PhenoFlow: A Human-LLM Driven Visual Analytics System for Exploring Large and Complex Stroke Datasets.

Acute stroke demands prompt diagnosis and treatment to achieve optimal patient outcomes. However, the intricate and irregular nature of clinical data associated with acute stroke, particularly blood pressure (BP) measurements, presents substantial obstacles to effective visual analytics and decision-making. Through a year-long collaboration with experienced neurologists, we developed PhenoFlow, a visual analytics system that leverages the collaboration between human and Large Language Models (LLMs) to analyze the extensive and complex data of acute ischemic stroke patients.

Pregnancy outcomes of cerclage in twin gestations: a multicenter retrospective cohort study.

Objectives: To determine the effects of cerclage on twin pregnancies.

Methods: A multicenter, retrospective, cohort study was conducted at 10 tertiary centers using a web-based data collection platform. The study population included twin pregnancies delivered after 20 weeks of gestation.

Enhancing Data Literacy On-demand: LLMs as Guides for Novices in Chart Interpretation.

With the growing complexity and volume of data, visualizations have become more intricate, often requiring advanced techniques to convey insights. These complex charts are prevalent in everyday life, and individuals who lack knowledge in data visualization may find them challenging to understand. This paper investigates using Large Language Models (LLMs) to help users with low data literacy understand complex visualizations.

MoNetExplorer: A Visual Analytics System for Analyzing Dynamic Networks With Temporal Network Motifs.

Partitioning a dynamic network into subsets (i.e., snapshots) based on disjoint time intervals is a widely used technique for understanding how structural patterns of the network evolve.

Classes are not Clusters: Improving Label-based Evaluation of Dimensionality Reduction.

A common way to evaluate the reliability of dimensionality reduction (DR) embeddings is to quantify how well labeled classes form compact, mutually separated clusters in the embeddings. This approach is based on the assumption that the classes stay as clear clusters in the original high-dimensional space. However, in reality, this assumption can be violated; a single class can be fragmented into multiple separated clusters, and multiple classes can be merged into a single cluster.

CLAMS: A Cluster Ambiguity Measure for Estimating Perceptual Variability in Visual Clustering.

Visual clustering is a common perceptual task in scatterplots that supports diverse analytics tasks (e.g., cluster identification).

An Artificial Intelligence Exercise Coaching Mobile App: Development and Randomized Controlled Trial to Verify Its Effectiveness in Posture Correction.

Background: Insufficient physical activity due to social distancing and suppressed outdoor activities increases vulnerability to diseases like cardiovascular diseases, sarcopenia, and severe COVID-19. While bodyweight exercises, such as squats, effectively boost physical activity, incorrect postures risk abnormal muscle activation joint strain, leading to ineffective sessions or even injuries. Avoiding incorrect postures is challenging for novices without expert guidance.

Effects of Fetal Images Produced in Virtual Reality on Maternal-Fetal Attachment: Randomized Controlled Trial.

Background: Maternal-fetal attachment (MFA) has been reported to be associated with the postpartum mother-infant relationship. Seeing the fetus through ultrasound might influence MFA, and the effect could be increased by more realistic images, such as those generated in virtual reality (VR).

Objective: The aim was to determine the effect of fetal images generated in VR on MFA and depressive symptoms through a prenatal-coaching mobile app.

Efficacy improvement in searching MEDLINE database using a novel PubMed visual analytic system: EEEvis.

PubMed is the most extensively used database and search engine in the biomedical and healthcare fields. However, users could experience several difficulties in acquiring their target papers facing massive numbers of search results, especially in their unfamiliar fields. Therefore, we developed a novel user interface for PubMed and conducted three steps of study: step A, a preliminary user survey with 76 medical experts regarding the current usability for the biomedical literature search task at PubMed; step B is implementing EEEvis, a novel interactive visual analytic system for the search task; step C, a randomized user study comparing PubMed and EEEvis.

RCMVis: A Visual Analytics System for Route Choice Modeling.

We present RCMVis, a visual analytics system to support interactive Route Choice Modeling analysis. It aims to model which characteristics of routes, such as distance and the number of traffic lights, affect travelers' route choice behaviors and how much they affect the choice during their trips. Through close collaboration with domain experts, we designed a visual analytics framework for Route Choice Modeling.

Measuring and Explaining the Inter-Cluster Reliability of Multidimensional Projections.

We propose Steadiness and Cohesiveness, two novel metrics to measure the inter-cluster reliability of multidimensional projection (MDP), specifically how well the inter-cluster structures are preserved between the original high-dimensional space and the low-dimensional projection space. Measuring inter-cluster reliability is crucial as it directly affects how well inter-cluster tasks (e.g.

Augmenting Parallel Coordinates Plots With Color-Coded Stacked Histograms.

We introduce Parallel Histogram Plot (PHP), a technique that overcomes the innate limitations of parallel coordinates plot (PCP) by attaching stacked-bar histograms with discrete color schemes to PCP. The color-coded histograms enable users to see an overview of the whole data without cluttering or scalability issues. Each rectangle in the PHP histograms is color coded according to the data ranking by a selected attribute.

Comparative Layouts Revisited: Design Space, Guidelines, and Future Directions.

We present a systematic review on three comparative layouts-juxtaposition, superposition, and explicit-encoding-which are information visualization (InfoVis) layouts designed to support comparison tasks. For the last decade, these layouts have served as fundamental idioms in designing many visualization systems. However, we found that the layouts have been used with inconsistent terms and confusion, and the lessons from previous studies are fragmented.

Githru: Visual Analytics for Understanding Software Development History Through Git Metadata Analysis.

Git metadata contains rich information for developers to understand the overall context of a large software development project. Thus it can help new developers, managers, and testers understand the history of development without needing to dig into a large pile of unfamiliar source code. However, the current tools for Git visualization are not adequate to analyze and explore the metadata: They focus mainly on improving the usability of Git commands instead of on helping users understand the development history.

ProReveal: Progressive Visual Analytics With Safeguards.

We present a new visual exploration concept-Progressive Visual Analytics with Safeguards-that helps people manage the uncertainty arising from progressive data exploration. Despite its potential benefits, intermediate knowledge from progressive analytics can be incorrect due to various machine and human factors, such as a sampling bias or misinterpretation of uncertainty. To alleviate this problem, we introduce PVA-Guards, safeguards people can leave on uncertain intermediate knowledge that needs to be verified, and derive seven PVA-Guards based on previous visualization task taxonomies.

PANENE: A Progressive Algorithm for Indexing and Querying Approximate k-Nearest Neighbors.

We present PANENE, a progressive algorithm for approximate nearest neighbor indexing and querying. Although the use of k-nearest neighbor (KNN) libraries is common in many data analysis methods, most KNN algorithms can only be queried when the whole dataset has been indexed, i.e.

A Liquid Chromatography-Quadrupole-Time-of-Flight Mass Spectrometric Assay for the Quantification of Fabry Disease Biomarker Globotriaosylceramide (GB3) in Fabry Model Mouse.

Fabry disease is a rare lysosomal storage disorder resulting from the lack of gene activity. Globotriaosylceramide (GB3, ceramide trihexoside) is a novel endogenous biomarker which predicts the incidence of Fabry disease. At the early stage efficacy/biomarker study, a rapid method to determine this biomarker in plasma and in all relevant tissues related to this disease simultaneously is required.

miRTarVis+: Web-based interactive visual analytics tool for microRNA target predictions.

In this paper, we present miRTarVis+, a Web-based interactive visual analytics tool for miRNA target predictions and integrative analyses of multiple prediction results. Various microRNA (miRNA) target prediction algorithms have been developed to improve sequence-based miRNA target prediction by exploiting miRNA-mRNA expression profile data. There are also a few analytics tools to help researchers predict targets of miRNAs.

GazeDx: Interactive Visual Analytics Framework for Comparative Gaze Analysis with Volumetric Medical Images.

We present an interactive visual analytics framework, GazeDx (abbr. of GazeDiagnosis), for the comparative analysis of gaze data from multiple readers examining volumetric images while integrating important contextual information with the gaze data. Gaze pattern comparison is essential to understanding how radiologists examine medical images, and to identifying factors influencing the examination.

Comparative study of idursulfase beta and idursulfase in vitro and in vivo.

Hunter syndrome is an X-linked lysosomal storage disease caused by a deficiency in the enzyme iduronate-2-sulfatase (IDS), leading to the accumulation of glycosaminoglycans (GAGs). Two recombinant enzymes, idursulfase and idursulfase beta are currently available for enzyme replacement therapy for Hunter syndrome. These two enzymes exhibited some differences in various clinical parameters in a recent clinical trial.

WordlePlus: Expanding Wordle's Use through Natural Interaction and Animation.

Wordle has been commonly used to summarize texts, with each word size-coded by its frequency of occurrences--the more often a word occurs in texts, the bigger it is. The interactive authoring tool WordlePlus leverages natural interaction and animation to give users more control over wordle development. WordlePlus supports direct manipulation of words with pen and touch interaction.

miRTarVis: an interactive visual analysis tool for microRNA-mRNA expression profile data.

Background: MicroRNAs (miRNA) are short nucleotides that down-regulate its target genes. Various miRNA target prediction algorithms have used sequence complementarity between miRNA and its targets. Recently, other algorithms tried to improve sequence-based miRNA target prediction by exploiting miRNA-mRNA expression profile data.

LiveGantt: Interactively Visualizing a Large Manufacturing Schedule.

In this paper, we introduce LiveGantt as a novel interactive schedule visualization tool that helps users explore highly-concurrent large schedules from various perspectives. Although a Gantt chart is the most common approach to illustrate schedules, currently available Gantt chart visualization tools suffer from limited scalability and lack of interactions. LiveGantt is built with newly designed algorithms and interactions to improve conventional charts with better scalability, explorability, and reschedulability.

XCluSim: a visual analytics tool for interactively comparing multiple clustering results of bioinformatics data.

Background: Though cluster analysis has become a routine analytic task for bioinformatics research, it is still arduous for researchers to assess the quality of a clustering result. To select the best clustering method and its parameters for a dataset, researchers have to run multiple clustering algorithms and compare them. However, such a comparison task with multiple clustering results is cognitively demanding and laborious.

A Novel Computerized Clinical Decision Support System for Treating Thrombolysis in Patients with Acute Ischemic Stroke.

Background And Purpose: Thrombolysis is underused in acute ischemic stroke, mainly due to the reluctance of physicians to treat thrombolysis patients. However, a computerized clinical decision support system can help physicians to develop individualized stroke treatments.

Methods: A consecutive series of 958 patients, hospitalized within 12 hours of ischemic stroke onset from a representative clinical center in Korea, was used to establish a prognostic model.