Publicação
Intelligent analysis with visualisation of health data
| dc.contributor.advisor | Faria, Brígida Mónica | |
| dc.contributor.advisor | Oliveira, Alexandra Alves | |
| dc.contributor.author | Ribeiro, Inês Castro Henriques | |
| dc.date.accessioned | 2026-02-23T14:28:36Z | |
| dc.date.available | 2026-02-23T14:28:36Z | |
| dc.date.issued | 2025-11-24 | |
| dc.description.abstract | Intelligent data analysis and visualisation transform large volumes of information into practical knowledge, enabling faster, more accurate, and better-informed decision-making. By using charts, interactive dashboards, and other visual representations, it becomes easier to interpret complex data, identify patterns, and anticipate trends. The increasing digitalisation of healthcare has generated vast amounts of data from electronic medical records, laboratory tests, medical devices, and hospital systems, with the potential to extract even more information. This dissertation aims to facilitate the analysis of large datasets generated in the healthcare sector, supporting clinical decision-making. To achieve this, a literature review on intelligent data analysis and visualisation was conducted to answer the question: “How can intelligent data analysis and visualisation improve clinical decision-making in different healthcare contexts?”. Based on this review, a taxonomy was developed to support and simplify the interpretation of data visualisations. The development followed a systematic strategy designed to ensure comprehensiveness, clarity, and applicability. Existing literature and classification frameworks were reviewed to identify key criteria used in current visualisation taxonomies. A total of 59 visualisations were then grouped according to their purpose, analytical objective, and type of variables. The structure was validated using real-world data examples and translated into an interactive Power BI format, allowing users to dynamically explore categories and examples. Using this taxonomy, visualisations were created from a public database of patients with chronic obstructive pulmonary disease, and a clinical profile card was developed to present key patient information concisely and interactively. Finally, a convenience sample of healthcare professionals evaluated the clarity and usefulness of the visualisations in supporting clinical decision-making. The results showed that simpler visual representations, such as bar and scatter plots, were perceived as more accurate and useful. Moreover, 95% of professionals expressed a clear preference for interactive over static visualisations. The findings suggest that integrating these approaches has the potential to enhance understanding and improve support for decision-making in healthcare. By transforming complex information into accessible visual representations, BI tools can optimise resources, personalise care, and monitor health indicators more effectively. | eng |
| dc.identifier.tid | 204178320 | |
| dc.identifier.uri | http://hdl.handle.net/10400.22/31879 | |
| dc.language.iso | eng | |
| dc.rights.uri | N/A | |
| dc.subject | Data visualisation | |
| dc.subject | Intelligent data analysis | |
| dc.subject | Healthcare data | |
| dc.subject | data | |
| dc.subject | Clinical decision-making | |
| dc.title | Intelligent analysis with visualisation of health data | eng |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| thesis.degree.name | Master’s degree in Biostatistics and Bioinformatics Applied to Health |
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