Global ETD Search

11	Communication Privacy Management: Exploring Health Communication in Families Deborah Eyram Anornu (15334792) 22 April 2023 (has links) <p>Health communication is a growing field of research under interpersonal and family communication. Gaining enough health information is primarily the duty of healthcare providers. However, our immediate source of health information is family members; but most people decide to privatize and keep their health information from other relatives. The criterion for withholding health information, what contributes to the information shared, and how communication patterns affect health communication were all examined to understand the reasons behind this action.</p> <p>This qualitative study used the narrations on health communication from various families to form themes. In addition, responses were mostly from non-Western cultures, which helps to expand the applicability of the theory used.</p> <p>Some of the results were consistent with the criteria within the theory. However, other criteria were found that expand the theory in relation to health information. The new criteria found were when disclosing the information, age matter, I don’t understand the condition myself so how can my family, the number of people in the family matters. Also, reasons such as anticipated reactions from family members, and the severity of the condition came up when exploring what impacts how much health information family members share with one another. Finally, the frequency of communication and the initiator of conservations were found to influence health communication in families.</p> <p>To conclude, healthy communication in the family may impact individual communication on health. </p> Family care Primary health care Health promotion Preventative health care Communication studies Family communication Health communication communication privacy management theory Family communication patterns Health Disclosure Criteria for Disclosure
12	<b>Digital Health And Improvement Of Healthcare Access</b> Mateus Schmitt (18445557) 26 April 2024 (has links) <p dir="ltr">Digital Health technologies have revolutionized healthcare delivery, offering innovative solutions that enhance access, improve patient outcomes, and optimize the use of resources. Despite this advancement, health outcomes remain disparate across different social groups, with underprivileged populations at an increased risk of poor health outcomes due to inadequate access to care. Digital Health technologies serve as a critical intervention in mitigating these disparities, particularly for groups affected by geographical, economic, and infrastructural barriers.<br><br>The purpose of this study was to conduct a review of the current state of Digital Health technologies, including Software as a Medical Device (SaMD), Wearable Health, Portable Diagnostic Devices, and remote care platforms, and their impact on healthcare accessibility. Employing qualitative methodology, this metasynthesis emphasized an important discovery: the need for a paradigm shift among stakeholders in healthcare towards integrated and digitally-driven patient care. This shift requires more than just an understanding of new technologies. It demands a fundamental re-evaluation of patient care methods and the orchestration of the entire healthcare system towards integrated digital practices. Importantly, this study found that the pace of digitalization must be carefully managed and cultural factors must be considered and signals the urgency for a balanced approach to digital integration in healthcare.</p> Digital electronic devices Digital health Rural and remote health services Health equity Preventative health care Social determinants of health Applications in health Digital Health Technology Healthcare Healthcare Access Digital Integration Medical Devices
13	EXPLORING GRAPH NEURAL NETWORKS FOR CLUSTERING AND CLASSIFICATION Fattah Muhammad Tahabi (14160375) 03 February 2023 (has links) <p><strong>Graph Neural Networks</strong> (GNNs) have become excessively popular and prominent deep learning techniques to analyze structural graph data for their ability to solve complex real-world problems. Because graphs provide an efficient approach to contriving abstract hypothetical concepts, modern research overcomes the limitations of classical graph theory, requiring prior knowledge of the graph structure before employing traditional algorithms. GNNs, an impressive framework for representation learning of graphs, have already produced many state-of-the-art techniques to solve node classification, link prediction, and graph classification tasks. GNNs can learn meaningful representations of graphs incorporating topological structure, node attributes, and neighborhood aggregation to solve supervised, semi-supervised, and unsupervised graph-based problems. In this study, the usefulness of GNNs has been analyzed primarily from two aspects - <strong>clustering and classification</strong>. We focus on these two techniques, as they are the most popular strategies in data mining to discern collected data and employ predictive analysis.</p> Biomechanical engineering Neural engineering Health promotion Preventative health care Applications in health Spatial data and applications Evolutionary computation Natural language processing Planning and decision making Data engineering and data science Data mining and knowledge discovery Graph, social and multimedia data Information retrieval and web search Knowledge and information management Context learning Deep learning Neural networks Semi- and unsupervised learning Data structures and algorithms Graph neural network Node classification Graph clustering Temporal graphs dynamic graphs NODE2VEC Graph Attention Mechanism Hunting BiLSTM model EHR data colorectal Cancer Cancers Cancer symptoms symptom Symptom cluster studies Coauthorship networks network analysis Word2vec Hierarchical Clustering method Dunn index semantic analysis text mining Natural Language Processing Tool UMLS identifiers umls Clinical Data Management

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