1. Dezhi Fang, Fred Hohman, Peter Polack, Hillol Sarker, Minsuk Kahng, Moushumi Sharmin, Mustafa and Duen Horng Chau.
    mHealth Visual Discovery Dashboard. In Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers. 2017, 237–240. URL, DOI BibTeX

    	author = "Fang, Dezhi and Hohman, Fred and Polack, Peter and Sarker, Hillol and Kahng, Minsuk and Sharmin, Moushumi and al'Absi, Mustafa and Chau, Duen Horng",
    	title = "mHealth Visual Discovery Dashboard",
    	booktitle = "Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers",
    	year = 2017,
    	series = "UbiComp '17",
    	pages = "237--240",
    	address = "New York, NY, USA",
    	publisher = "ACM",
    	abstract = "We present Discovery Dashboard, a visual analytics system for exploring large volumes of time series data from mobile medical field studies. Discovery Dashboard offers interactive exploration tools and a data mining motif discovery algorithm to help researchers formulate hypotheses, discover trends and patterns, and ultimately gain a deeper understanding of their data. Discovery Dashboard emphasizes user freedom and flexibility during the data exploration process and enables researchers to do things previously challenging or impossible to do --- in the web-browser and in real time. We demonstrate our system visualizing data from a mobile sensor study conducted at the University of Minnesota that included 52 participants who were trying to quit smoking.",
    	acmid = 3123170,
    	doi = "10.1145/3123024.3123170",
    	isbn = "978-1-4503-5190-4",
    	keywords = "health informatics, motif discovery, time series data, visual analytics",
    	location = "Maui, Hawaii",
    	numpages = 4,
    	pmid = 29204228,
    	url = ""
  2. Robert Pienta, Acar Tamersoy, Alex Endert, Shamkant Navathe, Hanghang Tong and Duen Horng Chau.
    VISAGE: Interactive Visual Graph Querying. In Proceedings of the International Working Conference on Advanced Visual Interfaces. 2016, 272–279. URL, DOI BibTeX

    	author = "Pienta, Robert and Tamersoy, Acar and Endert, Alex and Navathe, Shamkant and Tong, Hanghang and Chau, Duen Horng",
    	title = "VISAGE: Interactive Visual Graph Querying",
    	booktitle = "Proceedings of the International Working Conference on Advanced Visual Interfaces",
    	year = 2016,
    	series = "AVI '16",
    	pages = "272--279",
    	address = "Bari, Italy",
    	publisher = "ACM",
    	abstract = {Extracting useful patterns from large network datasets has become a fundamental challenge in many domains. We present VISAGE, an interactive visual graph querying approach that empowers users to construct expressive queries, without writing complex code (e.g., finding money laundering rings of bankers and business owners). Our contributions are as follows: (1) we introduce graph autocomplete, an interactive approach that guides users to construct and refine queries, preventing over-specification; (2) VISAGE guides the construction of graph queries using a data-driven approach, enabling users to specify queries with varying levels of specificity, from concrete and detailed (e.g., query by example), to abstract (e.g., with "wildcard" nodes of any types), to purely structural matching; (3) a twelve-participant, within-subject user study demonstrates VISAGE's ease of use and the ability to construct graph queries significantly faster than using a conventional query language; (4) VISAGE works on real graphs with over 468K edges, achieving sub-second response times for common queries.},
    	doi = "10.1145/2909132.2909246",
    	isbn = "978-1-4503-4131-8",
    	keywords = "Graph Querying and Mining, Interaction Design, Visualization",
    	pmid = 28553670,
    	pubstate = "published",
    	tppubtype = "inproceedings",
    	url = ""
  3. Z He, S Carini, I Sim and C Weng.
    Visual aggregate analysis of eligibility features of clinical trials. J Biomed Inform, 2015. URL BibTeX

    	author = "Z. He and S. Carini and I. Sim and C. Weng",
    	title = "Visual aggregate analysis of eligibility features of clinical trials",
    	journal = "J Biomed Inform",
    	year = 2015,
    	abstract = {To develop a method for profiling the collective populations targeted for recruitment by multiple clinical studies addressing the same medical condition using one eligibility feature each time.Using a previously published database COMPACT as the backend, we designed a scalable method for visual aggregate analysis of clinical trial eligibility features. This method consists of four modules for eligibility feature frequency analysis, query builder, distribution analysis, and visualization, respectively. This method is capable of analyzing (1) frequently used qualitative and quantitative features for recruiting subjects for a selected medical condition, (2) distribution of study enrollment on consecutive value points or value intervals of each quantitative feature, and (3) distribution of studies on the boundary values, permissible value ranges, and value range widths of each feature. All analysis results were visualized using Google Charts API. Five recruited potential users assessed the usefulness of this method for identifying common patterns in any selected eligibility feature for clinical trial participant selection.We implemented this method as a Web-based analytical system called VITTA (Visual Analysis Tool of Clinical Study Target Populations). We illustrated the functionality of VITTA using two sample queries involving quantitative features BMI and HbA1c for conditions "hypertension" and "Type 2 diabetes", respectively. The recruited potential users rated the user-perceived usefulness of VITTA with an average score of 86.4/100.We contributed a novel aggregate analysis method to enable the interrogation of common patterns in quantitative eligibility criteria and the collective target populations of multiple related clinical studies. A larger-scale study is warranted to formally assess the usefulness of VITTA among clinical investigators and sponsors in various therapeutic areas.},
    	institution = "Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA. Electronic address:",
    	keywords = "Clinical trial, Knowledge management, Patient selection, Selection bias",
    	pmid = 25615940,
    	pubstate = "published",
    	tppubtype = "article",
    	url = ""
  4. Moushumi Sharmin, Andrew Raij, David Epstien, Inbal Nahum-Shani, Gayle J Beck, Sudip Vhaduri, Kenzie Preston and Santosh Kumar.
    Visualization of Time-series Sensor Data to Inform the Design of Just-in-time Adaptive Stress Interventions. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. 2015, 505–516. URL, DOI BibTeX

    	author = "Sharmin, Moushumi and Raij, Andrew and Epstien, David and Nahum-Shani, Inbal and Beck, J. Gayle and Vhaduri, Sudip and Preston, Kenzie and Kumar, Santosh",
    	title = "Visualization of Time-series Sensor Data to Inform the Design of Just-in-time Adaptive Stress Interventions",
    	booktitle = "Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing",
    	year = 2015,
    	series = "UbiComp '15",
    	pages = "505--516",
    	address = "Osaka, Japan",
    	publisher = "ACM",
    	abstract = "We investigate needs, challenges, and opportunities in visualizing time-series sensor data on stress to inform the design of just-in-time adaptive interventions (JITAIs). We identify seven key challenges: massive volume and variety of data, complexity in identifying stressors, scalability of space, multifaceted relationship between stress and time, a need for representation at multiple granularities, inter-person variability, and limited understanding of JITAI design requirements due to its novelty. We propose four new visualizations based on one million minutes of sensor data (n=70). We evaluate our visualizations with stress researchers (n=6) to gain first insights into its usability and usefulness in JITAI design. Our results indicate that spatio-temporal visualizations help identify and explain between- and within-person variability in stress patterns and contextual visualizations enable decisions regarding the timing, content, and modality of intervention. Interestingly, a granular representation is considered informative but noise-prone; an abstract representation is the preferred starting point for designing JITAIs.",
    	doi = "10.1145/2750858.2807537",
    	isbn = "978-1-4503-3574-4",
    	keywords = "just-in-time adaptive interventions (JITAIs), Stress, stress management, Visualization",
    	pmid = 26539566,
    	pubstate = "published",
    	tppubtype = "inproceedings",
    	url = ""
  5. V Kumar, H Park, R C Basole, M Braunstein, M Kahng, D H Chau, A Tamersoy, D A Hirsh, N Serban, J Bost, B Lesnick, B Schissel and M Thompson.
    Exploring Clinical Care Processes Using Visual and Data Analytics: Challenges and Opportunities. Knowledge Discovery and Data Mining (KDD): Workshop on Data Science for Social Good, 2014. URL BibTeX

    	author = "V. Kumar and H. Park and R.C. Basole and M. Braunstein and M. Kahng and D.H. Chau and A. Tamersoy and D.A. Hirsh and N. Serban and J. Bost and B. Lesnick and B. Schissel and M. Thompson",
    	title = "Exploring Clinical Care Processes Using Visual and Data Analytics: Challenges and Opportunities",
    	journal = "Knowledge Discovery and Data Mining (KDD): Workshop on Data Science for Social Good",
    	year = 2014,
    	keywords = "asthma, emergency care, pediatric hospital, process mining, Visual analytics",
    	pubstate = "published",
    	tppubtype = "article",
    	url = ""
  6. C D Stolper, F Foerster, M Kahng, Z Lin, A Goel, J Stasko and D H Chau.
    GLOs: graph-level operations for exploratory network visualization. In 2014 ACM CHI Conference on Human Factors in Computing Systems (CHI 2014). 2014, 1375–1380. URL BibTeX

    	author = "C.D. Stolper and F. Foerster and M. Kahng and Z. Lin and A. Goel and J. Stasko and D.H. Chau",
    	title = "GLOs: graph-level operations for exploratory network visualization",
    	booktitle = "2014 ACM CHI Conference on Human Factors in Computing Systems (CHI 2014)",
    	year = 2014,
    	pages = "1375--1380",
    	organization = "ACM",
    	abstract = "There is a wealth of visualization techniques available for graph and network visualization. However, each of these techniques was designed for a specific task. Many graph visualization techniques and the transitions between them can be specified using a set of operations on the visualization elements such as positioning or resizing nodes, showing or hiding edges, or showing or hiding axes. We term these operations Graph-Level Operations or GLOs. Our goal is to identify and provide a comprehensive set of these operations in order to better support the broadest range of graph and network analysis tasks. Here we present early results of our work, including a preliminary set of operations and an example application of GLOs in transitioning between familiar graph visualization techniques.",
    	keywords = "Graphs; visualization techniques; operations",
    	pubstate = "published",
    	tppubtype = "inproceedings",
    	url = ""




Copyright © 2018 MD2K. MD2K is supported by the National Institutes of Health Big Data to Knowledge Initiative (Grant #1U54EB020404)
Team: Cornell Tech, GA Tech, Harvard, U. Memphis, Northwestern, Ohio State, Open mHealth, UCLA, UCSD, UCSF, UMass, U. Michigan, Utah, WVU