Abstract
The presence of patients affected by different diseases at the same time is becoming a major health and societal issue. In clinical literature, this phenomenon is known as comorbidity, and it can be studied from the administrative databases of general practitioners’ prescriptions based on diagnoses. In this contribution, we propose a two-step strategy for analyzing comorbidity patterns. In the first step, we investigate the prescription data with association rules extracted by a two-mode network (or bipartite graph) to find frequent itemsets that can be used to assist physicians in making diagnoses. In the second step, we derive a one-mode network of the diseases codes with association rules, and we perform the k-core partitioning algorithm to identify the most relevant and connected parts in the network corresponding to the most related pathologies.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The interactome [26] is the whole set of molecular interactions in a given cell, or individual, and it is represented as a graph of the biological network. It includes all the interactions between molecules belonging to different biochemical families, such as nucleic acids, proteins, lipids, carbohydrates, hormones, etc. Interactomics [18] is a discipline at the intersection of bio-informatics and biology that deals with the study of networks of interactions and their consequences.
References
Adomavicius G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)
Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pp. 207–216. ACM, New York (1993)
Baggio, G., Corsini, A., Floreani, A., Giannini, S., Zagonel, V.: Gender medicine: a task for the third millennium. Clin. Chem. Lab. Med. 51(4), 713–727 (2013)
Brin, S., Motwani, R., Ullman, J.D., Tsur, S.: Dynamic itemset counting and implication rules for market basket data. ACM SIGMOD Rec. 26(2), 255–264 (1997)
Capobianco, E., Lio, P.: Comorbidity: a multidimensional approach. Trends Mol. Med. 19, 515–521 (2013)
Cavallo, P., Pagano, S., Boccia, G., De Caro, F., De Santis, M., Capunzo, M.: Network analysis of drug prescriptions. Pharmacoepidemiol. Drug Saf. 22, 130–137 (2013)
Cavallo P., Pagano, S., De Santis, M., Capobianco, E., General practitioners records are epidemiological predictors of comorbidities: an analytical cross-sectional 10-year retrospective study. J. Clin. Med. 7(8), 184 (2018)
Chrischilles, E., Schneider, K., Wilwert, J., Lessman, G., O’Donnell, B., Gryzlak, B., Wright, K., Wallace, R.: Beyond comorbidity: expanding the definition and measurement of complexity among older adults using administrative claims data. Med. Care 52(3), S75–S84 (2014)
Cramer, A.O.J., Waldorp, L.J., van der Maas, H.L.J., Borsboom, D.: Comorbidity: a network perspective. Behav. Brain Sci. 33, 137–150 (2010)
De Nooy, W., Mrvar, A., Batagelj, V.: Exploratory social network analysis with Pajek, vol. 27. Cambridge University Press, Cambridge (2011)
Everett, M.G., Borgatti, S.P.: The dual-projection approach for two-mode networks. Soc. Netw. 35, 204–210 (2013)
Feinstein, A.R.: The pre-therapeutic classification of co-morbidity in chronic disease. J. Chronic Dis. 27, 455–468 (1970)
Freeman, L.C.: Centrality in social networks conceptual clarification. Soc. Netw. 1(3), 215–239 (1978)
Gamberger, D., Lavrac, N., Jovanoski, V.: High confidence association rules for medical diagnosis. In: Proceedings of IDAMAP99, pp. 42–51 (1999)
Gupta, N., Mangal, N., Tiwari, K., Mitra, P.: Mining quantitative association rules in protein sequences. In: Williams, G.J., Simoff, S.J. (eds.) Data Mining, LNAI 3755, pp. 273–281. Springer, Berlin (2006)
Hahsler, M.: arulesViz: interactive visualization of association rules with R. R J. 9(2), 163–175 (2017)
Hahsler, M., Chelluboina, S., Hornik, K., Buchta, C.: The arules R-package ecosystem: analyzing interesting patterns from large transaction datasets. J. Mach. Learn. Res. 12, 1977–1981 (2011)
Kiemer, L., Cesareni, G.: Comparative interactomics: comparing apples and pears? Trends Biotechnol. 25(10), 448–454 (2007)
Mercer, S.W., Smith, S.M., Wyke, S., O’dowd, T., Watt, G.C.: Multimorbidity in primary care: developing the research agenda. Fam. Pract. 26, 79–80 (2009). Available via DIALOG. https://academic.oup.com/fampra/article/26/2/79/2367540. Cited 07 May 2018
Pfaundler, M., von Seht, L.: Uber Syntropie von Krankheitszustanden. Z. Kinderheilk. 30, 298–313 (1921)
Puzyrev, V.P.: Genetic bases of human comorbidity. Genetika 51, 491–502 (2015)
Seidman, S.B.: Network structure and minimum degree. Soc. Netw. 5(3), 269–287 (1983)
Serban, G., Czibula, I.G., Campan, A.: A programming interface for medical diagnosis prediction. Stud. Univ. Babes-Bolyai Inform. LI, 21–30 (2006)
Valderas, J.M.: Increasing clinical, community, and patient-centered health research. J. Comorb. 3, 41–44 (2013)
Valderas, J.M., Starfield, B., Sibbald, B., Salisbury, C., Roland, M.: Defining comorbidity: implications for understanding health and health services. Ann. Fam. Med. 7, 357–363 (2009)
Vidal, M., Cusick, M.E., Barabási, A.L.: Interactome networks and human disease. Cell 144(6), 986–998 (2011)
World Health Organization: International classification of Diseases (ICD) (2010). Available from: http://www.who.int/classifications/icd/en/
Yurkovich, M., Avina-Zubieta, J.A., Thomas, J., Gorenchtein, M., Lacaille, D.: A systematic review identifies valid co-morbidity indices derived from administrative health data. J. Clin. Epidemiol. 68, 3–14 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Giordano, G., De Santis, M., Pagano, S., Ragozini, G., Vitale, M.P., Cavallo, P. (2020). Association Rules and Network Analysis for Exploring Comorbidity Patterns in Health Systems. In: Ragozini, G., Vitale, M. (eds) Challenges in Social Network Research. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-030-31463-7_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-31463-7_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-31462-0
Online ISBN: 978-3-030-31463-7
eBook Packages: Computer ScienceComputer Science (R0)