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Predicting Chronic Heart Failure Using Diagnoses Graphs

Lecture Notes in Computer Science, 2017, Vol.LNCS-10410, p.295-312 [Peer Reviewed Journal]

IFIP International Federation for Information Processing 2017 ;Attribution ;ISSN: 0302-9743 ;ISBN: 3319668072 ;ISBN: 9783319668079 ;EISSN: 1611-3349 ;EISBN: 3319668080 ;EISBN: 9783319668086 ;DOI: 10.1007/978-3-319-66808-6_20

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  • Title:
    Predicting Chronic Heart Failure Using Diagnoses Graphs
  • Author: Nagrecha, Saurabh ; Thomas, Pamela Bilo ; Feldman, Keith ; Chawla, Nitesh V.
  • Subjects: Cardiovascular disease ; Computer Science ; Directed acyclic graph ; EMR ; Health care ; Heart failure ; Humanities and Social Sciences ; Library and information sciences ; Medicare
  • Is Part Of: Lecture Notes in Computer Science, 2017, Vol.LNCS-10410, p.295-312
  • Description: 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.
  • Publisher: Cham: Springer International Publishing
  • Language: English
  • Identifier: ISSN: 0302-9743
    ISBN: 3319668072
    ISBN: 9783319668079
    EISSN: 1611-3349
    EISBN: 3319668080
    EISBN: 9783319668086
    DOI: 10.1007/978-3-319-66808-6_20
  • Source: HAL SHS: Archive ouverte en Sciences de l'Homme et de la Société (Open Access)
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