Appl Clin Inform 2013; 04(01): 100-109
DOI: 10.4338/ACI-2012-12-RA-0051
Research Article
Schattauer GmbH

Validation of an Association Rule Mining-Based Method to Infer Associations Between Medications and Problems

A. Wright
1   Brigham & Women’s Hospital, Boston, MA, USA
2   Partners HealthCare, Boston, MA, USA
3   Harvard Medical School, Boston, MA, USA
,
A. McCoy
4   University of Texas Health Science Center at Houston, Boston, MA, USA
,
S. Henkin
1   Brigham & Women’s Hospital, Boston, MA, USA
5   Boston University School of Medicine, Boston, MA, USA
,
M. Flaherty
1   Brigham & Women’s Hospital, Boston, MA, USA
,
D. Sittig
4   University of Texas Health Science Center at Houston, Boston, MA, USA
› Institutsangaben
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Correspondence to:

Adam Wright, Ph.D.
Brigham and Women’s Hospital
1620 Tremont St.
Boston, MA 02115
(617) 525–9811

Publikationsverlauf

received: 05. Dezember 2012

accepted: 09. Februar 2012

Publikationsdatum:
19. Dezember 2017 (online)

 

Summary

Background: In a prior study, we developed methods for automatically identifying associations between medications and problems using association rule mining on a large clinical data warehouse and validated these methods at a single site which used a self-developed electronic health record. Objective: To demonstrate the generalizability of these methods by validating them at an external site.

Methods: We received data on medications and problems for 263,597 patients from the University of Texas Health Science Center at Houston Faculty Practice, an ambulatory practice that uses the Allscripts Enterprise commercial electronic health record product. We then conducted association rule mining to identify associated pairs of medications and problems and characterized these associations with five measures of interestingness: support, confidence, chi-square, interest and conviction and compared the top-ranked pairs to a gold standard.

Results: 25,088 medication-problem pairs were identified that exceeded our confidence and support thresholds. An analysis of the top 500 pairs according to each measure of interestingness showed a high degree of accuracy for highly-ranked pairs.

Conclusion: The same technique was successfully employed at the University of Texas and accuracy was comparable to our previous results. Top associations included many medications that are highly specific for a particular problem as well as a large number of common, accurate medication-problem pairs that reflect practice patterns.

Citation: Wright A, McCoy A, Henkin S, Flaherty M, Sittig D. Validation of an association rule mining-based method to infer associations between medications and problems. ppl Clin Inf 2013; 4: 100–109

http://dx.doi.org/10.4338/ACI-2012-12-RA-0051


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Conflicts of Interest

The authors report no conflicts of interest.

  • References

  • 1 Blumenthal D, Tavenner M. The "meaningful use" regulation for electronic health records. The New England journal of medicine 2010; 363 (06) 501-504. Epub 2010/07/22.
  • 2 Information Management Processes (Standard IM 6.40): 2008 Comprehensive Accreditation Manual for Hospitals. The Official Handbook Oakbrook Terrace; Illinois: 2008
  • 3 Wright A, Goldberg H, Hongsermeier T, Middleton B. A description and functional taxonomy of rule-based decision support content at a large integrated delivery network. Journal of the American Medical Informatics Association : JAMIA 2007; 14 (04) 489-496. Epub 2007/04/27.
  • 4 Lobach DF, Hammond WE. Computerized decision support based on a clinical practice guideline improves compliance with care standards. The American journal of medicine 1997; 102 (01) 89-98. Epub 1997/01/01.
  • 5 Szeto HC, Coleman RK, Gholami P, Hoffman BB, Goldstein MK. Accuracy of computerized outpatient diagnoses in a Veterans Affairs general medicine clinic. The American journal of managed care 2002; 8 (01) 37-43. Epub 2002/01/30.
  • 6 Wright A, Pang J, Feblowitz JC, Maloney FL, Wilcox AR, Ramelson HZ, Schneider LI, Bates DW. A method and knowledge base for automated inference of patient problems from structured data in an electronic medical record. Journal of the American Medical Informatics Association : JAMIA 2011; 18 (06) 859-867. Epub 2011/05/27.
  • 7 Wright A, Feblowitz J, Maloney FL, Henkin S, Bates DW. Use of an Electronic Problem List by Primary Care Providers and Specialists. Journal of general internal medicine. 2012 Epub 2012/03/20.
  • 8 Wright A, Maloney FL, Feblowitz JC. Clinician attitudes toward and use of electronic problem lists: a thematic analysis. BMC Med Inform Decis Mak 2011; 11: 36. Epub 2011/05/27.
  • 9 Jao C, Hier D, Galanter W, Valenta A. Assessing physician comprehension of and attitudes toward problem list documentation. AMIA Annual Symposium proceedings / AMIA Symposium AMIA Symposium 2008; 990. Epub 2008/11/13.
  • 10 Galanter WL, Hier DB, Jao C, Sarne D. Computerized physician order entry of medications and clinical decision support can improve problem list documentation compliance. Int J Med Inform 2010; 79 (05) 332-338. Epub 2008/07/05.
  • 11 Carpenter JD, Gorman PN. Using medication list--problem list mismatches as markers of potential error. Proceedings / AMIA Annual Symposium AMIA Symposium 2002: 106-110. Epub 2002/12/05.
  • 12 Meystre SM, Haug PJ. Randomized controlled trial of an automated problem list with improved sensitivity. Int J Med Inform 2008; 77 (09) 602-612. Epub 2008/02/19.
  • 13 Meystre S, Haug PJ. Automation of a problem list using natural language processing. BMC Med Inform Decis Mak 2005; 5: 30. Epub 2005/09/02.
  • 14 McCoy AB, Wright A, Laxmisan A, Ottosen MJ, McCoy JA, Butten D, Sittig DF. Development and evaluation of a crowdsourcing methodology for knowledge base construction: identifying relationships between clinical problems and medications. Journal of the American Medical Informatics Association: JAMIA. 2012 Epub 2012/05/15.
  • 15 Wright A, Chen ES, Maloney FL. An automated technique for identifying associations between medications, laboratory results and problems. Journal of biomedical informatics 2010; 43 (06) 891-901. Epub 2010/10/05.
  • 16 Wright A, Pang J, Feblowitz JC, Maloney FL, Wilcox AR, McLoughlin KS, Ramelson H, Schneider L, Bates DW. Improving completeness of electronic problem lists through clinical decision support: a randomized, controlled trial. Journal of the American Medical Informatics Association: JAMIA. 2012 Epub 2012/01/05.
  • 17 Goethals B. Survey on frequent pattern mining. Univ of Helsinki; 2003
  • 18 Zhang C, Zhang S. Association rule mining: models and algorithms. Springer-Verlag; 2002
  • 19 Tan PN, Kumar V, Srivastava J. editors. Selecting the right interestingness measure for association patterns. 2002. ACM;
  • 20 Hipp J, Güntzer U, Nakhaeizadeh G. Algorithms for association rule mining—a general survey and comparison. ACM SIGKDD Explorations Newsletter 2000; 2 (01) 58-64.
  • 21 Mobasher B, Dai H, Luo T, Nakagawa M. editors. Effective personalization based on association rule discovery from web usage data. Proceedings of the 3rd international workshop on Web information and data management. 2001. ACM;
  • 22 Sarwar B, Karypis G, Konstan J, Riedl J. editors. Analysis of recommendation algorithms for e-commerce. Proceedings of the 2nd ACM conference on Electronic commerce. 2000. ACM;
  • 23 Wright A CE, Maloney FL. An automated technique for identifying associations between medications, laboratory results, and problems. J Biomed Inf 2010; 43: 891-901.

Correspondence to:

Adam Wright, Ph.D.
Brigham and Women’s Hospital
1620 Tremont St.
Boston, MA 02115
(617) 525–9811

  • References

  • 1 Blumenthal D, Tavenner M. The "meaningful use" regulation for electronic health records. The New England journal of medicine 2010; 363 (06) 501-504. Epub 2010/07/22.
  • 2 Information Management Processes (Standard IM 6.40): 2008 Comprehensive Accreditation Manual for Hospitals. The Official Handbook Oakbrook Terrace; Illinois: 2008
  • 3 Wright A, Goldberg H, Hongsermeier T, Middleton B. A description and functional taxonomy of rule-based decision support content at a large integrated delivery network. Journal of the American Medical Informatics Association : JAMIA 2007; 14 (04) 489-496. Epub 2007/04/27.
  • 4 Lobach DF, Hammond WE. Computerized decision support based on a clinical practice guideline improves compliance with care standards. The American journal of medicine 1997; 102 (01) 89-98. Epub 1997/01/01.
  • 5 Szeto HC, Coleman RK, Gholami P, Hoffman BB, Goldstein MK. Accuracy of computerized outpatient diagnoses in a Veterans Affairs general medicine clinic. The American journal of managed care 2002; 8 (01) 37-43. Epub 2002/01/30.
  • 6 Wright A, Pang J, Feblowitz JC, Maloney FL, Wilcox AR, Ramelson HZ, Schneider LI, Bates DW. A method and knowledge base for automated inference of patient problems from structured data in an electronic medical record. Journal of the American Medical Informatics Association : JAMIA 2011; 18 (06) 859-867. Epub 2011/05/27.
  • 7 Wright A, Feblowitz J, Maloney FL, Henkin S, Bates DW. Use of an Electronic Problem List by Primary Care Providers and Specialists. Journal of general internal medicine. 2012 Epub 2012/03/20.
  • 8 Wright A, Maloney FL, Feblowitz JC. Clinician attitudes toward and use of electronic problem lists: a thematic analysis. BMC Med Inform Decis Mak 2011; 11: 36. Epub 2011/05/27.
  • 9 Jao C, Hier D, Galanter W, Valenta A. Assessing physician comprehension of and attitudes toward problem list documentation. AMIA Annual Symposium proceedings / AMIA Symposium AMIA Symposium 2008; 990. Epub 2008/11/13.
  • 10 Galanter WL, Hier DB, Jao C, Sarne D. Computerized physician order entry of medications and clinical decision support can improve problem list documentation compliance. Int J Med Inform 2010; 79 (05) 332-338. Epub 2008/07/05.
  • 11 Carpenter JD, Gorman PN. Using medication list--problem list mismatches as markers of potential error. Proceedings / AMIA Annual Symposium AMIA Symposium 2002: 106-110. Epub 2002/12/05.
  • 12 Meystre SM, Haug PJ. Randomized controlled trial of an automated problem list with improved sensitivity. Int J Med Inform 2008; 77 (09) 602-612. Epub 2008/02/19.
  • 13 Meystre S, Haug PJ. Automation of a problem list using natural language processing. BMC Med Inform Decis Mak 2005; 5: 30. Epub 2005/09/02.
  • 14 McCoy AB, Wright A, Laxmisan A, Ottosen MJ, McCoy JA, Butten D, Sittig DF. Development and evaluation of a crowdsourcing methodology for knowledge base construction: identifying relationships between clinical problems and medications. Journal of the American Medical Informatics Association: JAMIA. 2012 Epub 2012/05/15.
  • 15 Wright A, Chen ES, Maloney FL. An automated technique for identifying associations between medications, laboratory results and problems. Journal of biomedical informatics 2010; 43 (06) 891-901. Epub 2010/10/05.
  • 16 Wright A, Pang J, Feblowitz JC, Maloney FL, Wilcox AR, McLoughlin KS, Ramelson H, Schneider L, Bates DW. Improving completeness of electronic problem lists through clinical decision support: a randomized, controlled trial. Journal of the American Medical Informatics Association: JAMIA. 2012 Epub 2012/01/05.
  • 17 Goethals B. Survey on frequent pattern mining. Univ of Helsinki; 2003
  • 18 Zhang C, Zhang S. Association rule mining: models and algorithms. Springer-Verlag; 2002
  • 19 Tan PN, Kumar V, Srivastava J. editors. Selecting the right interestingness measure for association patterns. 2002. ACM;
  • 20 Hipp J, Güntzer U, Nakhaeizadeh G. Algorithms for association rule mining—a general survey and comparison. ACM SIGKDD Explorations Newsletter 2000; 2 (01) 58-64.
  • 21 Mobasher B, Dai H, Luo T, Nakagawa M. editors. Effective personalization based on association rule discovery from web usage data. Proceedings of the 3rd international workshop on Web information and data management. 2001. ACM;
  • 22 Sarwar B, Karypis G, Konstan J, Riedl J. editors. Analysis of recommendation algorithms for e-commerce. Proceedings of the 2nd ACM conference on Electronic commerce. 2000. ACM;
  • 23 Wright A CE, Maloney FL. An automated technique for identifying associations between medications, laboratory results, and problems. J Biomed Inf 2010; 43: 891-901.