Literature DB >> 26390482

TimeSpan: Using Visualization to Explore Temporal Multi-dimensional Data of Stroke Patients.

Mona Hosseinkhani Loorak, Charles Perin, Noreen Kamal, Michael Hill, Sheelagh Carpendale.   

Abstract

We present TimeSpan, an exploratory visualization tool designed to gain a better understanding of the temporal aspects of the stroke treatment process. Working with stroke experts, we seek to provide a tool to help improve outcomes for stroke victims. Time is of critical importance in the treatment of acute ischemic stroke patients. Every minute that the artery stays blocked, an estimated 1.9 million neurons and 12 km of myelinated axons are destroyed. Consequently, there is a critical need for efficiency of stroke treatment processes. Optimizing time to treatment requires a deep understanding of interval times. Stroke health care professionals must analyze the impact of procedures, events, and patient attributes on time-ultimately, to save lives and improve quality of life after stroke. First, we interviewed eight domain experts, and closely collaborated with two of them to inform the design of TimeSpan. We classify the analytical tasks which a visualization tool should support and extract design goals from the interviews and field observations. Based on these tasks and the understanding gained from the collaboration, we designed TimeSpan, a web-based tool for exploring multi-dimensional and temporal stroke data. We describe how TimeSpan incorporates factors from stacked bar graphs, line charts, histograms, and a matrix visualization to create an interactive hybrid view of temporal data. From feedback collected from domain experts in a focus group session, we reflect on the lessons we learned from abstracting the tasks and iteratively designing TimeSpan.

Entities:  

Mesh:

Year:  2015        PMID: 26390482     DOI: 10.1109/TVCG.2015.2467325

Source DB:  PubMed          Journal:  IEEE Trans Vis Comput Graph        ISSN: 1077-2626            Impact factor:   4.579


  4 in total

1.  THALIS: Human-Machine Analysis of Longitudinal Symptoms in Cancer Therapy.

Authors:  Carla Floricel; Nafiul Nipu; Mikayla Biggs; Andrew Wentzel; Guadalupe Canahuate; Lisanne Van Dijk; Abdallah Mohamed; C David Fuller; G Elisabeta Marai
Journal:  IEEE Trans Vis Comput Graph       Date:  2021-12-24       Impact factor: 4.579

2.  A Tale of Two Centers: Visual Exploration of Health Disparities in Cancer Care.

Authors:  Sanjana Srabanti; Michael Tran; Virginie Achim; David Fuller; Guadalupe Canahuate; Fabio Miranda; G Elisabeta Marai
Journal:  IEEE Pac Vis Symp       Date:  2022-06-08

3.  Facilitating the Development of Deep Learning Models with Visual Analytics for Electronic Health Records.

Authors:  Cinyoung Hur; JeongA Wi; YoungBin Kim
Journal:  Int J Environ Res Public Health       Date:  2020-11-10       Impact factor: 3.390

4.  Dementia Patient Segmentation Using EMR Data Visualization: A Design Study.

Authors:  Hyoji Ha; Jihye Lee; Hyunwoo Han; Sungyun Bae; Sangjoon Son; Changhyung Hong; Hyunjung Shin; Kyungwon Lee
Journal:  Int J Environ Res Public Health       Date:  2019-09-16       Impact factor: 3.390

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.