Abstract
The objective of this work was to assess the interest of a data mining approach to detect incident breast cancer cases in medico administrative data. Data from the French casemix system (PMSI) were linked with the Isère cancer registry, which was the gold standard to define incident breast cancer. Formal Concept Analysis (FCA) was used to compute combinations of attribute values in the PMSI that could further define algorithm of detection of incident breast cancer. FCA allowed to automatically evaluate any possible combination of attribute values in terms of sensibility and Positive Predictive Value. This method can help experts in quality assessment of medico-economical databases as epidemiological tools.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Références
Levi F, Bosetti C, Lucchini F, Negri E, La Vecchia C. Monitoring the decrease in breast cancer mortality in Europe. Eur J Cancer Prev 2005; 14: 497–502
Stewart B, Kleihues P. World Cancer Report. Oxford, Oxford University Press: 2003
Fetter RB, Shin Y, Freeman JL, Averill RF, Thompson JD. Case mix definition by diagnosis-related groups. Med Care 1980; 18: 1–53
Dussaucy A, Viel JF, Mulin B, Euvrard J. The framework Prospective Payment Information Systems: bias, sources of errors and consequences. Rev Epidemiol Sante Publique 1994; 42(4): 345–58
Baron JA, Lu-Yao G, Barrett J et al. Internal validation of Medicare claims data. Epidemiology 1994; 5: 541–4
Couris CM, Schott AM, Morgon E, Ecochard R, Colin C. A literature review to assess the use of claims databases in identifying cancer incident cases. Health Services and Outcomes Research Methodology 2003; 4: 49–63
Freeman JL, Zhang D, Freeman DH, Goodwin JS. An approach to identifying incident breast cancer cases using Medicare claims data. J Clin Epidemiol 2000; 53: 605–14
Nattinger AB, Laud PW, Bajorunaite R, Sparapani RA, Freeman JL. An algorithm for the use of Medicare claims data to identify women with incident breast cancer. Health Serv Res 2004; 39: 1733–49
Couris CM, Colin C, Rabilloud M, Schott AM, Ecochard R. Method of correction to assess the number of hospitalized incident breast cancer cases based on claims databases. J Clin Epidemiol 2002; 55: 386–91
Couris CM, Forêt-Dodelin C, Rabilloud M, Colin C, Bobin JY, Dargent D, Raudrant D, Schott AM. Sensitivity and specificity of two methods used to identify incident breast cancer in specialized units using claims databases. Rev Epidemiol Sante Publique 2004; 52: 151–60
Gold HT, Do HT. Evaluation of three algorithms to identify incident breast cancer in Medicare claims data. Health Serv Res 2007; 42: 2056–69
Remontet L, Mitton N, Couris CM, Iwaz J, Gomez F, Olive F, Polazzi S, Schott AM Trombert B, Bossard N, Colonna M. Is it possible to estimate the incidence of breast cancer from medico-administrative databases? Eur J Epidemiol 2008; 23: 681–8
Baldi I, Vicari P, Di Cuonzo D, Zanetti R, Pagano E, Rosato R, Sacerdote C, Segnan N, Merletti F, Ciccone G. A high positive predictive value algorithm using hospital administrative data identified incident cancer cases. J Clin Epidemiol 2008; 61: 373–9
Couris CM, Polazzi S, Olive F, Remontet L, Bossard N, Gomez F, Schott AM, Mitton N, Colonna M, Trombert B. Breast cancer incidence using administrative data: correction with sensitivity and specificity. J Clin Epidemiol 2009; 62: 660–6
Fayyad U, Piatetsky-Shapiro G, Smyth P. The kdd process for extracting useful knowledge from volumes of data. Communications ACM 1996; 39: 27–34
Curtis JR, Cheng H, Delzell E, Fram D, Kilgore M, Saag K, Yun H, Dumouchel W. Adaptation of Bayesian data mining algorithms to longitudinal claims data: coxib safety as an example. Med Care 2008; 46: 969–75
Trifirò G, Pariente A, Coloma PM, Kors JA, Polimeni G, Miremont-Salamé G, Catania MA, Salvo F, David A, Moore N, Caputi AP, Sturkenboom M, Molokhia M, Hippisley-Cox J, Acedo CD, van der Lei J, Fourrier-Reglat A; EU-ADR group. Data mining on electronic health record databases for signal detection in pharmacovigilance: which events to monitor? Pharmacoepidemiol Drug Saf 2009; 18(12): 1176–84
Jay N, Napoli A, Kohler F. Cancer patient flows discovery in DRG databases. Stud Health Technol Inform 2006; 124: 725–30
Quantin C, Fassa M, Coatrieux G, Riandey B, Trouessin G, Allaert FA. Linking anonymous databases for national and international multicenter epidemiological studies: a cryptographic algorithm. Rev Epidemiol Sante Publique 2009; 57: 33–9
Wille R. Formal Concept Analysis as Mathematical Theory of Concepts and Concept Hierarchies. LNCS 2005; 3626: 1–33
Key TJ, Verkasalo PK, Banks E. Epidemiology of breast cancer. Lancet Oncol 2001; 2: 133–40
Comité national de pilotage. Cahier des charges du programme national de dépistage systématique du cancer du sein. DGS 1994 — mise à jour: janvier 1996
Author information
Authors and Affiliations
Consortia
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag France
About this chapter
Cite this chapter
Goetz, C., Zang, A., le groupe ONC-EPI., Jay, N. (2011). Apports d’une méthode de fouille de données pour la détection des cancers du sein incidents dans les données du programme de médicalisation des systèmes d’information. In: Staccini, P.M., Harmel, A., Darmoni, S.J., Gouider, R. (eds) Systèmes d’information pour l’amélioration de la qualité en santé. Informatique et Santé, vol 1. Springer, Paris. https://doi.org/10.1007/978-2-8178-0285-5_17
Download citation
DOI: https://doi.org/10.1007/978-2-8178-0285-5_17
Publisher Name: Springer, Paris
Print ISBN: 978-2-8178-0284-8
Online ISBN: 978-2-8178-0285-5