Evaluation of record linkage methods for iterative insertions

Methods of Information in Medicine

Volumn 48, Issue 5, 2009, Pages 429-437

  Sariyar, M ^a   Borg, A ^a   Pommerening, K ^a  

^a UNIVERSITY MEDICAL CENTER MAINZ   (Germany)

Author keywords

Decision trees; EM algorithm; Object identification; Record linkage; Support vector machines

Indexed keywords

ALGORITHM; ARTICLE; COMPUTER PROGRAM; DECISION TREE; EVALUATION; FUZZY LOGIC; GERMANY; HUMAN; INFORMATION PROCESSING; MEDICAL RECORD; METHODOLOGY; ORGANIZATION AND MANAGEMENT; REGISTER; STATISTICAL MODEL;

ALGORITHMS; AUTOMATIC DATA PROCESSING; DATA COLLECTION; DECISION TREES; FUZZY LOGIC; GERMANY; HUMANS; MEDICAL RECORD LINKAGE; MEDICAL RECORDS SYSTEMS, COMPUTERIZED; MODELS, STATISTICAL; REGISTRIES; SOFTWARE;

EID: 76649106456     PISSN: 00261270     EISSN: None     Source Type: Journal    
DOI: 10.3414/ME9238     Document Type: Conference Paper

References (26)

1. Fellegi IP, Sunter AB. A theory for record linkage. Journal of the American Statistical Association 1969; 64: 1183-1210.
2. Christensen R. Log-Linear Models and Logistic Regression. 2nd edition. New York: Springer; 1997.
3. Dempster AP, et al. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society B 1977; 39: 1-38.
4. Winkler WE. Advanced methods for record linkage. Proceedings of the Section on Survey Research Methods; American Statistical Association; available at www.census.gov/srd/www/byyear.html; 1994.
5. Armstrong JA, Mayda JE. Model-based estimation of record linkage error rates. Survey Methodology 1994; 9: 137-147.
6. Haberman SJ. Log-linear fit for contingency tablesalgorithm AS51. Applied Statistics 1972; 21: 218-225.
7. Espeland MA, Hui SL. A general approach to analyzing epidemiologic data that contains misclassification errors. Biometrics 1987; 43: 1001-1012.
8. Espeland MA, Odoroff C. Algorithms for computing maximum likelihood estimates from incomplete discrete data. Technical Report 01/84; University of Rochester (Statistical Department).
9. Darroch JN, Ratcliff D. Generalized iterative scaling for log-linear models. Annals of Mathematical Statistics 1972; 43 (5): 1470-1480.
10. Monge A, Elkan A. An efficient domain-independent algorithm for detecting approximately duplicate database records. In: Proc. of the ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery. Tucson/Arizona; 1997.
11. Yancey WE. Evaluating string comparator performance for record linkage. Research Report available at www.census.gov/srd/www/byyear.html; 2005.
12. Jaro MA. Advances in record-linkage methodology as applied to matching the 1985 census of Tampa/ Florida. Journal of the American Statistical Association 1989; 89: 414-420.
13. Winkler WE. String comparator metrics and enhanced decision rules in the Fellegi-Sunter model of record linkage. Proceedings of the Section on Survey Research Methods; American Statistical Association; 1990. pp 354-369.
14. Breiman L, Friedman, J Olshen R, Stone C. Classification and Regression Trees. Wadsworth; 1984.
15. Breiman L. Bagging predictors. Machine Learning 1996; 24: 123-140.
16. Freund Y, Schapire RE. Experiments with a new boosting algorithm. In: Machine Learning: Proceedings of the Thirteenth International Conference. San Francisco: Morgan Kaufman; 1996. pp 148-156.
17. Burges CJC. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 1998; 2 (2): 121-167.
18. Vapnik V. Statistical Learning Theory. John Wiley: New York; 1998.
19. Reng CM, Debold P, Specker C, Pommerening K. Generische Lösungen zum Datenschutz für die Forschungsnetze in der Medizin. München: MWV; 2006.
20. Christen P, Churches T, Hegland M. Febrla parallel open source data linkage system. In: Proceedings of the 8th Pacific-Asia Conference, Sydney 2004. New York: Springer Lecture Notes in Artificial Intelligence 2004; 3056: 638-647.
21. Quantin C, et al. The Epilink record linkage software. Methods Inf Med 2005; 44 (1): 66-71.
22. Haber E. AS207: Fitting a general log-linear model. Applied Statistics 1984; 33: 358-362.
23. Therneau TM, Atkinson EJ. An introduction to recursive partitioning using the rpart routine. Technical Report 61; Mayo Clinic, Section of Biostatistics; 1997.
24. Peters A, et al. ipred: Improved predictors. R News 2002. pp 33-36.
25. Chang CC, Lin CJ. Libsvm: a library for support vector machines. Software available at http://www.csie.ntu.edu.tw/ cjlin/libsvm, 2001.
26. Lin CJ. On the convergence of the decomposition method for support vector machines. IEEE Transactions on Neural Networks 2001; 12 (6): 1288-1298.