A Machine Learning Approach for the NLP-Based Analysis of Cyber Threats and Vulnerabilities of the Healthcare Ecosystem

Anthropogenic impact on ecosystem service value of urban blue space in Old Malda Municipality of eastern India

Present study attempted to quantify the anthropogenic impact on blue space (BS) and potential ecosystem service value (ESV) and link them. A small urban area of eastern India was taken as a case. To fulfill the purpose, the primary data extracted from the field was used. Firstly, an anthropogenic impact score (AIS) comprising eight components was calculated using rank compositing. ESV was estimated with the help of the Rapid Assessment of Wetland Ecosystem Services (RAWES) approach of the Ramsar convention. Rank correlation was carried out for linking AIS and ESV. Results show that 70% of BS is characterized by moderate to high levels of anthropo-driven impacts. Landscape, habitat, and pollution were detected as prime vectors behind these impacts. The impacts were higher in the core urban area. Contrarily, the potential ESV was found to be higher in the peripheral BS units. Most of the blue space failed to offer remarkable provisions and cultural services. However, the regulating services were recorded to have better status. The study clearly reported a negative association between AIS and ESV in BS. Conversion of BS, habitat alteration, pollution, and lack of restoration were found to have very prominent negative effects on ESV. Minimizing such anthropogenic impacts could improve the ESV credibility of urban blue spaces.

Enhancing infectious disease prediction model selection with multi-objective optimization: an empirical study

As the pandemic continues to pose challenges to global public health, developing effective predictive models has become an urgent research topic. This study aims to explore the application of multi-objective optimization methods in selecting infectious disease prediction models and evaluate their impact on improving prediction accuracy, generalizability, and computational efficiency. In this study, the NSGA-II algorithm was used to compare models selected by multi-objective optimization with those selected by traditional single-objective optimization. The results indicate that decision tree (DT) and extreme gradient boosting regressor (XGBoost) models selected through multi-objective optimization methods outperform those selected by other methods in terms of accuracy, generalizability, and computational efficiency. Compared to the ridge regression model selected through single-objective optimization methods, the decision tree (DT) and XGBoost models demonstrate significantly lower root mean square error (RMSE) on real datasets. This finding highlights the potential advantages of multi-objective optimization in balancing multiple evaluation metrics. However, this study's limitations suggest future research directions, including algorithm improvements, expanded evaluation metrics, and the use of more diverse datasets. The conclusions of this study emphasize the theoretical and practical significance of multi-objective optimization methods in public health decision support systems, indicating their wide-ranging potential applications in selecting predictive models.

Investigating the role of Cybersecurity's perceived threats in the adoption of health information systems

Information technology is one of the most rapidly growing technologies globally. Over the last decade, its usage in healthcare has been remarkable. Over the last decade, its usage in healthcare has been remarkable. The study examines the impact of various factors as barriers to adopting the information system in healthcare. These factors are categorized into three major types: external attacks, which include phishing attacks and ransomware; employee factors, including lack of skills and the issue of information misuse; and technological factors, including complexity and vulnerability. The findings show that external attacks and technological factors are the main barriers to adopting information systems, while employee factors have no significant impact on the adoption of information systems in the healthcare industry of Pakistan. The study provides implications for healthcare policy makers, professionals and organziations regarding the successful adoption of health information system.

Special Issue: "Intelligent Systems for Clinical Care and Remote Patient Monitoring"

The year 2020 was definitely like no other [...].

Threat Assessment and Risk Analysis (TARA) for Interoperable Medical Devices in the Operating Room Inspired by the Automotive Industry

Prevailing trends in the automotive and medical device industry, such as life cycle overarching configurability, connectivity, and automation, require an adaption of development processes, especially regarding the security and safety thereof. The changing requirements imply that interfaces are more exposed to the outside world, making them more vulnerable to cyberattacks or data leaks. Consequently, not only do development processes need to be revised but also cybersecurity countermeasures and a focus on safety, as well as privacy, have become vital. While vehicles are especially exposed to cybersecurity and safety risks, the medical devices industry faces similar issues. In the automotive industry, proposals and draft regulations exist for security-related risk assessment processes. The medical device industry, which has less experience in these topics and is more heterogeneous, may benefit from drawing inspiration from these efforts. We examined and compared current standards, processes, and methods in both the automotive and medical industries. Based on the requirements regarding safety and security for risk analysis in the medical device industry, we propose the adoption of methods already established in the automotive industry. Furthermore, we present an example based on an interoperable Operating Room table (OR table).