nursing collaboration group (n=30). Both groups underwent percutaneous coronary intervention (PCI). Outcome measures, including time to unblock infarcted vessels, incidence of adverse reactions during interventional therapy, mortality, treatment success rate, improvement in cardiac function, and length of hospital stay, were assessed using appropriate statistical analysis methods. Results: A t test showed that the systematic nursing collaboration group exhibited a significantly shorter time to unblock infarcted vessels compared to the routine care group (P < .05). The incidence of adverse reactions during interventional therapy was significantly lower in the systematic group compared to the routine group (P < .05), analyzed using a chi-square test. Furthermore, the systematic group demonstrated a higher treatment success rate (P < .05), analyzed using a chi-square test. Moreover, the improvement in cardiac function in the systematic group was significantly better compared to the routine group (P < .05), analyzed using a t test. Additionally, the systematic group had a significantly shorter length of hospital stay compared to the routine group (P < .05), analyzed using a t test. Conclusion: The findings of this study highlight the effectiveness of intraoperative coordination and nursing practices in reducing adverse reactions and mortality, improving treatment success rates, enhancing cardiac function, and shortening hospital stays in patients with STEMI. These results emphasize the importance of implementing systematic nursing collaboration in the interventional treatment of STEMI patients. Further research can explore specific protocols and strategies for integrating systematic nursing collaboration into medical practices, leading to improved healthcare delivery and patient outcomes.

Umbra: A Visual Analysis Approach for Defense Construction Against Inference Attacks on Sensitive Information.

Collecting and analyzing anonymous personal information is required as a part of data analysis processes, such as medical diagnosis and restaurant recommendation. Such data should ostensibly be stored so that specific individual information cannot be disclosed. Unfortunately, inference attacks-integrating background knowledge and intelligent models-hinder classic sanitization techniques like syntactic anonymity and differential privacy from exhaustively protecting sensitive information. As a solution, we introduce a three-stage approach empowered within a visual interface, which depicts underlying inference behaviors via a Bayesian Network and supports a customized defense against inference attacks from unknown adversaries. In particular, our approach visually explains the process details of the underlying privacy preserving models, allowing users to verify if the results sufficiently satisfy the requirements of privacy preservation. We demonstrate the effectiveness of our approach through two case studies and expert reviews.

Towards Efficient Visual Simplification of Computational Graphs in Deep Neural Networks.

A computational graph in a deep neural network (DNN) denotes a specific data flow diagram (DFD) composed of many tensors and operators. Existing toolkits for visualizing computational graphs are not applicable when the structure is highly complicated and large-scale (e.g., BERT [1]). To address this problem, we propose leveraging a suite of visual simplification techniques, including a cycle-removing method, a module-based edge-pruning algorithm, and an isomorphic subgraph stacking strategy. We design and implement an interactive visualization system that is suitable for computational graphs with up to 10 thousand elements. Experimental results and usage scenarios demonstrate that our tool reduces 60% elements on average and hence enhances the performance for recognizing and diagnosing DNN models. Our contributions are integrated into an open-source DNN visualization toolkit, namely, MindInsight [2].

GraphProtector: A Visual Interface for Employing and Assessing Multiple Privacy Preserving Graph Algorithms.

Analyzing social networks reveals the relationships between individuals and groups in the data. However, such analysis can also lead to privacy exposure (whether intentionally or inadvertently): leaking the real-world identity of ostensibly anonymous individuals. Most sanitization strategies modify the graph's structure based on hypothesized tactics that an adversary would employ. While combining multiple anonymization schemes provides a more comprehensive privacy protection, deciding the appropriate set of techniques-along with evaluating how applying the strategies will affect the utility of the anonymized results-remains a significant challenge. To address this problem, we introduce GraphProtector, a visual interface that guides a user through a privacy preservation pipeline. GraphProtector enables multiple privacy protection schemes which can be simultaneously combined together as a hybrid approach. To demonstrate the effectiveness of GraphProtector, we report several case studies and feedback collected from interviews with expert users in various scenarios.