Visual Analytics for the Representation, Exploration, and Analysis of High-Dimensional, Multi-faceted Medical Data

Raidou, Renata Georgia

doi:10.1007/978-3-030-14227-8_10

Cited by 10 publications

(3 citation statements)

References 120 publications

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“…The central tenet argued in this report is that engaging multiple direct representations of the forecasts interactively leads to a deeper understanding of the underlying dynamics. To this end, EpiForecast uses the popular linking-andbrushing model in which a user can inspect a visual element in one plot and the highlight is reflected in the other plots [26]. For example, if the user hovers their mouse over a nearest neighbor dot on the traditional time-series plot, then the coloring of the visual elements in each of the other plots is changed to highlight that same neighbor.…”

Section: Plos Onementioning

confidence: 99%

Interpretable, non-mechanistic forecasting using empirical dynamic modeling and interactive visualization

et al. 2023

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Forecasting methods are notoriously difficult to interpret, particularly when the relationship between the data and the resulting forecasts is not obvious. Interpretability is an important property of a forecasting method because it allows the user to complement the forecasts with their own knowledge, a process which leads to more applicable results. In general, mechanistic methods are more interpretable than non-mechanistic methods, but they require explicit knowledge of the underlying dynamics. In this paper, we introduce EpiForecast, a tool which performs interpretable, non-mechanistic forecasts using interactive visualization and a simple, data-focused forecasting technique based on empirical dynamic modelling. EpiForecast’s primary feature is a four-plot interactive dashboard which displays a variety of information to help the user understand how the forecasts are generated. In addition to point forecasts, the tool produces distributional forecasts using a kernel density estimation method–these are visualized using color gradients to produce a quick, intuitive visual summary of the estimated future. To ensure the work is FAIR and privacy is ensured, we have released the tool as an entirely in-browser web-application.

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Section: Plos Onementioning

confidence: 99%

Interpretable, non-mechanistic forecasting using empirical dynamic modeling and interactive visualization

et al. 2023

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“…VA is intended to keep the user within the analysis loop, so that human comprehension and perception actively contribute to generating new insights and increasing confidence with the analysis results. VA has extensively studied the problem of representing complex, multi-faceted phenomena [7,8], which is also the type of phenomena often encountered within real-life business processes.…”

Section: Introductionmentioning

confidence: 99%

Tiramisù: Making Sense of Multi-Faceted Process Information Through Time and Space

Alman,

Arleo,

Beerepoot

et al. 2024

Preprint

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Knowledge-intensive processes represent a particularly challenging scenario for process mining. The flexibility that such processes allow constitutes a hurdle as they are hard to capture in a single model. To tackle this problem, multiple visual representations of the same processes could be beneficial, each addressing different information dimensions according to the specific needs and background knowledge of the concrete process workers and stakeholders. In this paper, we propose, describe, and evaluate a framework, named Tiramisù, that leverages visual analytics for the interactive visualization of multi-faceted process information, aimed at supporting the investigation and insight generation of users in their process analysis tasks. Tiramisù is based on a multi-layer visualization methodology that includes a visual backdrop that provides context and an arbitrary number of superimposed and on-demand dimension layers. This arrangement allows our framework to display process information from different perspectives and to project this information onto a domain-friendly representation of the context in which the process unfolds. We provide an in-depth description of the approach's founding principles, deeply rooted in visualization research, that justify our design choices for the whole framework. We demonstrate the feasibility of the framework through its application in two use-case scenarios in the context of healthcare and personal information management. Plus, we conducted qualitative evaluations with potential end users of both scenarios, gathering precious insights about the efficacy and applicability of our framework to various application domains.

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“…The addition of interaction means that the analyst can display the details which are most important to them, and follow-up on any potential insights they gain from the resulting visualizations. Interaction is especially important for complex datasets because it means a lot of information can be included without needing to visualize it all at once 14 , 15 . Spatiotemporal data, in particular, is challenging to visualize because both spatial and temporal data are often best represented using the ‘position’ visual channel, and it is therefore difficult to visualize both together 16 .…”

Section: Introductionmentioning

confidence: 99%

EpiVECS: exploring spatiotemporal epidemiological data using cluster embedding and interactive visualization

Mason,

Hicks,

Almeida

2023

Sci Rep

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The analysis of data over space and time is a core part of descriptive epidemiology, but the complexity of spatiotemporal data makes this challenging. There is a need for methods that simplify the exploration of such data for tasks such as surveillance and hypothesis generation. In this paper, we use combined clustering and dimensionality reduction methods (hereafter referred to as ‘cluster embedding’ methods) to spatially visualize patterns in epidemiological time-series data. We compare several cluster embedding techniques to see which performs best along a variety of internal cluster validation metrics. We find that methods based on k-means clustering generally perform better than self-organizing maps on real world epidemiological data, with some minor exceptions. We also introduce EpiVECS, a tool which allows the user to perform cluster embedding and explore the results using interactive visualization. EpiVECS is available as a privacy preserving, in-browser open source web application at https://episphere.github.io/epivecs.

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Visual Analytics for the Representation, Exploration, and Analysis of High-Dimensional, Multi-faceted Medical Data

Cited by 10 publications

References 120 publications

Interpretable, non-mechanistic forecasting using empirical dynamic modeling and interactive visualization

Interpretable, non-mechanistic forecasting using empirical dynamic modeling and interactive visualization

Tiramisù: Making Sense of Multi-Faceted Process Information Through Time and Space

EpiVECS: exploring spatiotemporal epidemiological data using cluster embedding and interactive visualization

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