- Browse by Subject
Browsing by Subject "Data visualization"
Now showing 1 - 10 of 15
Results Per Page
Sort Options
Item Big Data Edge on Consumer Devices for Precision Medicine(IEEE, 2022) Stauffer, Jake; Zhang, Qingxue; Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and EngineeringConsumer electronics like smartphones and wearable computers are furthering precision medicine significantly, through capturing/leveraging big data on the edge towards real-time, interactive healthcare applications. Here we propose a big data edge platform that can, not only capture/manage different biomedical dynamics, but also enable real-time visualization of big data. The big data can also be uploaded to cloud for long-term management. The system has been evaluated on the real-world biomechanical data-based application, and demonstrated its effectiveness on big data management and interactive visualization. This study is expected to greatly advance big data-driven precision medicine applications.Item CalTrig: A GUI-Based Machine Learning Approach for Decoding Neuronal Calcium Transients in Freely Moving Rodents(bioRxiv, 2024-10-01) Lange, Michal A.; Chen, Yingying; Fu, Haoying; Korada, Amith; Guo, Changyong; Ma, Yao-Ying; Pharmacology and Toxicology, School of MedicineAdvances in in vivo Ca2+ imaging using miniatured microscopes have enabled researchers to study single-neuron activity in freely moving animals. Tools such as MiniAN and CalmAn have been developed to convert Ca2+ visual signals to numerical information, collectively referred to as CalV2N. However, substantial challenges remain in analyzing the large datasets generated by CalV2N, particularly in integrating data streams, evaluating CalV2N output quality, and reliably and efficiently identifying Ca2+ transients. In this study, we introduce CalTrig, an open-source graphical user interface (GUI) tool designed to address these challenges at the post-CalV2N stage of data processing. CalTrig integrates multiple data streams, including Ca2+ imaging, neuronal footprints, Ca2+ traces, and behavioral tracking, and offers capabilities for evaluating the quality of CalV2N outputs. It enables synchronized visualization and efficient Ca2+ transient identification. We evaluated four machine learning models (i.e., GRU, LSTM, Transformer, and Local Transformer) for Ca2+ transient detection. Our results indicate that the GRU model offers the highest predictability and computational efficiency, achieving stable performance across training sessions, different animals and even among different brain regions. The integration of manual, parameter-based, and machine learning-based detection methods in CalTrig provides flexibility and accuracy for various research applications. The user-friendly interface and low computing demands of CalTrig make it accessible to neuroscientists without programming expertise. We further conclude that CalTrig enables deeper exploration of brain function, supports hypothesis generation about neuronal mechanisms, and opens new avenues for understanding neurological disorders and developing treatments.Item Concept-Driven Visual Analytics: an Exploratory Study of Model- and Hypothesis-Based Reasoning with Visualizations(Association for Computer Machinery, 2019) Choi, In Kwon; Childers, Taylor; Raveendranath, Nirmal Kumar; Mishra, Swati; Harris, Kyle; Reda, KhairiVisualization tools facilitate exploratory data analysis, but fall short at supporting hypothesis-based reasoning. We conducted an exploratory study to investigate how visualizations might support a concept-driven analysis style, where users can optionally share their hypotheses and conceptual models in natural language, and receive customized plots depicting the fit of their models to the data. We report on how participants leveraged these unique affordances for visual analysis. We found that a majority of participants articulated meaningful models and predictions, utilizing them as entry points to sensemaking. We contribute an abstract typology representing the types of models participants held and externalized as data expectations. Our findings suggest ways for rearchitecting visual analytics tools to better support hypothesis- and model-based reasoning, in addition to their traditional role in exploratory analysis. We discuss the design implications and reflect on the potential benefits and challenges involved.Item End-User Needs of Fragmented Databases in Higher Education Data Analysis and Decision Making(2019-05) Briggs, Amanda; Cafaro, Francesco; Dombrowski, Lynn; Reda, KhairiIn higher education, a wealth of data is available to advisors, recruiters, marketers, and program directors. However, data sources can be accessed in a variety of ways and often do not seem to represent the same data set, presenting users with the confounding notion that data sources are in conflict with one another. As users are identifying new ways of accessing and analyzing this data, they are modifying existing work practices and sometimes creating their own databases. To understand how users are navigating these databases, the researchers employed a mixed methods research design including a survey and interview to understand the needs to end users who are accessing these seemingly fragmented databases. The study resulted in a three overarching categories – access, understandability, and use – that affect work practices for end users. The researchers used these themes to develop a set of broadly applicable design recommendations as well as six sets of sketches for implementation – development of a data gateway, training, collaboration, tracking, definitions and roadblocks, and time management.Item Geo-Temporal Visualization for Tourism Data Using Color Curves(2019-05) Choi, In Kwon; Fang, Shiaofen; Xia, Yuni; Zheng, Jiang-YuFor individuals in the tourism industry and other businesses, the department of tourism in the government, or the individuals who are planning a travel, the data of tourist population movement can be a valuable resource that can uncover insights that could bring more profit and more tourists, or make the trip more enjoyable. As visualization is an effective way of conveying information with multiple dimensions, we would like to visualize the geo-temporal floating population data of tourists and residents in Jeju island in the Republic of Korea in two-dimensional space. In this study, we introduce the two methods we have implemented for visualizing the geo-temporal data using color curves as the representation of time dimension. We use the dots as the markers of floating population, and each color of dots represents the 24 hours of a day. In the first method, we plot the colored dots directly on the map, thereby coloring the area the data represents. In the second method, we plot the same dots inside a semi-transparent circle divided into arcs that represent each month of a year. The user can compare the population of tourists and residents between the different times of a day, the different months and the weather conditions to analyze the floating population in the given area.Item Graphical Perception of Continuous Quantitative Maps: the Effects of Spatial Frequency and Colormap Design(ACM, 2018) Reda, Khairi; Nalawade, Pratik; Ansah-Koi, KateContinuous 'pseudocolor' maps visualize how a quantitative attribute varies smoothly over space. These maps are widely used by experts and lay citizens alike for communicating scientific and geographical data. A critical challenge for designers of these maps is selecting a color scheme that is both effective and aesthetically pleasing. Although there exist empirically grounded guidelines for color choice in segmented maps (e.g., choropleths), continuous maps are significantly understudied, and their color-coding guidelines are largely based on expert opinion and design heuristics--many of these guidelines have yet to be verified experimentally. We conducted a series of crowdsourced experiments to investigate how the perception of continuous maps is affected by colormap characteristics and spatial frequency (a measure of data complexity). We find that spatial frequency significantly impacts the effectiveness of color encodes, but the precise effect is task-dependent. While rainbow schemes afforded the highest accuracy in quantity estimation irrespective of spatial complexity, divergent colormaps significantly outperformed other schemes in tasks requiring the perception of high-frequency patterns. We interpret these results in relation to current practices and devise new and more granular guidelines for color mapping in continuous maps.Item Human Emotion and the Uncanny Valley: A Glm, Mds, and Isomap Analysis of Robot Video RatingsHo, Chin-Chang; MacDorman, Karl F.The eerie feeling attributed to human-looking robots and animated characters may be a key factor in our perceptual and cognitive discrimination between the human and the merely humanlike. This study applies factor analysis, correlation, the generalized linear model (GLM), multidimensional scaling (MDS), and kernel isometric mapping (ISOMAP) to analyze ratings of 27 emotions of 16 moving figures whose appearance varies along a human likeness continuum. The results indicate (1) Attributions of eerie and creepy better capture human visceral reaction to an uncanny robot than strange. (2) Eeriness and creepiness are mainly associated with fear but also shocked, disgusted, and nervous. Strange and humanlike are less strongly associated with emotion. (3) Thus, strange and humanlike may be more cognitive, while eerie and creepy are more perceptual and emotional. (4) Human and facial features increase ratings of human likeness. (5) Women are slightly more sensitive to eerie and creepy than men; and older people may be more willing to attribute human likeness to a robot despite its eeriness.Item Information and Data Visualization Needs among Direct Care Nurses in the Intensive Care Unit(Thieme, 2022) Lindroth, Heidi L.; Pinevich, Yuliya; Barwise, Amelia K.; Fathma, Sawsan; Diedrich, Daniel; Pickering, Brian W.; Herasevich, Vitaly; School of NursingObjectives: Intensive care unit (ICU) direct care nurses spend 22% of their shift completing tasks within the electronic health record (EHR). Miscommunications and inefficiencies occur, particularly during patient hand-off, placing patient safety at risk. Redesigning how direct care nurses visualize and interact with patient information during hand-off is one opportunity to improve EHR use. A web-based survey was deployed to better understand the information and visualization needs at patient hand-off to inform redesign. Methods: A multicenter anonymous web-based survey of direct care ICU nurses was conducted (9-12/2021). Semi-structured interviews with stakeholders informed survey development. The primary outcome was identifying primary EHR data needs at patient hand-off for inclusion in future EHR visualization and interface development. Secondary outcomes included current use of the EHR at patient hand-off, EHR satisfaction, and visualization preferences. Frequencies, means, and medians were calculated for each data item then ranked in descending order to generate proportional quarters using SAS v9.4. Results: In total, 107 direct care ICU nurses completed the survey. The majority (46%, n = 49/107) use the EHR at patient hand-off to verify exchanged verbal information. Sixty-four percent (n = 68/107) indicated that current EHR visualization was insufficient. At the start of an ICU shift, primary EHR data needs included hemodynamics (mean 4.89 ± 0.37, 98%, n = 105), continuous IV medications (4.55 ± 0.73, 93%, n = 99), laboratory results (4.60 ± 0.56, 96%, n = 103), mechanical circulatory support devices (4.62 ± 0.72, 90%, n = 97), code status (4.40 ± 0.85, 59%, n = 108), and ventilation status (4.35 + 0.79, 51%, n = 108). Secondary outcomes included mean EHR satisfaction of 65 (0-100 scale, standard deviation = ± 21) and preferred future EHR user-interfaces to be organized by organ system (53%, n = 57/107) and visualized by tasks/schedule (61%, n = 65/107). Conclusion: We identified information and visualization needs of direct care ICU nurses. The study findings could serve as a baseline toward redesigning an EHR interface.Item An Iterative Method of Sentiment Analysis for Reliable User Evaluation(2019-08) Hui, Jingyi; Fang, Shiaofen; Xia, Yuni; Durresi, ArjanBenefited from the booming social network, reading posts from other users over the internet is becoming one of commonest ways for people to intake information. One may also have noticed that sometimes we tend to focus on users provide well-founded analysis, rather than those merely who vent their emotions. This thesis aims at finding a simple and efficient way to recognize reliable information sources among countless internet users by examining the sentiments from their past posts. To achieve this goal, the research utilized a dataset of tweets about Apple's stock price retrieved from Twitter. Key features we studied include post-date, user name, number of followers of that user, and the sentiment of that tweet. Prior to making further use of the dataset, tweets from users who do not have sufficient posts are filtered out. To compare user sentiments and the derivative of Apple's stock price, we use Pearson correlation between them to describe how well each user performs. Then we iteratively increase the weight of reliable users and lower the weight of untrustworthy users, the correlation between overall sentiment and the derivative of stock price will finally converge. The final correlations for individual users are their performance scores. Due to the chaos of real-world data, manual segmentation via data visualization is also proposed as a denoise method to improve performance. Besides our method, other metrics can also be considered as user trust index, such as numbers of followers of each user. Experiments are conducted to prove that our method outperforms others. With simple input, this method can be applied to a wide range of topics including election, economy, and the job market.Item Measuring and Visualizing Chlamydia and Gonorrhea Inequality: An Informatics Approach Using Geographical Information Systems(University of Illinois at Chicago, 2019-09-19) Lai, Patrick T.S.; Wilson, Jeffrey S.; Wu, Huanmei; Jones, Josette; Dixon, Brian E.; Geography, School of Liberal ArtsBackground: Health inequality measurements are vital in understanding disease patterns in identifying high-risk patients and implementing effective intervention programs to treat and manage sexually transmitted diseases. Objectives: To measure and identify inequalities among chlamydia and gonorrhea rates using Gini coefficient measurements and spatial visualization mapping from geographical information systems. Additionally, we seek to examine trends of disease rate distribution longitudinally over a ten-year period for an urbanized county. Methods: Chlamydia and gonorrhea data from January 2005 to December 2014 were collected from the Indiana Network for Patient Care, a health information exchange system that gathers patient data from electronic health records. The Gini coefficient was used to calculate the magnitude of inequality in disease rates. Spatial visualization mapping and decile categorization of disease rates were conducted to identify locations where high and low rates of disease persisted and to visualize differences in inequality. A multiple comparisons ANOVA test was conducted to determine if Gini coefficient values were statistically different between townships and time periods during the study. Results: Our analyses show that chlamydia and gonorrhea rates are not evenly distributed. Inequalities in disease rates existed for different areas of the county with higher disease rates occurring near the center of the county. Inequality in gonorrhea rates were higher than chlamydia rates. Disease rates were statistically different when geographical locations or townships were compared to each other (p < 0.0001) but not for different years or time periods (p = 0.5152). Conclusion: The ability to use Gini coefficients combined with spatial visualization techniques presented a valuable opportunity to analyze information from health information systems in investigating health inequalities. Knowledge from this study can benefit and improve health quality, delivery of services, and intervention programs while managing healthcare costs.