Visual analytics of clinical data and treatment processes for cohorts

Problem: 

Providing new visual analytics techniques for the discovery of new clinical knowledge and the improvement of clinical treatments is a key factor to reduce healthcare costs, reduce the risks to the patients, increase compliance to clinical guidelines, and increase patients' satisfaction. 

The candidate will focus on challenges of time-oriented data in Visual Analytics for healthcare, in particular: 

  • Intertwining Patient Condition with Treatment Processes.
  • Scalable Analysis from Single Patients to Cohorts.
The topic is a follow-up of a previous thesis:

 

Aim: 
The candidate will design and develop a visualization for the temporal evolution of raw clinical parameters and the sequence of performed clinical actions, as well as the hierarchy and the logical structure of the clinical guidelines in use. Tightly intertwined visual and analytical methods will support the examination of temporal abstractions, compliance to recommendations, and context-specific modifications of care processes.
 
Topics: 
Visual Analytics, Information Visualization, (Medical Informatics)
Other information: 

The research prototytpe will be developed by using prefuse. More info: 

The clinical guidelines are represented by Asbru. More info:

 

Previous knowledge: 
java, (optionally: prefuse, asbru)
Scope: 
PR
Scope: 
MA
Contact: 
Paolo Federico, by appointment, federico [at] ifs.tuwien.ac.at
Area: 
Visual Analytics (VA)
Status: 
closed