Precision disease networks (PDN)

TL;DR

This paper introduces a novel approach called Precision Disease Networks (PDN) that models disease progression at the individual patient level, enhancing the prediction of medical outcomes through network analysis and visualization.

Contribution

The paper presents a new methodology for constructing patient-specific disease networks and demonstrates their effectiveness in predicting outcomes like death in heart disease patients.

Findings

PDN data improves outcome prediction accuracy

Networks reveal disease evolution patterns

Visualization techniques aid in data interpretation

Abstract

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.