Discriminative distance functions and the patient neighborhood graph for clinical decision support.
There are two essential reasons for the slow progress in the acceptance of clinical similarity search-based decision support systems (DSSs); the especial complexity of biomedical data making it difficult to define a meaningful and effective distance function and the lack of transparency and explanation ability in many existing DSSs. In this chapter, we address these two problems by introducing a novel technique for visualizing patient similarity with neighborhood graphs and by considering two techniques for learning discriminative distance functions. We present an experimental study and discuss our implementation of similarity visualization within a clinical DSS.
Tsymbal
Alexey
A
Corporate Technology Div. Siemens AG, Erlangen, Germany. alexey.tsymbal@siemens.com
Huber
Martin
M
Zhou
Shaohua Kevin
SK
D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology; D003196:Computer Graphics; D016208:Databases, Factual; D020000:Decision Support Systems, Clinical / Q000706:statistics & numerical data*; D006801:Humans; D012111:Residence Characteristics