Seminars in Medical Writing and Education
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Exploration of Scientific Documents through Unsupervised Learning-Based Segmentation Techniques
Get PDFNavigating the extensive landscape of scientific literature presents a significant challenge, prompting the development of innovative methodologies for efficient exploration. Our study introduces a pioneering approach for unsupervised segmentation, aimed at revealing thematic trends within articles and enhancing the accessibility of scientific knowledge. Leveraging three prominent clustering algorithms—K-Means, Hierarchical Agglomerative, and DBSCAN—we demonstrate their proficiency in generating meaningful clusters, validated through assessment metrics including Silhouette Score, Calinski-Harabasz Index, and Davies-Bouldin Index. Methodologically, comprehensive web scraping of scientific databases, coupled with thorough data cleaning and preprocessing, forms the foundation of our approach. The efficacy of our methodology in accurately identifying scientific domains and uncovering interdisciplinary connections underscores its potential to revolutionize the exploration of scientific publications. Future endeavors will further explore alternative unsupervised algorithms and extend the methodology to diverse data sources, fostering continuous innovation in scientific knowledge organizatio
Analyzing the Impact of Digital Health Communication on Patient Engagement and Treatment Adherence
Get PDFModern healthcare systems now strongly rely on digital health communication to get patients more engaged in their treatment and assist them to stay with their prescriptions. Healthcare professionals may now have more tailored and continuous interactions with their patients since so many individuals use mobile applications, telemedicine systems, and digital health data. With an eye on how technology-based solutions can enable patients to follow their treatment regimens for chronic illnesses and preventative care, this paper investigates how digital health communication influences patient engagement and treatment commitment. This paper examines how well various digital communication technologies text systems, notes, video chats, real-time tracking help patients and medical professionals interact with one another. The research also examines how successfully digital health technologies enable individuals to follow their treatments as well as how their behaviour, drive, and overall pleasure in regard to care. This paper uses a lot of current research, polls, and case studies to find the main things that make digital communication work in healthcare. These are ease of use, accessibility, perceived value, and trust in technology. The results show that digital health communication makes patients more interested by giving them personalised material, letting them connect with healthcare professionals at the right time, and giving them more chances to learn. Digital platforms have also been shown to help people stick with their treatments by reminding them, tracking their progress, and letting healthcare workers offer real-time support when they are used with personalised treatment plans. Even though there are benefits, there are still big problems that need to be fixed, like not knowing how to use technology, worries about privacy, and unequal access to digital tools
Evaluating the Impact of Machine Learning in Predictive Analytics for Personalized Healthcare Informatics
Get PDFBy adding machine learning (ML) into predictive analytics, the area of personalised healthcare computing has evolved and new approaches to enhance patient outcomes via tailored treatment plans have been generated. This paper examines how healthcare treatments could be tailored and predicted using machine learning methods. It underlines how crucial sophisticated analytics are for enhancing patient care and guiding clinical choices. Treatment is more accurate, more efficient, and better generally when one can predict how a condition will worsen, choose the best course of action for taking drugs, and observe any issues. Like controlled and unstructured learning algorithms, machine learning models have proved to be able to efficiently examine large and complex clinical datasets including electronic health records (EHR) and genetic data. These models identify hidden trends, relationships, and patterns that enable us to forecast individual health paths, identify those at risk, and simplify preventive action. ML also makes it feasible to merge many kinds of data, therefore providing clinicians with a more complete picture of every patient\u27s health and, ultimately, facilitates the provision of more individualised, better treatment. Many facets of healthcare, including management of chronic illnesses, cancer detection, mental health analysis, and new medication discovery, employ predictive models. By helping clinicians make decisions based on data, ML models assist to reduce errors and enhance the flow of treatment. Still, there are issues including concerns about data security, model understanding, and the necessity of consistent frameworks to ensure models are robust and dependable in real-life clinical environments. This work also addresses the moral issues raised by using machine learning algorithms in tailored healthcare. It addresses issues like prejudice, justice, and patient agreement. It emphasises the need of cooperation among legislators, data scientists, and healthcare professionals to maintain developing models so that the whole potential of machine learning in healthcare may be fulfilled
Risk Assessment and Mitigation Strategies in Medical Informatics for Cybersecurity and Patient Data Protection
Get PDFWhile combining modern technology with medical analytics has greatly improved healthcare services, it has also generated several questions over hacking and patient data protection. The risks healthcare companies confront becoming more complex as more of them use telemedicine, electronic health records (EHRs), and other digital technologies. Cyberattacks on private patient data and healthcare systems may have disastrous effects including data breaches, lost vital services, and individuals entering medical records without authorisation. Regarding hacking, this paper examines the hazards associated with medical computers with an eye on the weaknesses in the present healthcare system. It identifies the primary hazards to data integrity and patient risk including ransomware, hacking, and insider threats. Furthermore discussed in the paper are some approaches to enhance medical computer safety. Among these strategies are strong encryption, secure login systems, and continuous monitoring tools capable of locating and reacting to security concerns in real time. The article also discusses the need of strong legal frameworks requiring best practices for data security and the need of healthcare professionals learning about hacking. Furthermore underlined is the need of developing a security attitude within healthcare institutions in order to resist fresh internet risks. Finally, the study advises greater research and development to ensure patient data is safer and offers instances of improved approaches to manage risks in healthcare systems. Maintaining confidence, adhering to regulations, and the overall performance of healthcare delivery systems as healthcare providers become digital depend critically on patient data being secure and private
Comparing Clinical Outcomes and Treatment Efficiency of 3D Conformal and Intensity Modulated Radiotherapy
Get PDFRecently, a lot of progress has been made in using radiation therapy to treat cancer. The two most common methods are 3D Conformal Radiotherapy (3D-CRT) and Intensity Modulated Radiotherapy (IMRT). Both ways try to give tumors the exact amount of radiation they need while causing as little damage as possible to good cells around them. In this study, the clinical results and treatment effectiveness of 3D-CRT and IMRT are compared in cancer patients with different types of radiation therapy. A historical cohort study included 120 patients from different hospitals. They were split into two groups, with one group getting 3D-CRT and the other IMRT. The main goals of the study were to compare the two groups in terms of tumor control rates, side effects from treatment, and overall survival (OS). The effectiveness of the treatment was also judged by looking at the total time needed for planning and carrying out the treatment, as well as the radiation doses given to both the tumor and healthy cells. Overall, our results showed that IMRT had better tumor control rates than 3D-CRT, with more cases of local control and fewer cases of cancer coming back at treatment sites. When compared to 3D-CRT, the IMRT group had a significantly lower rate of acute radiation-induced effects, such as skin discomfort and stomach problems. Because it can send out very directed radiation beams, IMRT was also linked to less damage to good organs around the tumor, like the spinal cord and lungs. IMRT, on the other hand, needs more complicated treatment planning and takes longer to give than 3D-CRT, which can make prices and resource use go up. Even so, IMRT\u27s higher level of accuracy made it a clear winner when it came to controlling tumors and improving patients\u27 quality of life
Implementation of Augmented Reality in the Teaching of Computer Networks
Get PDFThe advancement of digital tools has significantly transformed the daily lives of undergraduate students, especially in Computer Engineering, Informatics and Mechatronics. At the Centro Universitario de la Ciénega, located in Ocotlán, Jalisco, various technologies are used in computer networking courses, whose main objective is for students to develop interconnection, configuration, and communication skills between computers through switches and routers in a specialized laboratory. This communication exhibits the implementation of Augmented Reality as a teaching methodology, integrating guided activities through a website. The activities describe step by step the practical procedures, so that students can visualize the network devices “almost” in reality through interactive photographs taken with their smartphones or tablets, as if they were physically in the laboratory. Augmented Reality is implemented by combining digital information with real situations that appear in images. The website covers the topics of the subject, which are organized in chapters, which students can access through QR codes. Students scan the QR codes and can then launch the ROAR app, which adds an augmented reality experience to the content, thereby enriching their understanding and practical learning of computer networks
Research in higher medical education: cognitive biases and their control from a hermeneutic and ethical perspective
Get PDFIntroduction: the theoretical-methodological analysis of the teaching-learning process in higher medical education requires the evaluation of cognitive biases. Objective: to reflect on the cognitive biases of research in higher medical education and their control from a hermeneutic and ethical perspective.Method: a bibliographic review was carried out of articles published in English and Spanish in scientific journals indexed in digital databases. Keywords were used, and 16 articles were selected that met the selection criteria. Content analysis was carried out.Development: research in higher medical education is the process of knowledge generation that acts as a sensor of the reality of academic phenomena, both individual and collective. Researchers have the potential to make mistakes where cognitive skills are fundamental. Despite having tools to control biases, scientists are vulnerable to possible deviations in results during the analysis and evaluation of the environment of the context studied. Their responsibility is to identify and control these biases in order to achieve superior quality results, reducing the risk of errors and developing reliable science. In this sense, the hermeneutic perspective becomes a process that favors understanding and interpretation where ethics must be a transversal axis.Conclusions: promoting critical thinking from the hermeneutic perspective and achieving empathy through communicative exercise and maintaining an ethical attitude are valid actions in favor of controlling cognitive biases in research in higher medical education.
Deep Learning Approaches for Lung Cancer Detection: A Comprehensive Analysis of Models, Optimization Techniques, and Architectures
No full textLung cancer continues to be a significant global health challenge, highlighting the urgent need for innovative methods for early detection and precise diagnosis. This paper provides an extensive review of various deep learning techniques applied to lung cancer detection using medical image datasets. We examine a broad spectrum of deep learning models, including sequential models, convolutional neural networks (CNNs), and several optimization algorithms like ADAM, SGD, and RMSPROP. The analysis begins with the use of sequential models for binary classification of lung cancer images, followed by an exploration of optimization strategies to enhance model performance. We then extend the discussion to multi-class classification, focusing on the different types of lung cancer. To ensure thorough model training and evaluation, random mini-batch evaluations are performed using Python Keras. Additionally, CNNs are employed for effective feature extraction and classification, leveraging spatial patterns in the imaging data. Alongside traditional architectures, we incorporate data augmentation and regularization techniques to prevent overfitting and improve the models\u27 generalization ability. The research also explores a range of CNN architectures, including the widely recognized VGG model, to identify the most suitable configurations for lung cancer detection. Beyond conventional models, alternative deep learning methods such as recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and autoencoders are also considered. By determining the optimal approach, this study aims to enhance the accuracy and efficiency of lung cancer diagnosis, potentially leading to better patient outcomes and reduced mortality rates
Data processing in internet of things networks
Get PDFAs an important component of the IoT ecosystem, data sets are an essential part of the decision-making process. IoT devices generate hundreds of new data sets every second and the problem of managing them appropriately arises. In the process of data management, their processing is a particularly complex and important process. Various methods and tools are used to process data sets in the IoT ecosystem. Here, data processing allows you to speed up the decision-making process and make it less risky by transforming that data into the required form and making it relatively simple. The article explores the concept of data, data management and processing in the IoT ecosystem and shows a simple example of data processing
Educational innovation and youth participation: the Inverted Classroom in the optional vote
Get PDFThe implementation of the Inverted Classroom in the civic education of high school students in Ecuador represented an attempt to modernize teaching methods and strengthen youth electoral participation. However, this phenomenon could not be analyzed in isolation, as similar challenges persist in Latin America in terms of political interest and trust in democratic institutions. During 2023 and 2024, the region experienced institutional crises and increasing polarization, affecting youth participation in electoral processes.The Inverted Classroom allowed students to access theoretical content outside the classroom and focus on reflection and application in class, promoting more dynamic learning. In countries such as Mexico and Colombia, similar initiatives strengthened democratic education through digital platforms and youth participation programs. However, in countries with technological gaps, such as Brazil and Peru, the implementation of this methodology required greater investment in infrastructure and teacher training.In conclusion, the Inverted Classroom proved to be an effective strategy to encourage youth participation in democracy. Its success depended on adaptation to each national context, as well as institutional support to ensure equitable access to civic and digital education