title: MatrixFlow: temporal network visual analytics to track symptom evolution during disease progression.
abstract: OBJECTIVE
  To develop a visual analytic system to help medical professionals improve disease diagnosis by providing insights for understanding disease progression.
  
  
  METHODS
  We develop MatrixFlow, a visual analytic system that takes clinical event sequences of patients as input, constructs time-evolving networks and visualizes them as a temporal flow of matrices. MatrixFlow provides several interactive features for analysis: 1) one can sort the events based on the similarity in order to accentuate underlying cluster patterns among those events; 2) one can compare co-occurrence events over time and across cohorts through additional line graph visualization.
  
  
  RESULTS
  MatrixFlow is applied to visualize heart failure (HF) symptom events extracted from a large cohort of HF cases and controls (n=50,625), which allows medical experts to reach insights involving temporal patterns and clusters of interest, and compare cohorts in novel ways that may lead to improved disease diagnoses.
  
  
  CONCLUSIONS
  MatrixFlow is an interactive visual analytic system that allows users to quickly discover patterns in clinical event sequences. By unearthing the patterns hidden within and displaying them to medical experts, users become empowered to make decisions influenced by historical patterns.
authors.affiliation: IBM T.J. Watson Research Center, Hawthorne, NY, USA.
authors.forename: Adam
authors.initials: A
authors.lastname: Perer
authors.forename: Jimeng
authors.initials: J
authors.lastname: Sun
mesh_terms: D016022:Case-Control Studies; D003196:Computer Graphics; D003658:Decision Making, Computer-Assisted; D018450:Disease Progression; D005260:Female; D006333:Heart Failure / Q000175:diagnosis*; D006801:Humans; D008297:Male