IDMVis: Temporal event sequence visualization for type 1 diabetes treatment decision support

@Article{Zhang2019IdmvisTemporalEvent,
  author   = {Zhang, Yixuan and Chanana, Kartik and Dunne, Cody},
  journal  = {IEEE Transactions on Visualization and Computer Graphics},
  title    = {{IDMVis:} Temporal event sequence visualization for type 1 diabetes treatment decision support},
  year     = {2019},
  issn     = {1077-2626},
  note     = {VIS '18. Preprint \& Supplemental Material: https://github.com/visdunneright/IDMVis/},
  number   = {1},
  pages    = {512-522},
  volume   = {25},
  doi      = {10.1109/TVCG.2018.2865076},
  keywords = {blood;data visualisation;decision support systems;diseases;medical diagnostic computing;patient diagnosis;patient treatment;sugar;dual sentinel events;design decisions;domain abstractions;clinicians;IDMVis;patient records;temporal event sequence visualization;type 1 diabetes treatment decision support;blood glucose levels;intensive diabetes management;insulin protocol;manual logs;medical device data;disparate visualization designs;data abstraction;novel hierarchical task abstraction;visualization tool;multidimensional data;interrelated data;folding records;aligning records;chronic;incurable autoimmune disease;Americans;diet;behavior;Data visualization;Diabetes;Blood;Sugar;Insulin;Task analysis;Tools;Design study;task analysis;event sequence visualization;time series data;qualitative evaluation;health applications},
  series   = {VIS/TVCG},
}