Pathway-Finder: An Interactive Recommender System for Supporting Personalized Care Pathways
Abstract—Clinical pathways define the essential component of the complex care process, with the objective to optimize patient outcomes and resource allocation. Clinical pathway analysis has gained increasing attention in order to automate the patient treatment process. In this demonstration paper, we propose Pathway-Finder, an interactive recommender system to vi- sually explore and discover clinical pathways. The interactive web-service efficiently collects patient information that is nec- essary for designing an effective personalized treatment plan. Pathway-Finder implements a Bayesian Network to discover causal relationships among different factors. Additionally, the system implements a big-data infrastructure using Spark that is hosted as a HDinsight cluster on Microsoft Azure for Research platform to support real-time recommendation and visualization. We demonstrate Pathway-Finder to interactively recommend personalized interventions to minimize 30-day readmission risk for Heart Failure (HF).
ICDM 2014 | IEEE International Conference on Data Mining