IDMVis: Temporal Event Sequence Visualization for Type 1 Diabetes Treatment Decision Support
IDMVis: Temporal Event Sequence Visualization for Type 1 Diabetes Treatment Decision Support
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DOI：
10.1109/tvcg.2018.2865076
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发表时间：
2019-01
影响因子：
5.2
通讯作者：
Yixuan Zhang;Kartik Chanana;Cody Dunne
中科院分区：
文献类型：
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作者：
Yixuan Zhang;Kartik Chanana;Cody Dunne
1型糖尿病是一种慢性、不可治愈的自身免疫性疾病，影响数百万美国人，其中身体停止产生胰岛素，血糖水平升高。强化糖尿病管理的目标是通过频繁调整胰岛素方案、饮食和行为来降低平均血糖。手动日志和医疗设备数据由患者收集，但这些多个源以不同的可视化设计呈现给临床医生，使得时间推断困难。我们进行了一项为期18个月的设计研究，临床医生进行密集的糖尿病管理。我们提出了一个数据抽象和新的分层任务抽象这个域。我们还有助于IDMVis：一个可视化工具的时间事件序列与多维的，相互关联的数据。IDMVis包括一种新的技术，用于通过双哨兵事件折叠和对齐记录，并缩放中间时间轴。我们验证我们的设计决策的基础上，我们的领域抽象，最佳实践，并通过定性评估与六名临床医生。这项研究的结果表明，IDMVis准确地反映了临床医生的工作流程。使用IDMVI，临床医生能够识别数据质量问题，如缺失或冲突的数据，在数据缺失时重建患者记录，区分不同模式的日期，并在识别差异后促进教育干预。
Type 1 diabetes is a chronic, incurable autoimmune disease affecting millions of Americans in which the body stops producing insulin and blood glucose levels rise. The goal of intensive diabetes management is to lower average blood glucose through frequent adjustments to insulin protocol, diet, and behavior. Manual logs and medical device data are collected by patients, but these multiple sources are presented in disparate visualization designs to the clinician—making temporal inference difficult. We conducted a design study over 18 months with clinicians performing intensive diabetes management. We present a data abstraction and novel hierarchical task abstraction for this domain. We also contribute IDMVis: a visualization tool for temporal event sequences with multidimensional, interrelated data. IDMVis includes a novel technique for folding and aligning records by dual sentinel events and scaling the intermediate timeline. We validate our design decisions based on our domain abstractions, best practices, and through a qualitative evaluation with six clinicians. The results of this study indicate that IDMVis accurately reflects the workflow of clinicians. Using IDMVis, clinicians are able to identify issues of data quality such as missing or conflicting data, reconstruct patient records when data is missing, differentiate between days with different patterns, and promote educational interventions after identifying discrepancies.