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Item The Role of Data Science in Electronic Health Records: How Medical Decision Making can be improved based on a Comprehensive Electronic Medical Record?(Grupo GRIAL, 2026-03-10) Azadi, AliDespite the integration of modern technologies in medical applications, a significant gap remains in achieving high-level interaction between medical staff, physicians, and the systems they utilize. This gap often results in inefficiencies, user frustration, medical errors, and, in some cases, compromised patient safety, highlighting the critical need for improved system design. To address this issue, this thesis examines the impact of user interaction with these systems in medical settings, with a focus on the crucial role of Human-Computer Interaction (HCI) elements. A comprehensive systematic literature review (SLR) was conducted to identify and categorize HCI elements applicable within Clinical Decision Support System (CDSS) environments, emphasizing the necessity for Electronic Medical Records (EMRs) to be designed with these elements in mind, as they serve as the primary data source for CDSS. The current thesis extracted and categorized various HCI evaluation methods from existing studies based on their technical characteristics, providing a structured guideline for future investigations. Furthermore, the thesis details the impact of each HCI element on CDSS functionality, distinguishing between positive contributions and negative factors (termed "HCI barriers") that hinder effective interaction. Solutions to these barriers are also discussed in a dedicated chapter. Fundamentally, this thesis introduces a pivotal bridge between HCI principles and the critical domains of medical data management and quality. This foundational work has already led to the publication of three peer-reviewed scientific papers in prestigious journals, demonstrating its significant contribution to the field. Moreover, the benefits of integrating these HCI elements into other interconnected medical platforms, such as Personal Health Records (PHRs), were articulated. A novel cyclical EMR model is proposed that restructures patient data into distinct treatment cycles, thereby aligning digital records with the iterative nature of clinical workflows. This model enhances several critical HCI elements (including interface clarity, individuality, explainability, and user satisfaction) while improving data analysis and decision support accuracy. Empirical evaluations based on the proposed model reveal that structured data categorization and cyclebased data entry enhance the transparency and explainability of CDSS outputs, contributing to improved system usability and interpretability. Ultimately, this thesis presents a scientific framework that bridges the gap between HCI and medical data management, offering both theoretical insights and practical contributions to medical informatics. The significance of these contributions is further demonstrated by the publication of four peer-reviewed papers in prestigious journals, establishing a robust foundation for advancing CDSS development and user-centered system design in future research.Item Testing and Improvements of KoopaML: A Platform to Ease the Development of Machine Learning Pipelines in the Medical Domain(Springer, 2023-05-01) Vázquez-Ingelmo, A.; García-Holgado, A.; García-Peñalvo, F. J.; Fraile-Sanchón, R.; Pérez-Sánchez, P.; Antúnez-Muiños, P.; Sánchez-Puente, A.; Vicente-Palacios, V.; Dorado-Díaz, P. I.; Cruz-González, I.; Sánchez, P. L.Machine Learning (ML) applications in complex domains, such as the medical domain, can be highly beneficial, but also hazardous if some concepts are overlooked. In this context, however, health professionals denote expertise in their domain, but they often lack skills in terms of ML. In this sense, to leverage ML applications in the medical domain, it is important to combine both domain expertise and ML-related skills. In previous works, we tackled this challenge in the health context through a visual platform (KoopaML) that enables lay users to build ML pipelines. The present work describes the challenges derived from the first version of the platform and the prototypes for the new features designed to address them. The prototypes have been validated by two experts, obtaining highly valuable feedback.Item Automatic generation of software interfaces for supporting decision-making processes. An application of domain engineering & machine learning(Grupo GRIAL, 2022-07-26) Vázquez-Ingelmo, A.Data analysis is a key process to foster knowledge generation in particular domains or fields of study. With a strong informative foundation derived from the analysis of collected data, decision-makers can make strategic choices with the aim of obtaining valuable benefits in their specific areas of action. However, given the steady growth of data volumes, data analysis needs to rely on powerful tools to enable knowledge extraction. Information dashboards offer a software solution to analyze large volumes of data visually to identify patterns and relations and make decisions according to the presented information. But decision-makers may have different goals and, consequently, different necessities regarding their dashboards. Moreover, the variety of data sources, structures, and domains can hamper the design and implementation of these tools. This Ph.D. Thesis tackles the challenge of improving the development process of information dashboards and data visualizations while enhancing their quality and features in terms of personalization, usability, and flexibility, among others. Several research activities have been carried out to support this thesis. First, a systematic literature mapping and review was performed to analyze different methodologies and solutions related to the automatic generation of tailored information dashboards. The outcomes of the review led to the selection of a model-driven approach in combination with the software product line paradigm to deal with the automatic generation of information dashboards. In this context, a meta-model was developed following a domain engineering approach. This meta-model represents the skeleton of information dashboards and data visualizations through the abstraction of their components and features and has been the backbone of the subsequent generative pipeline of these tools. The meta-model and generative pipeline have been tested through their integration in different scenarios, both theoretical and practical. Regarding the theoretical dimension of the research, the meta-model has been successfully integrated with other meta-model to support knowledge generation in learning ecosystems, and as a framework to conceptualize and instantiate information dashboards in different domains. In terms of the practical applications, the focus has been put on how to transform the meta-model into an instance adapted to a specific context, and how to finally transform this later model into code, i.e., the final, functional product. These practical scenarios involved the automatic generation of dashboards in the context of a Ph.D. Programme, the application of Artificial Intelligence algorithms in the process, and the development of a graphical instantiation platform that combines the meta-model and the generative pipeline into a visual generation system. Finally, different case studies have been conducted in the employment and employability, health, and education domains. The number of applications of the meta-model in theoretical and practical dimensions and domains is also a result itself. Every outcome associated to this thesis is driven by the dashboard meta-model, which also proves its versatility and flexibility when it comes to conceptualize, generate, and capture knowledge related to dashboards and data visualizations.