Predicting Chronic Heart Failure Using Diagnoses Graphs

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10410)


Predicting the onset of heart disease is of obvious importance as doctors try to improve the general health of their patients. If it were possible to identify high-risk patients before their heart failure diagnosis, doctors could use that information to implement preventative measures to keep a heart failure diagnosis from becoming a reality. Integration of Electronic Medical Records (EMRs) into clinical practice has enabled the use of computational techniques for personalized healthcare at scale. The larger goal of such modeling is to pivot from reactive medicine to preventative care and early detection of adverse conditions. In this paper, we present a trajectory-based disease progression model to detect chronic heart failure. We validate our work on a database of Medicare records of 1.1 million elderly US patients. Our supervised approach allows us to assign likelihood of chronic heart failure for an unseen patient’s disease history and identify key disease progression trajectories that intensify or diminish said likelihood. This information will be a tremendous help as patients and doctors try to understand what are the most dangerous diagnoses for those who are susceptible to heart failure. Using our model, we demonstrate some of the most common disease trajectories that eventually result in the development of heart failure.


Heart failure Cardiovascular disease Directed acyclic graph Medicare EMR Health care 


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© IFIP International Federation for Information Processing 2017

Authors and Affiliations

  1. 1.Department of Computer Science and Engineering, Interdisciplinary Center for Network Science and Applications (iCeNSA)University of Notre DameNotre DameUSA
  2. 2.Indiana Biosciences Research InstituteIndianapolisUSA

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