Examinando por Autor "Mohedano-Munoz, Miguel A."

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A streaming data visualization framework for supporting decision-making in the Intensive Care Unit
(Elsevier, 2023) Mohedano-Munoz, Miguel A.; Soguero-Ruiz, Cristina; Mora-Jiménez, Inmaculada; Rubio-Sánchez, Manuel; Álvarez-Rodríguez, Joaquín; Sanchez, Alberto
The number of reporting activities in real time has increased over the last years. This situation has pushed the need for providing real time analysis and visualizations to support decision-making. We propose a visualization framework for exploratory data analysis of multivariate data streams that relies on dimensionality reduction and machine learning techniques for plotting the data in two dimensions. Users can demarcate regions of interest for their study, and use them to make predictions or to decide when to train a new model. The knowledge gained from these visualizations allows users to: (i) characterize the data stream scenario; (ii) track the evolution of a case of interest; and (iii) configure and raise alarms according to the user-defined regions. We illustrate the effectiveness of our proposal through a case study analyzing real-world streaming data to identify patients with multi-drug resistant bacteria when they are in a hospital intensive care unit. Our visualization framework enables the patient follow-up which can allow clinicians to support decisions about the health status evolution of a particular patient. This could provide information for deciding on a particular treatment or whether to isolate patients with a high risk of having multi-drug resistant bacteria since their presence boosts infections in intensive care units.
Guided Decision Tree: A Tool to Interactively Create Decision Trees Through Visualization of Subsequent LDA Diagrams
(MDPI, 2024-11-14) Mohedano-Munoz, Miguel A.; Raya, Laura; Sanchez, Alberto
Decision trees are a widely used machine learning technique due to their ease of interpretation. and construction. This method allows domain experts to learn from raw data, but they cannot include their prior knowledge in the analysis due to its automatic nature, which implies minimal human intervention in its computation. Conversely, interactive visualization methods have proven to be effective in gaining insights from data, as they incorporate the researcher’s criteria into the analysis process. In an effort to combine both methodologies, we have developed a tool to manually build decision trees according to subsequent visualizations of data mapping after applying linear discriminant analysis in combination with Star Coordinates in order to analyze the importance of each feature in the separation. The nodes’ information contains data about the features that can be used to split and their cut-off values, in order to select them in a guided manner. In this way, it is possible to produce simpler and more expertly driven decision trees than those obtained by automatic methods. The resulting decision trees reduces the tree size compared to those generated by automatic machine learning algorithms, obtaining a similar accuracy and therefore improving their understanding. The tool developed and presented here to manually create decision trees in a guided manner based on the subsequent visualizations of the data mapping facilitates the use of this method in real-world applications. The usefulness of this tool is demonstrated through a case study with a complex dataset used for motion recognition, where domain experts built their own decision trees by applying their prior knowledge and the visualizations provided by the tool in node construction. The resulting trees are more comprehensible and explainable, offering valuable insights into the data and confirming the relevance of upper body features and hand movements for motion recognition.