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van den Brandt A, Jonkheer EM, van Workum DJM, van de Wetering H, Smit S, Vilanova A. PanVA: Pangenomic Variant Analysis. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2024; 30:4895-4909. [PMID: 37267130 DOI: 10.1109/tvcg.2023.3282364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Genomics researchers increasingly use multiple reference genomes to comprehensively explore genetic variants underlying differences in detectable characteristics between organisms. Pangenomes allow for an efficient data representation of multiple related genomes and their associated metadata. However, current visual analysis approaches for exploring these complex genotype-phenotype relationships are often based on single reference approaches or lack adequate support for interpreting the variants in the genomic context with heterogeneous (meta)data. This design study introduces PanVA, a visual analytics design for pangenomic variant analysis developed with the active participation of genomics researchers. The design uniquely combines tailored visual representations with interactions such as sorting, grouping, and aggregation, allowing users to navigate and explore different perspectives on complex genotype-phenotype relations. Through evaluation in the context of plants and pathogen research, we show that PanVA helps researchers explore variants in genes and generate hypotheses about their role in phenotypic variation.
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Li Z, Liu X, Tang Z, Jin N, Zhang P, Eadon MT, Song Q, Chen YV, Su J. TrajVis: a visual clinical decision support system to translate artificial intelligence trajectory models in the precision management of chronic kidney disease. J Am Med Inform Assoc 2024:ocae158. [PMID: 38916922 DOI: 10.1093/jamia/ocae158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 05/31/2024] [Accepted: 06/10/2024] [Indexed: 06/26/2024] Open
Abstract
OBJECTIVE Our objective is to develop and validate TrajVis, an interactive tool that assists clinicians in using artificial intelligence (AI) models to leverage patients' longitudinal electronic medical records (EMRs) for personalized precision management of chronic disease progression. MATERIALS AND METHODS We first perform requirement analysis with clinicians and data scientists to determine the visual analytics tasks of the TrajVis system as well as its design and functionalities. A graph AI model for chronic kidney disease (CKD) trajectory inference named DisEase PrOgression Trajectory (DEPOT) is used for system development and demonstration. TrajVis is implemented as a full-stack web application with synthetic EMR data derived from the Atrium Health Wake Forest Baptist Translational Data Warehouse and the Indiana Network for Patient Care research database. A case study with a nephrologist and a user experience survey of clinicians and data scientists are conducted to evaluate the TrajVis system. RESULTS The TrajVis clinical information system is composed of 4 panels: the Patient View for demographic and clinical information, the Trajectory View to visualize the DEPOT-derived CKD trajectories in latent space, the Clinical Indicator View to elucidate longitudinal patterns of clinical features and interpret DEPOT predictions, and the Analysis View to demonstrate personal CKD progression trajectories. System evaluations suggest that TrajVis supports clinicians in summarizing clinical data, identifying individualized risk predictors, and visualizing patient disease progression trajectories, overcoming the barriers of AI implementation in healthcare. DISCUSSION The TrajVis system provides a novel visualization solution which is complimentary to other risk estimators such as the Kidney Failure Risk Equations. CONCLUSION TrajVis bridges the gap between the fast-growing AI/ML modeling and the clinical use of such models for personalized and precision management of chronic diseases.
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Affiliation(s)
- Zuotian Li
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, United States
- Department of Computer Graphics Technology, Purdue University, West Lafayette, IN 47907, United States
| | - Xiang Liu
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, United States
| | - Ziyang Tang
- Department of Computer and Information Technology, Purdue University, West Lafayette, IN 47907, United States
| | - Nanxin Jin
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, United States
- Department of Computer and Information Technology, Purdue University, West Lafayette, IN 47907, United States
| | - Pengyue Zhang
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, United States
| | - Michael T Eadon
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN 46202, United States
| | - Qianqian Song
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL 32610, United States
| | - Yingjie V Chen
- Department of Computer Graphics Technology, Purdue University, West Lafayette, IN 47907, United States
| | - Jing Su
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, United States
- Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, NC 27101, United States
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Wu X, Chen C, Quan L. Visual analysis and interactive interface design of students' abnormal behavior introducing clustering algorithm. Technol Health Care 2024:THC232024. [PMID: 38875056 DOI: 10.3233/thc-232054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2024]
Abstract
BACKGROUND Traditional methods have the limitations of low accuracy and inconvenient operation in analyzing students' abnormal behavior. Hence, a more intuitive, flexible, and user-friendly visualization tool is needed to help better understand students' behavior data. OBJECTIVE In this study a visual analysis and interactive interface of students' abnormal behavior based on a clustering algorithm were examined and designed. METHODS Firstly, this paper discusses the development of traditional methods for analyzing students' abnormal behavior and visualization technology and discusses its limitations. Then, the K-means clustering algorithm is selected as the solution to find potential abnormal patterns and groups from students' behaviors. By collecting a large number of students' behavior data and preprocessing them to extract relevant features, a K-means clustering algorithm is applied to cluster the data and obtain the clustering results of students' abnormal behaviors. To visually display the clustering results and help users analyze students' abnormal behaviors, a visual analysis method and an interactive interface are designed to present the clustering results to users. The interactive functions are provided, such as screening, zooming in and out, and correlation analysis, to support users' in-depth exploration and analysis of data. Finally, the experimental evaluation is carried out, and the effectiveness and practicability of the proposed method are verified by using big data to obtain real student behavior data. RESULTS The experimental results show that this method can accurately detect and visualize students' abnormal behaviors and provide intuitive analysis results. CONCLUSION This paper makes full use of the advantages of big data to understand students' behavior patterns more comprehensively and provides a new solution for students' management and behavior analysis in the field of education. Future research can further expand and improve this method to adapt to more complex students' behavior data and needs.
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Park JH, Prasad V, Newsom S, Najar F, Rajan R. IdMotif: An Interactive Motif Identification in Protein Sequences. IEEE COMPUTER GRAPHICS AND APPLICATIONS 2024; 44:114-125. [PMID: 38127603 DOI: 10.1109/mcg.2023.3345742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
This article presents a visual analytics framework, idMotif, to support domain experts in identifying motifs in protein sequences. A motif is a short sequence of amino acids usually associated with distinct functions of a protein, and identifying similar motifs in protein sequences helps us to predict certain types of disease or infection. idMotif can be used to explore, analyze, and visualize such motifs in protein sequences. We introduce a deep-learning-based method for grouping protein sequences and allow users to discover motif candidates of protein groups based on local explanations of the decision of a deep-learning model. idMotif provides several interactive linked views for between and within protein cluster/group and sequence analysis. Through a case study and experts' feedback, we demonstrate how the framework helps domain experts analyze protein sequences and motif identification.
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Scimone A, Eckelt K, Streit M, Hinterreiter A. Marjorie: Visualizing Type 1 Diabetes Data to Support Pattern Exploration. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2024; 30:1216-1226. [PMID: 37874710 DOI: 10.1109/tvcg.2023.3326936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/26/2023]
Abstract
In this work we propose Marjorie, a visual analytics approach to address the challenge of analyzing patients' diabetes data during brief regular appointments with their diabetologists. Designed in consultation with diabetologists, Marjorie uses a combination of visual and algorithmic methods to support the exploration of patterns in the data. Patterns of interest include seasonal variations of the glucose profiles, and non-periodic patterns such as fluctuations around mealtimes or periods of hypoglycemia (i.e., glucose levels below the normal range). We introduce a unique representation of glucose data based on modified horizon graphs and hierarchical clustering of adjacent carbohydrate or insulin entries. Semantic zooming allows the exploration of patterns on different levels of temporal detail. We evaluated our solution in a case study, which demonstrated Marjorie's potential to provide valuable insights into therapy parameters and unfavorable eating habits, among others. The study results and informal feedback collected from target users suggest that Marjorie effectively supports patients and diabetologists in the joint exploration of patterns in diabetes data, potentially enabling more informed treatment decisions. A free copy of this paper and all supplemental materials are available at https://osf.io/34t8c/.
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WarehouseLens: visualizing and exploring turnover events of digital warehouse. J Vis (Tokyo) 2023. [DOI: 10.1007/s12650-023-00913-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/27/2023]
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Liu S, Weng D, Tian Y, Deng Z, Xu H, Zhu X, Yin H, Zhan X, Wu Y. ECoalVis: Visual Analysis of Control Strategies in Coal-fired Power Plants. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2023; 29:1091-1101. [PMID: 36191102 DOI: 10.1109/tvcg.2022.3209430] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Improving the efficiency of coal-fired power plants has numerous benefits. The control strategy is one of the major factors affecting such efficiency. However, due to the complex and dynamic environment inside the power plants, it is hard to extract and evaluate control strategies and their cascading impact across massive sensors. Existing manual and data-driven approaches cannot well support the analysis of control strategies because these approaches are time-consuming and do not scale with the complexity of the power plant systems. Three challenges were identified: a) interactive extraction of control strategies from large-scale dynamic sensor data, b) intuitive visual representation of cascading impact among the sensors in a complex power plant system, and c) time-lag-aware analysis of the impact of control strategies on electricity generation efficiency. By collaborating with energy domain experts, we addressed these challenges with ECoalVis, a novel interactive system for experts to visually analyze the control strategies of coal-fired power plants extracted from historical sensor data. The effectiveness of the proposed system is evaluated with two usage scenarios on a real-world historical dataset and received positive feedback from experts.
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Wang Q, Chen Z, Wang Y, Qu H. A Survey on ML4VIS: Applying Machine Learning Advances to Data Visualization. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2022; 28:5134-5153. [PMID: 34437063 DOI: 10.1109/tvcg.2021.3106142] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Inspired by the great success of machine learning (ML), researchers have applied ML techniques to visualizations to achieve a better design, development, and evaluation of visualizations. This branch of studies, known as ML4VIS, is gaining increasing research attention in recent years. To successfully adapt ML techniques for visualizations, a structured understanding of the integration of ML4VIS is needed. In this article, we systematically survey 88 ML4VIS studies, aiming to answer two motivating questions: "what visualization processes can be assisted by ML?" and "how ML techniques can be used to solve visualization problems? "This survey reveals seven main processes where the employment of ML techniques can benefit visualizations: Data Processing4VIS, Data-VIS Mapping, Insight Communication, Style Imitation, VIS Interaction, VIS Reading, and User Profiling. The seven processes are related to existing visualization theoretical models in an ML4VIS pipeline, aiming to illuminate the role of ML-assisted visualization in general visualizations. Meanwhile, the seven processes are mapped into main learning tasks in ML to align the capabilities of ML with the needs in visualization. Current practices and future opportunities of ML4VIS are discussed in the context of the ML4VIS pipeline and the ML-VIS mapping. While more studies are still needed in the area of ML4VIS, we hope this article can provide a stepping-stone for future exploration. A web-based interactive browser of this survey is available at https://ml4vis.github.io.
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Representation and analysis of time-series data via deep embedding and visual exploration. J Vis (Tokyo) 2022. [DOI: 10.1007/s12650-022-00890-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
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10
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You are experienced: interactive tour planning with crowdsourcing tour data from web. J Vis (Tokyo) 2022. [DOI: 10.1007/s12650-022-00884-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
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Scheer J, Volkert A, Brich N, Weinert L, Santhanam N, Krone M, Ganslandt T, Boeker M, Nagel T. Visualization Techniques of Time-Oriented Data for the Comparison of Single Patients With Multiple Patients or Cohorts: Scoping Review. J Med Internet Res 2022; 24:e38041. [PMID: 36279164 PMCID: PMC9641521 DOI: 10.2196/38041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 05/28/2022] [Accepted: 07/28/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Visual analysis and data delivery in the form of visualizations are of great importance in health care, as such forms of presentation can reduce errors and improve care and can also help provide new insights into long-term disease progression. Information visualization and visual analytics also address the complexity of long-term, time-oriented patient data by reducing inherent complexity and facilitating a focus on underlying and hidden patterns. OBJECTIVE This review aims to provide an overview of visualization techniques for time-oriented data in health care, supporting the comparison of patients. We systematically collected literature and report on the visualization techniques supporting the comparison of time-based data sets of single patients with those of multiple patients or their cohorts and summarized the use of these techniques. METHODS This scoping review used the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. After all collected articles were screened by 16 reviewers according to the criteria, 6 reviewers extracted the set of variables under investigation. The characteristics of these variables were based on existing taxonomies or identified through open coding. RESULTS Of the 249 screened articles, we identified 22 (8.8%) that fit all criteria and reviewed them in depth. We collected and synthesized findings from these articles for medical aspects such as medical context, medical objective, and medical data type, as well as for the core investigated aspects of visualization techniques, interaction techniques, and supported tasks. The extracted articles were published between 2003 and 2019 and were mostly situated in clinical research. These systems used a wide range of visualization techniques, most frequently showing changes over time. Timelines and temporal line charts occurred 8 times each, followed by histograms with 7 occurrences and scatterplots with 5 occurrences. We report on the findings quantitatively through visual summarization, as well as qualitatively. CONCLUSIONS The articles under review in general mitigated complexity through visualization and supported diverse medical objectives. We identified 3 distinct patient entities: single patients, multiple patients, and cohorts. Cohorts were typically visualized in condensed form, either through prior data aggregation or through visual summarization, whereas visualization of individual patients often contained finer details. All the systems provided mechanisms for viewing and comparing patient data. However, explicitly comparing a single patient with multiple patients or a cohort was supported only by a few systems. These systems mainly use basic visualization techniques, with some using novel visualizations tailored to a specific task. Overall, we found the visual comparison of measurements between single and multiple patients or cohorts to be underdeveloped, and we argue for further research in a systematic review, as well as the usefulness of a design space.
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Affiliation(s)
- Jan Scheer
- Human Data Interaction Lab, Mannheim University of Applied Sciences, Mannheim, Germany
| | - Alisa Volkert
- Center for Innovative Care, University Hospital Tübingen, Tübingen, Germany
| | - Nicolas Brich
- Big Data Visual Analytics in Life Sciences, Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany
| | - Lina Weinert
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
| | - Nandhini Santhanam
- Abteilung für Biomedizinische Informatik, Zentrum für Präventivmedizin und Digitale Gesundheit Baden-Württemberg, Medizinische Fakultät Mannheim der Universität Heidelberg, Mannheim, Germany
| | - Michael Krone
- Big Data Visual Analytics in Life Sciences, Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany
| | - Thomas Ganslandt
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Martin Boeker
- University Hospital Rechts der Isar, School of Medicine, Technical University of Munich, München, Germany
| | - Till Nagel
- Human Data Interaction Lab, Mannheim University of Applied Sciences, Mannheim, Germany
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Deng Z, Weng D, Liu S, Tian Y, Xu M, Wu Y. A survey of urban visual analytics: Advances and future directions. COMPUTATIONAL VISUAL MEDIA 2022; 9:3-39. [PMID: 36277276 PMCID: PMC9579670 DOI: 10.1007/s41095-022-0275-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 02/08/2022] [Indexed: 06/16/2023]
Abstract
Developing effective visual analytics systems demands care in characterization of domain problems and integration of visualization techniques and computational models. Urban visual analytics has already achieved remarkable success in tackling urban problems and providing fundamental services for smart cities. To promote further academic research and assist the development of industrial urban analytics systems, we comprehensively review urban visual analytics studies from four perspectives. In particular, we identify 8 urban domains and 22 types of popular visualization, analyze 7 types of computational method, and categorize existing systems into 4 types based on their integration of visualization techniques and computational models. We conclude with potential research directions and opportunities.
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Affiliation(s)
- Zikun Deng
- State Key Lab of CAD & CG, Zhejiang University, Hangzhou, 310058 China
| | - Di Weng
- Microsoft Research Asia, Beijing, 100080 China
| | - Shuhan Liu
- State Key Lab of CAD & CG, Zhejiang University, Hangzhou, 310058 China
| | - Yuan Tian
- State Key Lab of CAD & CG, Zhejiang University, Hangzhou, 310058 China
| | - Mingliang Xu
- School of Information Engineering, Zhengzhou University, Zhengzhou, China
- Henan Institute of Advanced Technology, Zhengzhou University, Zhengzhou, 450001 China
| | - Yingcai Wu
- State Key Lab of CAD & CG, Zhejiang University, Hangzhou, 310058 China
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Nakikj D, Kreda D, Gehlenborg N. New Ways for Patients to Make Sense of Their EHR Data Using the Discovery Web-application: A Think-aloud Evaluation Study (Preprint). JMIR Form Res 2022; 7:e41346. [PMID: 37010887 PMCID: PMC10131650 DOI: 10.2196/41346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 12/21/2022] [Accepted: 12/27/2022] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND In the United States, patients can access their electronic health record (EHR) data through patient portals. However, current patient portals are mainly focused on a single provider, with very limited data sharing capabilities and put low emphasis on independent sensemaking of the EHR data. This makes it very challenging for patients to switch between different portals and aggregate the data to obtain a complete picture of their medical history and to make sense of it. Owing to this fragmentation, patients are exposed to numerous inconveniences such as medical errors, repeated tests, and limited self-advocacy. OBJECTIVE To overcome the limitations of EHR patient portals, we designed and developed Discovery-a web-based application that aggregates EHR data from multiple providers and present them to the patient for efficient exploration and sensemaking. To learn how well Discovery meets the patients' sensemaking needs and what features should such applications include, we conducted an evaluation study. METHODS We conducted a remote study with 14 participants. In a 60-minute session and relying on the think-aloud protocol, participants were asked to complete a variety of sensemaking tasks and provide feedback upon completion. The audio materials were transcribed for analysis and the video recordings of the users' interactions with Discovery were annotated to provide additional context. These combined textual data were thematically analyzed to surface themes that reflect how participants used Discovery's features, what sensemaking of their EHR data really entails, and what features are desirable to support that process better. RESULTS We found that Discovery provided much needed features and could be used in a variety of everyday scenarios, especially for preparing and during clinical visits and also for raising awareness, reflection, and planning. According to the study participants, Discovery provided a robust set of features for supporting independent exploration and sensemaking of their EHR data: summary and quick overview of the data, finding prevalence, periodicity, co-occurrence, and pre-post of medical events, as well as comparing medical record types and subtypes across providers. In addition, we extracted important design implications from the user feedback on data exploration with multiple views and nonstandard user interface elements. CONCLUSIONS Patient-centered sensemaking tools should have a core set of features that can be learned quickly and support common use cases for a variety of users. The patients should be able to detect time-oriented patterns of medical events and get enough context and explanation on demand in a single exploration view that feels warm and familiar and relies on patient-friendly language. However, this view should have enough plasticity to adjust to the patient's information needs as the sensemaking unfolds. Future designs should include the physicians in the patient's sensemaking process and improve the communication in clinical visits and via messaging.
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Affiliation(s)
- Drashko Nakikj
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - David Kreda
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Nils Gehlenborg
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
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Musleh M, Chatzimparmpas A, Jusufi I. Visual analysis of blow molding machine multivariate time series data. J Vis (Tokyo) 2022; 25:1329-1342. [PMID: 35845181 PMCID: PMC9273703 DOI: 10.1007/s12650-022-00857-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 04/25/2022] [Accepted: 06/01/2022] [Indexed: 12/02/2022]
Abstract
Abstract The recent development in the data analytics field provides a boost in production for modern industries. Small-sized factories intend to take full advantage of the data collected by sensors used in their machinery. The ultimate goal is to minimize cost and maximize quality, resulting in an increase in profit. In collaboration with domain experts, we implemented a data visualization tool to enable decision-makers in a plastic factory to improve their production process. The tool is an interactive dashboard with multiple coordinated views supporting the exploration from both local and global perspectives. In summary, we investigate three different aspects: methods for preprocessing multivariate time series data, clustering approaches for the already refined data, and visualization techniques that aid domain experts in gaining insights into the different stages of the production process. Here we present our ongoing results grounded in a human-centered development process. We adopt a formative evaluation approach to continuously upgrade our dashboard design that eventually meets partners' requirements and follows the best practices within the field. We also conducted a case study with a domain expert to validate the potential application of the tool in the real-life context. Finally, we assessed the usability and usefulness of the tool with a two-layer summative evaluation that showed encouraging results. Graphical Abstract
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Affiliation(s)
- Maath Musleh
- Institute of Visual Computing and Human-Centered Technology, TU Wien, 1040 Vienna, Austria
| | - Angelos Chatzimparmpas
- Department of Computer Science and Media Technology, Linnaeus University, Växjö, 351 95 Sweden
| | - Ilir Jusufi
- Department of Computer Science and Media Technology, Linnaeus University, Växjö, 351 95 Sweden
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Mining sequential patterns with flexible constraints from MOOC data. APPL INTELL 2022; 52:16458-16474. [PMID: 35340983 PMCID: PMC8940599 DOI: 10.1007/s10489-021-03122-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/16/2021] [Indexed: 11/29/2022]
Abstract
Online learning is playing an increasingly important role in education. Massive open online course (MOOC) platforms are among the most important tools in online learning, and record historical learning data from an extremely large number of learners. To enhance the learning experience, a promising approach is to apply sequential pattern mining (SPM) to discover useful knowledge in these data. In this paper, mining sequential patterns (SPs) with flexible constraints in MOOC enrollment data is proposed, which follows that research approach. Three constraints are proposed: the length constraint, discreteness constraint, and validity constraint. They are used to describe the effect of the length of enrollment sequences, variance of enrollment dates, and enrollment moments, respectively. To improve the mining efficiency, the three constraints are pushed into the support, which is the most typical parameter in SPM, to form a new parameter called support with flexible constraints (SFC). SFC is proved to satisfy the downward closure property, and two algorithms are proposed to discover SPs with flexible constraints. They traverse the search space in a breadth-first and depth-first manner. The experimental results demonstrate that the proposed algorithms effectively reduce the number of patterns, with comparable performance to classical SPM algorithms.
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Exploring Life in Concentration Camps through a Visual Analysis of Prisoners’ Diaries. INFORMATION 2022. [DOI: 10.3390/info13020054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Diaries are private documentations of people’s lives. They contain descriptions of events, thoughts, fears, and desires. While diaries are usually kept in private, published ones, such as the diary of Anne Frank, show that they bear the potential to give personal insight into events and into the emotional impact on their authors. We present a visualization tool that provides insight into the Bergen-Belsen memorial’s diary corpus, which consists of dozens of diaries written by concentration camp prisoners. We designed a calendar view that documents when authors wrote about concentration camp life. Different modes support quantitative and sentiment analyses, and we provide a solution for historians to create thematic concepts that can be used for searching and filtering for specific diary entries. The usage scenarios illustrate the importance of the tool for researchers and memorial visitors as well as for commemorating the Holocaust.
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Using Visual Analytics to Optimize Blood Product Inventory at a Hospital’s Blood Transfusion Service. Artif Intell Med 2022. [DOI: 10.1007/978-3-031-09342-5_46] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Wang Q, Mazor T, Harbig TA, Cerami E, Gehlenborg N. ThreadStates: State-based Visual Analysis of Disease Progression. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2022; 28:238-247. [PMID: 34587068 DOI: 10.1109/tvcg.2021.3114840] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
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, these observations are usually treated as general events in existing visual analysis tools. As a result, their capabilities in modeling disease progression are not fully utilized. 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 Glyph Matrix 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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Tang J, Zhou Y, Tang T, Weng D, Xie B, Yu L, Zhang H, Wu Y. A Visualization Approach for Monitoring Order Processing in E-Commerce Warehouse. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2022; 28:857-867. [PMID: 34596553 DOI: 10.1109/tvcg.2021.3114878] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The efficiency of warehouses is vital to e-commerce. Fast order processing at the warehouses ensures timely deliveries and improves customer satisfaction. However, monitoring, analyzing, and manipulating order processing in the warehouses in real time are challenging for traditional methods due to the sheer volume of incoming orders, the fuzzy definition of delayed order patterns, and the complex decision-making of order handling priorities. In this paper, we adopt a data-driven approach and propose OrderMonitor, a visual analytics system that assists warehouse managers in analyzing and improving order processing efficiency in real time based on streaming warehouse event data. Specifically, the order processing pipeline is visualized with a novel pipeline design based on the sedimentation metaphor to facilitate real-time order monitoring and suggest potentially abnormal orders. We also design a novel visualization that depicts order timelines based on the Gantt charts and Marey's graphs. Such a visualization helps the managers gain insights into the performance of order processing and find major blockers for delayed orders. Furthermore, an evaluating view is provided to assist users in inspecting order details and assigning priorities to improve the processing performance. The effectiveness of OrderMonitor is evaluated with two case studies on a real-world warehouse dataset.
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