  This paper presents a method for building patient-based networks that we call
Precision disease networks, and its uses for predicting medical outcomes. Our
methodology consists of building networks, one for each patient or case, that
describes the dis-ease evolution of the patient (PDN) and store the networks as
a set of features in a data set of PDN's, one per observation. We cluster the
PDN data and study the within and between cluster variability. In addition, we
develop data visualization technics in order to display, compare and summarize
the network data. Finally, we analyze a dataset of heart diseases patients from
a New Jersey statewide data-base MIDAS (Myocardial Infarction Data Acquisition
System, in order to show that the network data improve on the prediction of
important patient outcomes such as death or cardiovascular death, when compared
with the standard statistical analysis.
