Understanding chronic disease comorbidities from baseline networks: knowledge discovery utilising administrative healthcare data

ABSTRACT

Hospitals routinely collect admitted patients' data for administrative purposes and for reporting to the government and health insurers. These heterogeneous and mostly untapped data contain rich semantic information about patients' health conditions in the form of standard disease codes. These traces of clinical information can be aggregated over patients to understand how their health progresses over time. When applied on particular chronic disease patients, this approach can potentially help in understanding chronic disease comorbidities as well as in the knowledge discovery on how the chronic disease progresses over time. In this paper, we propose a network-based approach to extract semantic information from hospital administrative data in order to develop a representation of chronic disease progression specifically - type 2 diabetes. We then propose measures for attribution adjustment that ranks the more prevalent comorbidities in chronic patients higher, compared to the non-chronic ones. We also have applied the framework on the administrative data of 2,760 sampled patients to understand how diabetes progresses over time through different comorbidities. This understanding can be effectively converted to actionable intelligence that can be useful in the formulation of better health policy and resource management.

References

1. World Health Organization, 2012. Preventing chronic diseases: a vital investment. Geneva: WHO, 2005.
2. Ward, B., Schiller, J., and Goodman, R., 2014. Multiple chronic conditions among US adults: a 2012 update. Prev. Chronic Dis. 11.
3. Australian Institute of Health and Welfare (AIHW), 2014. Australia's Health 2014.
4. Kopelman, P.G., 2000. Obesity as a medical problem. Nature 404, 6778, 635--643.
5. Rathmann, W., Haastert, B., Icks, A.a., Löwel, H., Meisinger, C., Holle, R., and Giani, G., 2003. High prevalence of undiagnosed diabetes mellitus in Southern Germany: target populations for efficient screening. The KORA survey 2000. Diabetologia 46, 2, 182--189.
6. Gregg, E.W., Cadwell, B.L., Cheng, Y.J., Cowie, C.C., Williams, D.E., Geiss, L., Engelgau, M.M., and Vinicor, F., 2004. Trends in the prevalence and ratio of diagnosed to undiagnosed diabetes according to obesity levels in the US. Diabetes Care 27, 12, 2806--2812.
7. Taubert, G., Winkelmann, B.R., Schleiffer, T., März, W., Winkler, R., Gök, R., Klein, B., Schneider, S., and Boehm, B.O., 2003. Prevalence, predictors, and consequences of unrecognized diabetes mellitus in 3266 patients scheduled for coronary angiography. Am. Heart J. 145, 2, 285--291.
8. MacKenzie, E.J., Morris Jr, J.A., and Eeelstein, S.L., 1989. Effect of pre-existing disease on length of hospital stay in trauma patients. Journal of Trauma and Acute Care Surgery 29, 6, 757--765.
9. Umpierrez, G.E., Isaacs, S.D., Bazargan, N., You, X., Thaler, L.M., and Kitabchi, A.E., 2002. Hyperglycemia: an independent marker of in-hospital mortality in patients with undiagnosed diabetes. The Journal of Clinical Endocrinology & Metabolism 87, 3, 978--982.
10. Lauruschkat, A.H., Arnrich, B., Albert, A.A., Walter, J.A., Amann, B., Rosendahl, U.P., Alexander, T., and Ennker, J., 2005. Prevalence and risks of undiagnosed diabetes mellitus in patients undergoing coronary artery bypass grafting. Circulation 112, 16, 2397--2402.
11. Tenenbaum, A., Motro, M., Fisman, E.Z., Boyko, V., Mandelzweig, L., Reicher-Reiss, H., Graff, E., Brunner, D., and Behar, S., 2000. Clinical impact of borderline and undiagnosed diabetes mellitus in patients with coronary artery disease. The American journal of cardiology 86, 12, 1363--1366.
12. Kapur, V., Sandblom, R.E., Hert, R., James, B., and Sean, D., 1999. The medical cost of undiagnosed sleep apnea. Sleep 22, 6, 749.
13. Harris, M.I., 1993. Undiagnosed NIDDM: clinical and public health issues. Diabetes Care 16, 4, 642--652.
14. Khan, A., Uddin, S., and Srinivasan, U., 2016. Adapting graph theory and social network measures on healthcare data: a new framework to understand chronic disease progression. In Proceedings of the Australasian Computer Science Week Multiconference ACM, 66.
15. Charlson, M.E., Pompei, P., Ales, K.L., and MacKenzie, C.R., 1987. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J. Chronic Dis. 40, 5, 373--383.
16. Elixhauser, A., Steiner, C., Harris, D.R., and Coffey, R.M., 1998. Comorbidity measures for use with administrative data. Med. Care 36, 1, 8--27.
17. Sharabiani, M.T., Aylin, P., and Bottle, A., 2012. Systematic review of comorbidity indices for administrative data. Med. Care 50, 12, 1109--1118.
18. Wong, D.T. and Knaus, W.A., 1991. Predicting outcome in critical care: the current status of the APACHE prognostic scoring system. Can. J. Anaesth. 38, 3, 374--383.
19. Breslow, M.J. and Badawi, O., 2012. Severity scoring in the critically ill: part 1---interpretation and accuracy of outcome prediction scoring systems. CHEST Journal 141, 1, 245--252.
20. Barabási, A.-L., 2007. Network medicine---from obesity to the "diseasome". N. Engl. J. Med. 357, 4, 404--407.
21. Loscalzo, J., Kohane, I., and Barabasi, A.L., 2007. Human disease classification in the postgenomic era: a complex systems approach to human pathobiology. Mol. Syst. Biol. 3, 1.
22. Burton, P.R., Clayton, D.G., Cardon, L.R., Craddock, N., Deloukas, P., Duncanson, A., Kwiatkowski, D.P., McCarthy, M.I., Ouwehand, W.H., and Samani, N.J., 2007. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447, 7145, 661--678.
23. Hidalgo, C.A., Blumm, N., Barabási, A.-L., and Christakis, N.A., 2009. A dynamic network approach for the study of human phenotypes. PLoS Comput. Biol. 5, 4, e1000353.
24. Ideker, T. and Sharan, R., 2008. Protein networks in disease. Genome Res. 18, 4, 644--652.
25. Davis, D.A., Chawla, N.V., Christakis, N.A., and Barabási, A.-L., 2010. Time to CARE: a collaborative engine for practical disease prediction. Data Mining and Knowledge Discovery 20, 3, 388--415.
26. Davis, D.A., Chawla, N.V., Blumm, N., Christakis, N., and Barabási, A.-L., 2008. Predicting individual disease risk based on medical history. In Proceedings of the 17th ACM conference on Information and knowledge management ACM, 769--778.
27. Breault, J.L., Goodall, C.R., and Fos, P.J., 2002. Data mining a diabetic data warehouse. Artif. Intell. Med. 26, 1, 37--54.
28. Baglioni, M., Pieroni, S., Geraci, F., Mariani, F., Molinaro, S., Pellegrini, M., and Lastres, E., 2013. A New Framework for Distilling Higher Quality Information from Health Data via Social Network Analysis. In Data Mining Workshops (ICDMW), 2013 IEEE 13th International Conference on IEEE, 48--55.
29. Anderson, J.G., 2002. Evaluation in health informatics: social network analysis. Comput. Biol. Med. 32, 3, 179--193.
30. World health Organization, 2014. WHO | International Classification of Diseases (ICD).
31. Luijks, H., Schermer, T., Bor, H., van Weel, C., Lagro-Janssen, T., Biermans, M., and de Grauw, W., 2012. Prevalence and incidence density rates of chronic comorbidity in type 2 diabetes patients: an exploratory cohort study. BMC Med. 10, 1, 128.
32. Folino, F., Pizzuti, C., and Ventura, M., 2010. A comorbidity network approach to predict disease risk. In Information Technology in Bio-and Medical Informatics, ITBAM 2010 Springer, 102--109.
33. Fetter, R.B., Shin, Y., Freeman, J.L., Averill, R.F., and Thompson, J.D., 1980. Case mix definition by diagnosis-related groups. Med. Care, i--53.
34. Quan, H., Sundararajan, V., Halfon, P., Fong, A., Burnand, B., Luthi, J.-C., Saunders, L.D., Beck, C.A., Feasby, T.E., and Ghali, W.A., 2005. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med. Care, 1130--1139.
35. Garland, A., Fransoo, R., Olafson, K., Ramsey, C., Yogendren, M., Chateau, D., and McGowan, K., 2012. The epidemiology and outcomes of critical illness in Manitoba. University of Manitoba, Faculty of Medicine, Department of Community Health Sciences.
36. American Diabetes Association, 2013. Kidney Disease (Nephropathy). In Living With Diabetes.
37. National Heart, L., and Blood Institute, 2015. Who Is at Risk for Heart Valve Disease? In Heart Valve Diseases.
38. Benjamin, E.J., Levy, D., Vaziri, S.M., D'Agostino, R.B., Belanger, A.J., and Wolf, P.A., 1994. Independent risk factors for atrial fibrillation in a population-based cohort: the Framingham Heart Study. JAMA 271, 11, 840--844.

Index Terms

• Applied computing

  • Life and medical sciences

    • Health care information systems

    • Health informatics

• Human-centered computing

  • Collaborative and social computing

    • Collaborative and social computing design and evaluation methods

      • Social network analysis

• Mathematics of computing

  • Discrete mathematics

    • Graph theory

      • Graph algorithms