ThreadStates: State-based Visual Analysis of Disease Progression

A growing number of longitudinal cohort studies are generating data with extensive patient observations across multiple timepoints. Such data offers promising opportunities to better understand the progression of diseases. However, most existing visual analysis tools of health records are aimed at general event sequences and little attention has been paid to common types of clinical data that contain extensive observations. To fill this gap, we designed and implemented ThreadStates, an interactive visual analytics tool for the exploration of longitudinal patient cohort data. The focus of ThreadStates is to identify the states of disease progression by learning from observation data in a human-in-the-loop manner. We propose a novel matrix+glyph design and combine it with a scatter plot to enable seamless identification, observation, and refinement of states. The disease progression patterns are then revealed in terms of state transitions using Sankey-based visualizations. We employ sequence clustering techniques to find patient groups with distinctive progression patterns, and to reveal the association between disease progression and patient-level features. The design and development were driven by a requirement analysis and iteratively refined based on feedback from domain experts over the course of a 10-month design study. Case studies and expert interviews demonstrate that ThreadStates can successively summarize disease states, reveal disease progression, and compare patient groups.

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TV-MV Analytics: A visual analytics framework to explore time-varying multivariate data

Information Visualization ◽

10.1177/1473871619858937 ◽

2019 ◽

Vol 19 (1) ◽

pp. 3-23

Author(s):

Aurea Soriano-Vargas ◽

Bernd Hamann ◽

Maria Cristina F de Oliveira

Keyword(s):

Visual Analytics ◽

Visual Analysis ◽

Multivariate Data ◽

Visual Exploration ◽

Data Sets ◽

Time Varying ◽

Domain Experts ◽

Data Mining Algorithms ◽

Temporal Relationships ◽

Visualization Techniques

We present an integrated interactive framework for the visual analysis of time-varying multivariate data sets. As part of our research, we performed in-depth studies concerning the applicability of visualization techniques to obtain valuable insights. We consolidated the considered analysis and visualization methods in one framework, called TV-MV Analytics. TV-MV Analytics effectively combines visualization and data mining algorithms providing the following capabilities: (1) visual exploration of multivariate data at different temporal scales, and (2) a hierarchical small multiples visualization combined with interactive clustering and multidimensional projection to detect temporal relationships in the data. We demonstrate the value of our framework for specific scenarios, by studying three use cases that were validated and discussed with domain experts.

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Conventional displays of structures in data compared with interactive projection-based clustering (IPBC)

International Journal of Data Science and Analytics ◽

10.1007/s41060-021-00264-2 ◽

2021 ◽

Author(s):

Michael C. Thrun ◽

Felix Pape ◽

Alfred Ultsch

Keyword(s):

Visual Analytics ◽

Clustering Algorithms ◽

Empirical Evaluation ◽

Similar Data ◽

Qualitative Comparison ◽

Domain Experts ◽

Human In The Loop ◽

3D Displays ◽

Benchmark Datasets ◽

Data Points

AbstractClustering is an important task in knowledge discovery with the goal to identify structures of similar data points in a dataset. Here, the focus lies on methods that use a human-in-the-loop, i.e., incorporate user decisions into the clustering process through 2D and 3D displays of the structures in the data. Some of these interactive approaches fall into the category of visual analytics and emphasize the power of such displays to identify the structures interactively in various types of datasets or to verify the results of clustering algorithms. This work presents a new method called interactive projection-based clustering (IPBC). IPBC is an open-source and parameter-free method using a human-in-the-loop for an interactive 2.5D display and identification of structures in data based on the user’s choice of a dimensionality reduction method. The IPBC approach is systematically compared with accessible visual analytics methods for the display and identification of cluster structures using twelve clustering benchmark datasets and one additional natural dataset. Qualitative comparison of 2D, 2.5D and 3D displays of structures and empirical evaluation of the identified cluster structures show that IPBC outperforms comparable methods. Additionally, IPBC assists in identifying structures previously unknown to domain experts in an application.

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NE-Motion: Visual Analysis of Stroke Patients Using Motion Sensor Networks

Sensors ◽

10.3390/s21134482 ◽

2021 ◽

Vol 21 (13) ◽

pp. 4482

Author(s):

Rodrigo Colnago Contreras ◽

Avinash Parnandi ◽

Bruno Gomes Coelho ◽

Claudio Silva ◽

Heidi Schambra ◽

...

Keyword(s):

Visual Analysis ◽

Treatment Strategies ◽

Motion Capture Data ◽

Motion Sensors ◽

Healthy Individuals ◽

Stroke Patients ◽

Close Collaboration ◽

Domain Experts ◽

Bedside Test ◽

Detailed Assessment

A large number of stroke survivors suffer from a significant decrease in upper extremity (UE) function, requiring rehabilitation therapy to boost recovery of UE motion. Assessing the efficacy of treatment strategies is a challenging problem in this context, and is typically accomplished by observing the performance of patients during their execution of daily activities. A more detailed assessment of UE impairment can be undertaken with a clinical bedside test, the UE Fugl–Meyer Assessment, but it fails to examine compensatory movements of functioning body segments that are used to bypass impairment. In this work, we use a graph learning method to build a visualization tool tailored to support the analysis of stroke patients. Called NE-Motion, or Network Environment for Motion Capture Data Analysis, the proposed analytic tool handles a set of time series captured by motion sensors worn by patients so as to enable visual analytic resources to identify abnormalities in movement patterns. Developed in close collaboration with domain experts, NE-Motion is capable of uncovering important phenomena, such as compensation while revealing differences between stroke patients and healthy individuals. The effectiveness of NE-Motion is shown in two case studies designed to analyze particular patients and to compare groups of subjects.

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Visual Analysis of Biomarkers Reveals Differences in Lipid Profiles and Liver Enzymes before and after Gastric Sleeve and Bypass

Obesity Facts ◽

10.1159/000510401 ◽

2021 ◽

pp. 1-11

Author(s):

Marijn Marthe Georgine van Berckel ◽

Saskia L.M. van Loon ◽

Arjen-Kars Boer ◽

Volkher Scharnhorst ◽

Simon W. Nienhuijs

Keyword(s):

Bariatric Surgery ◽

Visual Analytics ◽

Biochemical Markers ◽

Visual Analysis ◽

Large Population ◽

High Volume ◽

Cholesterol Concentration ◽

Health State ◽

Data Set ◽

Before And After

Introduction: Bariatric surgery results in both intentional and unintentional metabolic changes. In a high-volume bariatric center, extensive laboratory panels are used to monitor these changes pre- and postoperatively. Consecutive measurements of relevant biochemical markers allow exploration of the health state of bariatric patients and comparison of different patient groups. Objective: The objective of this study is to compare biomarker distributions over time between 2 common bariatric procedures, i.e., sleeve gastrectomy (SG) and gastric bypass (RYGB), using visual analytics. Methods: Both pre- and postsurgical (6, 12, and 24 months) data of all patients who underwent primary bariatric surgery were collected retrospectively. The distribution and evolution of different biochemical markers were compared before and after surgery using asymmetric beanplots in order to evaluate the effect of primary SG and RYGB. A beanplot is an alternative to the boxplot that allows an easy and thorough visual comparison of univariate data. Results: In total, 1,237 patients (659 SG and 578 RYGB) were included. The sleeve and bypass groups were comparable in terms of age and the prevalence of comorbidities. The mean presurgical BMI and the percentage of males were higher in the sleeve group. The effect of surgery on lowering of glycated hemoglobin was similar for both surgery types. After RYGB surgery, the decrease in the cholesterol concentration was larger than after SG. The enzymatic activity of aspartate aminotransferase, alanine aminotransferase, and alkaline phosphate in sleeve patients was higher presurgically but lower postsurgically compared to bypass values. Conclusions: Beanplots allow intuitive visualization of population distributions. Analysis of this large population-based data set using beanplots suggests comparable efficacies of both types of surgery in reducing diabetes. RYGB surgery reduced dyslipidemia more effectively than SG. The trend toward a larger decrease in liver enzyme activities following SG is a subject for further investigation.

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Sequoia: an interactive visual analytics platform for interpretation and feature extraction from nanopore sequencing datasets

BMC Genomics ◽

10.1186/s12864-021-07791-z ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Ratanond Koonchanok ◽

Swapna Vidhur Daulatabad ◽

Quoseena Mir ◽

Khairi Reda ◽

Sarath Chandra Janga

Keyword(s):

Single Molecule ◽

Visual Analytics ◽

Visual Analysis ◽

Direct Sequencing ◽

Visual Exploration ◽

Nanopore Sequencing ◽

Sequencing Data ◽

Rna Sequences ◽

Sequencing Technologies ◽

Signal Features

Abstract Background Direct-sequencing technologies, such as Oxford Nanopore’s, are delivering long RNA reads with great efficacy and convenience. These technologies afford an ability to detect post-transcriptional modifications at a single-molecule resolution, promising new insights into the functional roles of RNA. However, realizing this potential requires new tools to analyze and explore this type of data. Result Here, we present Sequoia, a visual analytics tool that allows users to interactively explore nanopore sequences. Sequoia combines a Python-based backend with a multi-view visualization interface, enabling users to import raw nanopore sequencing data in a Fast5 format, cluster sequences based on electric-current similarities, and drill-down onto signals to identify properties of interest. We demonstrate the application of Sequoia by generating and analyzing ~ 500k reads from direct RNA sequencing data of human HeLa cell line. We focus on comparing signal features from m6A and m5C RNA modifications as the first step towards building automated classifiers. We show how, through iterative visual exploration and tuning of dimensionality reduction parameters, we can separate modified RNA sequences from their unmodified counterparts. We also document new, qualitative signal signatures that characterize these modifications from otherwise normal RNA bases, which we were able to discover from the visualization. Conclusions Sequoia’s interactive features complement existing computational approaches in nanopore-based RNA workflows. The insights gleaned through visual analysis should help users in developing rationales, hypotheses, and insights into the dynamic nature of RNA. Sequoia is available at https://github.com/dnonatar/Sequoia.

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Fraud Audit Based on Visual Analysis: A Process Mining Approach

Applied Sciences ◽

10.3390/app11114751 ◽

2021 ◽

Vol 11 (11) ◽

pp. 4751

Author(s):

Jorge-Félix Rodríguez-Quintero ◽

Alexander Sánchez-Díaz ◽

Leonel Iriarte-Navarro ◽

Alejandro Maté ◽

Manuel Marco-Such ◽

...

Keyword(s):

Visual Analytics ◽

Visual Analysis ◽

Process Mining ◽

Fraud Detection ◽

Financial Sector ◽

Contextual Analysis ◽

Use Case ◽

Hotel Management ◽

Domain Iii ◽

Audit Approach

Among the knowledge areas in which process mining has had an impact, the audit domain is particularly striking. Traditionally, audits seek evidence in a data sample that allows making inferences about a population. Mistakes are usually committed when generalizing the results and anomalies; therefore, they appear in unprocessed sets; however, there are some efforts to address these limitations using process-mining-based approaches for fraud detection. To the best of our knowledge, no fraud audit method exists that combines process mining techniques and visual analytics to identify relevant patterns. This paper presents a fraud audit approach based on the combination of process mining techniques and visual analytics. The main advantages are: (i) a method is included that guides the use of the visual capabilities of process mining to detect fraud data patterns during an audit; (ii) the approach can be generalized to any business domain; (iii) well-known process mining techniques are used (dotted chart, trace alignment, fuzzy miner…). The techniques were selected by a group of experts and were extended to enable filtering for contextual analysis, to handle levels of process abstraction, and to facilitate implementation in the area of fraud audits. Based on the proposed approach, we developed a software solution that is currently being used in the financial sector as well as in the telecommunications and hospitality sectors. Finally, for demonstration purposes, we present a real hotel management use case in which we detected suspected fraud behaviors, thus validating the effectiveness of the approach.

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Implementing Visual Analytics Pipelines with Simulation Data

10.5772/intechopen.96152 ◽

2021 ◽

Author(s):

Taimur Khan ◽

Syed Samad Shakeel ◽

Afzal Gul ◽

Hamza Masud ◽

Achim Ebert

Keyword(s):

Visual Analytics ◽

Visual Analysis ◽

Simulated Data ◽

Evaluation Study ◽

Ease Of Use ◽

Preliminary Evaluation ◽

Simulation Data ◽

Visual Data Analytics ◽

High Level ◽

Simulation Parameters

Visual analytics has been widely studied in the past decade both in academia and industry to improve data exploration, minimize the overall cost, and improve data analysis. In this chapter, we explore the idea of visual analytics in the context of simulation data. This would then provide us with the capability to not only explore our data visually but also to apply machine learning models in order to answer high-level questions with respect to scheduling, choosing optimal simulation parameters, finding correlations, etc. More specifically, we examine state-of-the-art tools to be able to perform these above-mentioned tasks. Further, to test and validate our methodology we followed the human-centered design process to build a prototype tool called ViDAS (Visual Data Analytics of Simulated Data). Our preliminary evaluation study illustrates the intuitiveness and ease-of-use of our approach with regards to visual analysis of simulated data.

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VOLARE: Visual analysis of disease-associated microbiome-immune system interplay

10.1101/431379 ◽

2018 ◽

Author(s):

Janet C. Siebert ◽

Charles Preston Neff ◽

Jennifer M. Schneider ◽

EmiLie H. Regner ◽

Neha Ohri ◽

...

Keyword(s):

Immune System ◽

Web Application ◽

Visual Analysis ◽

System Development ◽

Patient Specific ◽

Domain Experts ◽

Immune System Development ◽

Interactive Environment ◽

System Logs ◽

Linear Regressions

AbstractBackgroundRelationships between specific microbes and proper immune system development, composition, and function have been reported in a number of studies. However, researchers have discovered only a fraction of the likely relationships. High-dimensional “omic” methodologies such as 16S ribosomal RNA (rRNA) sequencing and Time-of-flight mass cytometry (CyTOF) immunophenotyping generate data that support generation of hypotheses, with the potential to identify additional relationships at a level of granularity ripe for further experimentation. Pairwise linear regressions between microbial and host immune features is one approach for quantifying relationships between “omes”, and the differences in these relationships across study cohorts or arms. This approach yields a top table of candidate results. However, the top table alone lacks the detail that domain experts need to vet candidate results for follow-up experiments.ResultsTo support this vetting, we developed VOLARE (Visualization Of LineAr Regression Elements), a web application that integrates a searchable top table, small in-line graphs illustrating the fitted models, a network summarizing the top table, and on-demand detailed regression plots showing full sample-level detail. We applied VOLARE to three case studies—microbiome:cytokine data from fecal samples in HIV, microbiome:cytokine data in inflammatory bowel disease and spondyloarthritis, and microbiome:immune cell data from gut biopsies in HIV. We present both patient-specific phenomena and relationships that differ by disease state. We also analyzed interaction data from system logs to characterize usage scenarios. This log analysis revealed that, in using VOLARE, domain experts frequently generated detailed regression plots, suggesting that this detail aids the vetting of results.ConclusionsSystematically integrating microbe:immune cell readouts through pairwise linear regressions and presenting the top table in an interactive environment supports the vetting of results for scientific relevance. VOLARE allows domain experts to control the analysis of their results, screening dozens of candidate relationships with ease. This interactive environment transcends the limitations of a static top table.

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Dementia Patient Segmentation Using EMR Data Visualization: A Design Study

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16183438 ◽

2019 ◽

Vol 16 (18) ◽

pp. 3438 ◽

Cited By ~ 2

Author(s):

Hyoji Ha ◽

Jihye Lee ◽

Hyunwoo Han ◽

Sungyun Bae ◽

Sangjoon Son ◽

...

Keyword(s):

Medical Record ◽

Visual Analysis ◽

Qualitative Evaluation ◽

Record Management ◽

Radial Coordinate ◽

Dementia Patient ◽

Biomedical Data ◽

Record System ◽

Efficient Treatment ◽

Domain Experts

(1) Background: The Electronic Medical Record system, which is a digital medical record management architecture, is critical for reliable medical research. It facilitates the investigation of disease patterns and efficient treatment via collaboration with data scientists. (2) Methods: In this study, we present multidimensional visual tools for the analysis of multidimensional datasets via a combination of 3-dimensional radial coordinate visualization (3D RadVis) and many-objective optimization (e.g., Parallel Coordinates). Also, we propose a user-driven research design to facilitate visualization. We followed a design process to (1) understand the demands of domain experts, (2) define the problems based on relevant works, (3) design visualization, (4) implement visualization, and (5) enable qualitative evaluation by domain experts. (3) Results: This study provides clinical insight into dementia based on EMR data via visual analysis. Results of a case study based on questionnaires surveying daily living activities indicated that daily behaviors influenced the progression of dementia. (4) Conclusions: This study provides a visual analytical tool to support cluster segmentation. Using this tool, we segmented dementia patients into clusters and interpreted the behavioral patterns of each group. This study contributes to biomedical data interpretation based on a visual approach.

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