Privacy-Preserving Synthetic Data Generation Method for IoT-Sensor Network IDS Using CTGAN.

The increased usage of IoT networks brings about new privacy risks, especially when intrusion detection systems (IDSs) rely on large datasets for machine learning (ML) tasks and depend on third parties for storing and training the ML-based IDS. This study proposes a privacy-preserving synthetic data generation method using a conditional tabular generative adversarial network (CTGAN) aimed at maintaining the utility of IoT sensor network data for IDS while safeguarding privacy. We integrate differential privacy (DP) with CTGAN by employing controlled noise injection to mitigate privacy risks.

Comparing characteristics and outcomes of in-hospital cardiac arrest during the early and late COVID-19 pandemic at an academic institution in Saudi Arabia.

Background: The COVID-19 pandemic has introduced major changes in the resuscitation practices of cardiac arrest victims.

Aim: We aimed to compare the characteristics and outcomes of patients who sustained in-hospital cardiac arrest (IHCA) during the early COVID-19 pandemic period (2020) with those during the late COVID-19 pandemic period (2021).

Methods: This was a retrospective review of adult patients sustaining IHCA at a single academic centre.

Frequency and Risk Factors of Unplanned 30-Day Readmission After Open Heart Surgeries: A Retrospective Study in a Tertiary Care Center.

Background: Unplanned 30-day readmission post-cardiac surgery imposes higher risks for complications, increased costs, and unfavorable events for the care provider and patient. This study was to determine the unplanned readmission rate, determinants, and most common events within 30 days post-cardiac surgery. Recommendations to prevent or minimize these complications are included.