Early prediction of weaning outcomes in mechanically ventilated patients has significant potential to influence the duration of treatment as well as associated morbidity and mortality. This study aimed to investigate the utility of signal analysis using electromyographic diaphragm (EMG) and electrocardiography (ECG) signals to classify the success or failure of weaning in mechanically ventilated patients. Electromyographic signals of 40 subjects were recorded using 5-channel surface electrodes placed around the diaphragm muscle, along with an ECG recording through a 3-lead Holter system during extubation. EMG and ECG signals were recorded from mechanically ventilated patients undergoing weaning trials. Linear and nonlinear signal analysis techniques were used to assess the interaction between diaphragm muscle activity and cardiac activity. Supervised machine learning algorithms were then used to classify the weaning outcomes. The study revealed clear differences in diaphragmatic and cardiac patterns between patients who succeeded and failed in the weaning trials. Successful weaning was characterised by a higher ECG-derived respiration amplitude, whereas failed weaning was characterised by an elevated EMG amplitude. Furthermore, successful weaning exhibited greater oscillations in diaphragmatic muscle activity. Spectral analysis and parameter extraction identified 320 parameters, of which 43 were significant predictors of weaning outcomes. Using seven of these parameters, the Naive Bayes classifier demonstrated high accuracy in classifying weaning outcomes. Surface electromyographic and electrocardiographic signal analyses can predict weaning outcomes in mechanically ventilated patients. This approach could facilitate the early identification of patients at risk of weaning failure, allowing for improved clinical management.

Preventive Medicine 2.0 represents a paradigm shift in public health, driven by the integration of digital technologies, big data analytics, and artificial intelligence. This study provides a comprehensive review of the role of integrated digital health data systems in improving preventive strategies and community-level health outcomes. A structured analysis of high-impact literature published from 2020 onwards was conducted, focusing on key domains such as data integration, interoperability, artificial intelligence, and digital health implementation. The findings indicate that while foundational components such as electronic health records and big data systems have achieved significant adoption, critical gaps remain in interoperability and advanced analytics. The results demonstrate that higher levels of data integration are associated with substantial improvements in early detection, risk stratification, and targeted interventions. However, persistent barriers—including data fragmentation, infrastructure limitations, workforce gaps, and health data poverty—continue to limit the full potential of digital health systems. The study also highlights regional disparities, particularly in Latin America, where countries such as Mexico, Colombia, and Ecuador show progress but remain in transitional stages of digital transformation. The findings emphasize that the success of Preventive Medicine 2.0 depends not only on technological advancements but also on governance, equity, and system-level integration. Ultimately, this work supports the need for coordinated strategies to strengthen data-driven public health systems and enhance community-level health outcomes.