A bootstrapping algorithm to improve cohort identification using structured data

Journal of Biomedical Informatics

Volumn 44, Issue SUPPL. 1, 2011, Pages

  Kandula, Sasikiran ^a   Zeng Treitler, Qing ^a   Chen, Lingji ^b,d   Salomon, William L ^c   Bray, Bruce E ^a  

^a UNIVERSITY OF UTAH SCHOOL OF MEDICINE   (United States)

^b SCIENTIFIC SYSTEMS COMPANY INC   (United States)

^c Clinical Metrics LLC   (United States)

^d BAE SYSTEMS   (United States)

Author keywords

Cohort analysis; International classification of diseases; Machine learning

Indexed keywords

BOOTSTRAPPING ALGORITHM; BOOTSTRAPPING METHOD; CLASSIFICATION MODELS; CLINICAL RESEARCH; COHORT ANALYSIS; DIABETES MELLITUS; EVALUATION RESULTS; HYPERLIPIDEMIA; INTERNATIONAL CLASSIFICATION OF DISEASES; PRIOR INFORMATION; STRUCTURED DATA;

BIOINFORMATICS; COMPUTER APPLICATIONS; LEARNING SYSTEMS;

CODES (SYMBOLS);

ALGORITHM; ARTICLE; BOOTSTRAPPING; DATA BASE; DIABETES MELLITUS; DRUG SURVEILLANCE PROGRAM; ELECTRONIC MEDICAL RECORD; HISTOGRAM; HUMAN; HYPERLIPIDEMIA; INTERNATIONAL CLASSIFICATION OF DISEASES; MAJOR CLINICAL STUDY; PRIORITY JOURNAL;

ALGORITHMS; COHORT STUDIES; DATABASES, FACTUAL; DIABETES MELLITUS; HUMANS; HYPERLIPIDEMIAS; INTERNATIONAL CLASSIFICATION OF DISEASES;

EID: 83955166031     PISSN: 15320464     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbi.2011.10.013     Document Type: Article

References (25)

1. World Health Organization. The importance of pharmacovigilance. safety monitoring of medicinal products. Geneva: WHO; 2002.
2. Lowe H., Weber S., Ferris T., Hernandez P. Self-service support for research patient cohort identification and review of clinical data in the STRIDE clinical data warehouse 2010, AMIA CRI Summit.
3. Turchin A, Pendergrass ML, Kohane IS. DITTO - a tool for identification of patient cohorts from the text of physician notes in the electronic medical record. In: AMIA annual symposium proceedings/AMIA symposium; 2005. p. 744-8.
4. Shea A.M., Curtis L.H., Szczech L.A., Schulman K.A. Sensitivity of international classification of diseases codes for hyponatremia among commercially insured outpatients in the United States. BMC Nephrol 2008, 9:5.
5. Boyd M., Specks U., Finkielman J.D. Accuracy of the ICD-9 code for identification of patients with Wegener's granulomatosis. J Rheumatol 2010, 37(2):474.
6. Kullo I.J., Fan J., Pathak J., Savova G.K., Ali Z., Chute C.G. Leveraging informatics for genetic studies: use of the electronic medical record to enable a genome-wide association study of peripheral arterial disease. J Am Med Inform Assoc 2010, 17(5):568-574.
7. Schildcrout J.S., Basford M.A., Pulley J.M., Masys D.R., Roden D.M., Wang D., et al. An analytical approach to characterize morbidity profile dissimilarity between distinct cohorts using electronic medical records. J Biomed Inform 2010, 43(6):914-923.
8. McCarty C.A., Chisholm R.L., Chute C.G., Kullo I.J., Jarvik G.P., Larson E.B., et al. The eMERGE Network: a consortium of biorepositories linked to electronic medical records data for conducting genomic studies. BMC Med Genom 2011, 4:13.
9. The eMERGE Network. https://www.mc.vanderbilt.edu/victr/dcc/projects/acc/index.php/Library_o f_Phenotype_Algorithms.
10. Friedlin J, Overhage M, Al-Haddad MA, Waters JA, Aguilar-Saavedra JJ, Kesterson J, et al. Comparing methods for identifying pancreatic cancer patients using electronic data sources. In: AMIA annual symposium proceedings/AMIA symposium 2010, 2010. p. 237-41.
11. Liao K.P., Cai T., Gainer V., Goryachev S., Zeng-treitler Q., Raychaudhuri S., et al. Electronic medical records for discovery research in rheumatoid arthritis. Arthrit Care Res (Hoboken) 2010, 62(8):1120-1127.
12. Lagor C., Aronsky D., Fiszman M., Haug P.J. Automatic identification of patients eligible for a pneumonia guideline: comparing the diagnostic accuracy of two decision support models. Stud Health Technol Inform 2001, 84(Pt 1):493-497.
13. Meystre SM, Deshmukh VG, Mitchell J. A clinical use case to evaluate the i2b2 Hive: predicting asthma exacerbations. In: AMIA annual symposium proceedings/AMIA symposium 2009, 2009. p. 442-6.
14. Aronson J.K., Ferner R.E. Joining the DoTS: new approach to classifying adverse drug reactions. BMJ Clin Res 2003, 327(7425):1222-1225.
15. Noren G.N., Sundberg R., Bate A., Edwards I.R. A statistical methodology for drug-drug interaction surveillance. Stat Med 2008, 27(16):3057-3070.
16. Noren G.N., Edwards IR: Modern methods of pharmacovigilance: detecting adverse effects of drugs. Clin Med (Lond, Engl) 2009, 9(5):486-489.
17. Zeng Q., Cimino J.J. A knowledge-based, concept-oriented view generation system for clinical data. J Biomed Inform 2001, 34(2):112-128.
18. Zeng Q., Cimino J.J., Zou K.H. Providing concept-oriented views for clinical data using a knowledge-based system: an evaluation. J Am Med Inform Assoc 2002, 9(3):294-305.
19. Witten I.H., Frank E. Data mining: Practical machine learning tools and techniques 2005, Morgan Kaufmann Publications.
20. Keerthi S.S., Shevade S.K., Bhattacharyya C., Murthy K.R.K. Improvements to Platt's SMO algorithm for SVM classifier design 2001, vol. 13. MIT Press, p. 637-49.
21. Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report. Circulation 2002;106(25): 3143-421.
22. Zeng Q.T., Goryachev S., Weiss S., Sordo M., Murphy S.N., Lazarus R. Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language processing system. BMC Med Inform Decis Making 2006, 6:30.
23. Meystre S., Haug P.J. Automation of a problem list using natural language processing. BMC Med Inform Decis Making 2005, 5:30.
24. El-kan C., Noto K. Learning classifiers from only positive and unlabeled data. Proceedings of the 14th international conference on Knowledge discovery and data mining: 2008 2008, 213-220. AMC.
25. Benesch C., Witter D.M., Wilder A.L., Duncan P.W., Samsa G.P., Matchar D.B. Inaccuracy of the international classification of diseases (ICD-9-CM) in identifying the diagnosis of ischemic cerebrovascular disease. Neurology 1997, 49(3):660-664.