Advertisement

Predicting Chronic Heart Failure Using Diagnoses Graphs

  • Saurabh Nagrecha
  • Pamela Bilo Thomas
  • Keith Feldman
  • Nitesh V. Chawla
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10410)

Abstract

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.

Keywords

Heart failure Cardiovascular disease Directed acyclic graph Medicare EMR Health care 

References

  1. 1.
    Prokosch, H.U., Ganslandt, V., et al.: Perspectives for medical informatics. Meth. Inf. Med. 48(1), 38–44 (2009)Google Scholar
  2. 2.
    Jensen, P.B., Jensen, L.J., Brunak, S.: Mining electronic health records: towards better research applications and clinical care. Nat. Rev. Genet. 13(6), 395–405 (2012)CrossRefGoogle Scholar
  3. 3.
    Belle, A., Thiagarajan, R., Soroushmehr, S.M., Navidi, F., Beard, D.A., Najarian, K.: Big data analytics in healthcare. BioMed. Res. Int. 2015 (2015)Google Scholar
  4. 4.
    Kawamoto, K., Houlihan, C.A., Balas, E.A., Lobach, D.F.: Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ 330(7494), 765 (2005)CrossRefGoogle Scholar
  5. 5.
    Nguyen, O.K., Makam, A.N., Clark, C., Zhang, S., Xie, B., Velasco, F., Amarasingham, R., Halm, E.A.: Predicting Readmissions from EHR Data. J. Hosp. Med. 7, 473–480 (2016). doi: 10.1002/jhm.2568
  6. 6.
    Kasl, S.V., Cobb, S.: Health behavior, illness behavior and sick role behavior: I. health and illness behavior. Arch. Environ. Health Int. J. 12(2), 246–266 (1966)CrossRefGoogle Scholar
  7. 7.
    Holzinger, A.: Trends in interactive knowledge discovery for personalized medicine: Cognitive science meets machine learning. IEEE Intell. Inf. Bull. 15, 6–14 (2014)Google Scholar
  8. 8.
    Delen, D., Walker, G., Kadam, A.: Predicting breast cancer survivability: a comparison of three data mining methods. Artif. Intell. Med. 34(2), 113–127 (2005)CrossRefGoogle Scholar
  9. 9.
    Davis, D.A., Chawla, N.V., Christakis, N.A., Barabási, A.-L.: Time to care: a collaborative engine for practical disease prediction. Data Min. Knowl. Disc. 20(3), 388–415 (2010)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Marek, K., Jennings, D., Lasch, S., Siderowf, A., Tanner, C., Simuni, T., Coffey, C., Kieburtz, K., Flagg, E., Chowdhury, S., et al.: The parkinson progression marker initiative (ppmi). Prog. Neurobiol. 95(4), 629–635 (2011)CrossRefGoogle Scholar
  11. 11.
    Wilkosz, P.A., Seltman, H.J., Devlin, B., Weamer, E.A., Lopez, O.L., DeKosky, S.T., Sweet, R.A.: Trajectories of cognitive decline in Alzheimer’s disease. Int. Psychogeriatr. 22(02), 281–290 (2010)CrossRefGoogle Scholar
  12. 12.
    Jensen, A.B., Moseley, P.L., Oprea, T.I., Ellesøe, S.G., Eriksson, R., Schmock, H., Jensen, P.B., Jensen, L.J., Brunak, S.: Temporal disease trajectories condensed from population-wide registry data covering 6.2 million patients. Nat. Commun. 5 (2014)Google Scholar
  13. 13.
    Lauderdale, D.S., Furner, S.E., Miles, T.P., Goldberg, J.: Epidemiologic uses of medicare data. Epidemiol. Rev. 15(2), 319–327 (1993)CrossRefGoogle Scholar
  14. 14.
    Mitchell, J.B., Bubolz, T., Paul, J.E., Pashos, C.L., Escarce, J.J., Muhlbaier, L.H., Wiesman, J.M., Young, W.W., Epstein, R., Javitt, J.C.: Using medicare claims for outcomes research. Med. Care 32(7), JS38 (1994)Google Scholar
  15. 15.
    Mozaffarian, D., Benjamin, E.J., Go, A.S., Arnett, D.K., Blaha, M.J., Cushman, M., Das, S.R., de Ferranti, S., Després, J.-P., Fullerton, H.J., et al.: Heart disease and stroke statistics2016 update. Circulation 133(4), e38–e360 (2016)CrossRefGoogle Scholar
  16. 16.
    Benson, A.R., Gleich, D.F., Leskovec, J.: Higher-order organization of complex networks. Science 353(6295), 163–166 (2016)CrossRefGoogle Scholar
  17. 17.
    McMurray, J.J., Stewart, S.: Epidemiology, aetiology, and prognosis of heart failure. Heart 83(5), 596–602 (2000)CrossRefGoogle Scholar
  18. 18.
    Sarnak, M.J., Levey, A.S., Schoolwerth, A.C., Coresh, J., Culleton, B., Hamm, L.L., McCullough, P.A., Kasiske, B.L., Kelepouris, E., Klag, M.J., Parfrey, P., Pfeffer, M., Raij, L., Spinosa, D.J., Wilson, P.W.: Kidney disease as a risk factor for development of cardiovascular disease. Hypertension 42(5), 1050–1065 (2003)CrossRefGoogle Scholar
  19. 19.
    Lloyd-Jones, D., Adams, R.J., Brown, T.M., Carnethon, M., Dai, S., De Simone, G., Ferguson, T.B., Ford, E., Furie, K., Gillespie, C., Go, A., Greenlund, K., Haase, N., Hailpern, S., Ho, P.M., Howard, V., Kissela, B., Kittner, S., Lackland, D., Lisabeth, L., Marelli, A., McDermott, M.M., Meigs, J., Mozaffarian, D., Mussolino, M., Nichol, G., Roger, V.L., Rosamond, W., Sacco, R., Sorlie, P., Stafford, R., Thom, T., Wasserthiel-Smoller, S., Wong, N.D., Wylie-Rosett, J.: Heart disease and stroke statistics-2010 update. Circulation 121(7), e46–e215 (2010)CrossRefGoogle Scholar
  20. 20.
    Picano, E., Gargani, L., Gheorghiade, M.: Why, when, and how to assess pulmonary congestion in heart failure: pathophysiological, clinical, and methodological implications. Heart Fail. Rev. 15(1), 63–72 (2010)CrossRefGoogle Scholar
  21. 21.
    Hoesel, L.M., Niederbichler, A.D., Ward, P.A.: Complement-related molecular events in sepsis leading to heart failure. Molecular Immunol. 44(1), 95–102 (2007). XXI International Complement Workshop Beijing, China, October 22–26, 2006CrossRefGoogle Scholar
  22. 22.
    Cao, Y.M., Hu, D.Y., Wu, Y., Wang, H.Y.: A pilot survey of the main causes of chronic heart failure in patients treated in primary hospitals in china. Zhonghua nei ke za zhi 44(7), 487–489 (2005)Google Scholar
  23. 23.
    Bland, E.F., Jones, D.: Rheumatic fever and rheumatic heart disease. Circulation 4(6), 836–843 (1951)CrossRefGoogle Scholar
  24. 24.
    Levy, D., Larson, M.G., Vasan, R.S., Kannel, W.B., Ho, K.K.L.: The progression from hypertension to congestive heart failure. JAMA 275(20), 1557–1562 (1996)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2017

Authors and Affiliations

  • Saurabh Nagrecha
    • 1
  • Pamela Bilo Thomas
    • 1
    • 2
  • Keith Feldman
    • 1
  • Nitesh V. Chawla
    • 1
    • 2
  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

Personalised recommendations