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Implementation of a Machine Learning Algorithm for the Detection of Cardiovascular Diseases in Adult Patients in Public Hospitals of Lima, Peru, 2023
Get PDFIntroduction: Cardiovascular diseases are one of the leading causes of death worldwide. In Lima, Peru, public hospitals face significant challenges in the early and accurate diagnosis of these diseases due to limited resources and trained personnel. The implementation of machine learning (ML) algorithms offers a promising solution to improve the detection and management of cardiovascular diseases.Objective: The objective of this study is to implement and evaluate a machine learning algorithm for the detection of cardiovascular diseases in adult patients attended to in public hospitals of Lima, Peru, in the year 2023.Methodology: Medical data from 10,000 adult patients were collected, including medical histories, laboratory test results, and electrocardiogram (ECG) records from various public hospitals in Lima. The data were cleaned and normalized to ensure their quality and consistency. A classification algorithm based on deep neural networks was selected. The model was trained using 80% of the data and validated with the remaining 20%. Metrics of accuracy, sensitivity, and specificity were used to evaluate the model\u27s performance.Results: The model achieved an accuracy of 92% in detecting cardiovascular diseases. The sensitivity was 89%, indicating that the model correctly identified 89% of positive cases. The specificity reached 94%, meaning the model correctly identified 94% of negative cases.Conclusion: The implementation of the machine learning algorithm proved effective for the detection of cardiovascular diseases in adult patients in public hospitals in Lima, Peru. With high accuracy, sensitivity, and specificity, this approach has the potential to significantly improve medical care and patient outcomes in resource-limited settings. Integrating this system into clinical processes is recommended to maximize its positive impact on public health
Role of Redox Reactions and AI-Driven Approaches in Enhancing Nutrient Availability for Plants
Get PDFEmpirical studies have shown that environmental variability in the field remains uncontrolled in certain cases, with research often conducted at a limited number of agricultural sites. Direct measurements of redox potential in soils have been reported, yet quantifying rapid changes in this variable across microsites proves inaccessible in situ. Existing measurements of redox potential also fail to account for variability in the identity of reduced or oxidized compounds. Additionally, methodological constraints and researcher bias, particularly in studies focusing on processes in reduced sediments, may impair interpretations of anabolic reactions resulting from oxidation.Case studies further indicate that the effects of redox potential on nitrification, net mineralization, or immobilization of other nutrients often remain unmeasured. As a result, increased denitrification might stimulate nitrification, reducing the effects of nitrogen immobilization due to increasing carbon storage in environments where reduction predominates.Given the absence of studies specifically exploring the balance between reduction and oxidation in relation to nutrient availability, assessing the magnitude and likelihood of methodological shortcomings based on prior field research remains challenging. Existing research serves as a foundation for understanding how this balance may significantly influence nutrient dynamics and availability at larger scales. Future studies manipulating redox potential in the field should consider factors that could disproportionately facilitate reductions before an eastward shift occurs in the balance between oxidation and reduction in response to organic matter addition. Addressing these gaps will enhance understanding of redox reactions and their potential role in stimulating denitrification and sulfide responses
Artificial Intelligence Applied to Telemedicine: opportunities for healthcare delivery in rural areas
Get PDFThe integration of artificial intelligence (AI) in telemedicine is revolutionizing the provision of healthcare services, especially in rural areas. These technologies enable the overcoming of geographical and resource barriers, facilitating precise diagnoses, personalized recommendations, and continuous monitoring through portable devices. AI systems analyze patient data and suggest the most appropriate care options based on their health profile, thus optimizing the efficiency of the healthcare system and improving patient satisfaction. In addition, the automation of administrative tasks through AI frees up time for healthcare professionals to concentrate on direct care. To ensure trust and effectiveness in these technologies, it is essential to implement clinically validated and unbiased algorithms, while fostering transparency and collaboration among developers, healthcare professionals, and regulators. Therefore, AI applied to telemedicine offers a revolutionary opportunity to improve the accessibility and quality of healthcare in rural areas by promoting more equitable and efficient care
Use of AI to improve the teaching-learning process in children with special abilities
Get PDFThrough adaptive and assistive technologies, AI enables deep personalization of learning, as well as adjusting content and pacing based on each student\u27s individual needs. These systems not only optimize the delivery of educational material, but also offer new forms of interaction and accessibility for students with physical, visual and hearing disabilities. The research was conducted with the purpose of exploring how artificial intelligence (AI) has revolutionized special education. The results indicate that the implementation of tools such as speech recognition, brain-computer interfaces and text-to-speech software significantly improves student autonomy and participation in the classroom. However, the data also highlights the importance of addressing ethical and accessibility issues, ensuring that these technological advances benefit all students equitably and without compromising their security or privacy. The inquiry concluded that, while AI presents transformative opportunities for special education, its integration requires thoughtful approaches that prioritize inclusion and equity