Specializing for predicting obesity and its co-morbidities

Journal of Biomedical Informatics

Volumn 42, Issue 5, 2009, Pages 873-886

  Goldstein, Ira ^a   Uzuner, Özlem ^a,b  

^a State University of New York   (United States)

^b MIDDLE EAST TECHNICAL UNIVERSITY   (Turkey)

Author keywords

Classification; Combination of classifiers; Machine learning; Natural language processing

Indexed keywords

CLASSIFICATION; COMBINATION OF CLASSIFIERS; COMBINING CLASSIFIERS; DISCHARGE SUMMARY; MACHINE LEARNING; MULTI-CLASS CLASSIFICATION; NATURAL LANGUAGE PROCESSING;

COMPUTATIONAL LINGUISTICS; LINGUISTICS; LOCOMOTIVES; NATURAL LANGUAGE PROCESSING SYSTEMS; RAILROAD CARS; ROBOT LEARNING;

CLASSIFIERS;

ARTICLE; CLASSIFICATION ALGORITHM; COMORBIDITY; CONTROLLED STUDY; DATA ANALYSIS; DATA SYNTHESIS; HOSPITAL DISCHARGE; INFORMATION SYSTEM; MEDICAL RECORD; OBESITY; PREDICTION; PRIORITY JOURNAL;

BAYES THEOREM; COMORBIDITY; DATABASES, FACTUAL; DECISION TREES; DISEASE; HUMANS; INFORMATION STORAGE AND RETRIEVAL; MEDICAL INFORMATICS; MODELS, STATISTICAL; NATURAL LANGUAGE PROCESSING; OBESITY; PATIENT DISCHARGE; PREDICTIVE VALUE OF TESTS; SEMANTICS; UNIFIED MEDICAL LANGUAGE SYSTEM;

EID: 70349471136     PISSN: 15320464     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbi.2008.11.001     Document Type: Article

References (46)

1. Sibanda TC, He T, Szolovits P, Uzuner Ö. Syntactically-informed semantic category recognition in discharge summaries. In: Proceedings of the AMIA symposium; 2006. p. 714-8.
2. Goldstein I, Arzumtsyan A, Uzuner Ö. Three approaches to automatic assignment of ICD-9-CM codes to radiology reports. In: Proceedings of the AMIA symposium; 2007. p. 279-83.
3. Second i2b2 shared-task and workshop: challenges in natural language processing for clinical data. Available from: http://www.i2b2.org/NLP/.
4. Krenzelok E., Macpherson E., and Mrvos R. Disease surveillance and nonprescription medication sales can predict increases in poison exposure. J Med Toxicol 4 (2008) 7-10
5. Jain AK, Chandrasekaran B. Dimensionality and sample size considerations in pattern recognition practice. In: Krishnaia PR, Kanal LN, editors. Handbook of statistics. Amsterdam, The Netherlands; 1982, p. 835-55.
6. Tang L, Liu H. Bias analysis in text classification for highly skewed data. In: Fifth IEEE international conference on data mining (ICDM'05). Houston, TX; 2005. p. 781-4.
7. Wolpert D.H. Stacked generalization. Neural Netw 5 (1992) 241-259
8. Freund Y. Boosting a weak learning algorithm by majority. Inf Comput 121 (1995) 256-285
9. Schapire R.E. The strength of weak learnability. Mach Learn 5 (1990) 197-227
10. Schapire R.E., and Singer Y. BoosTexter: a boosting-based system for text categorization. Mach Learn 39 (2000) 135-168
11. Chan PK, Stolfo SJ. Experiments on multistrategy learning by meta-learning. In: Proceedings of the second international conference on Information and knowledge management. Washington, DC: ACM; 1993.
12. Chan PK, Stolfo SJ. Toward parallel and distributed learning by meta-learning. In: AAAI workshop in knowledge discovery in databases; 1993. p. 227-40.
13. Chan P.K., and Stolfo S.J. A comparative evaluation of voting and meta-learning on partitioned data. In: Prieditis A., and Russell S. (Eds). Proceedings of the twelfth international conference on machine learning (1995), Morgan Kaufman, San Francisco
14. Zenko B, Todorovski L, Dzeroski S, Cercone N, Lin TY, Wu X, Cercone N, Lin TY, Wu X. A comparison of stacking with meta decision trees to bagging, boosting, and stacking with other methods. In: Proceedings 2001 IEEE international conference on data mining. San Jose, CA: IEEE Comput. Soc.; 2001.
15. Liu Y.-Z., Jiang Y.-C., Liu X., and Yang S.-L. CSMC: a combination strategy for multi-class classification based on multiple association rules. Knowledge-based systems 21 (2008) 786-793
16. Daskalakis A., Kostopoulos S., Spyridonos P., Glotsos D., Ravazoula P., Kardari M., et al. Design of a multi-classifier system for discriminating benign from malignant thyroid nodules using routinely H&E-stained cytological images. Comput Biol Med 38 (2008) 196-203
17. Eom J.-.H., Kim S.-.C., and Zhang B.-.T. AptaCDSS-E: a classifier ensemble-based clinical decision support system for cardiovascular disease level prediction. Expert Syst Appl 34 (2008) 2465-2479
18. Duin RPW, Tax DMJ, Kittler J, Roli F, Kittler J, Roli F. Experiments with classifier combining rules. In: Multiple classifier systems. Proceedings of first international workshop, MCS 2000. Lecture Notes in Computer Science, vol.1857. Berlin, Germany: Springer-Verlag; 2000.
19. Tax D.M.J., van Breukelen M., Duin R.P.W., and Josef K. Combining multiple classifiers by averaging or by multiplying?. Pattern Recognit 33 (2000) 1475-1485
20. Hu R., and Damper R.I. A 'No Panacea Theorem' for classifier combination. Pattern Recognit 41 (2008) 2665-2673
21. Kuncheva L.I. Combining pattern classifiers: methods and algorithms (2004), J. Wiley, Hoboken, NJ
22. Porter M. An algorithm for suffix stripping. Program 14 (1980) 130-137
23. Salton G., and Buckley C. Term-weighting approaches in automatic text retrieval. Inf Process Manage 24 (1988) 513-523
24. Witten I.H., and Frank E. Data mining: practical machine learning tools and techniques. 2nd ed. (2005), Morgan Kaufman, Amsterdam, Boston, MA
25. Aronson AR. Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program. In: Proceedings of the AMIA symposium; 2001. p. 17-21.
26. Rao RB, Sandilya S, Niculescu RS, Germond C, Rao H. Clinical and financial outcomes analysis with existing hospital patient records. In: Proceedings of the ninth ACM SIGKDD international conference on knowledge discovery and data mining: ACM Press: New York, NY, USA; 2003. p. 416-25.
27. Kim J.J., and Park J.C. Extracting contrastive information from negation patterns in biomedical literature. ACM Trans Asian Language Inf Process 5 (2006) 44-60
28. Mutalik P.G., Deshpande A., and Nadkarni P.M. Use of general-purpose negation detection to augment concept indexing of medical documents: a quantitative study using the UMLS. J Am Med Inf Assoc 8 (2001) 598-609
29. Sibanda TC. Was the patient cured? Understanding semantic categories and their relationships in patient records. In: Electrical engineering and computer science. Master's Thesis, Cambridge, MA: Massachusetts Institute of Technology; 2006.
30. Chapman WW, Bridewell W, Hanbury P, Cooper GF, Buchanan BG. A simple algorithm for identifying negated findings and diseases in discharge summaries; J Biomed Inform 2001. p. 301-10.
31. Ryan R., and Aldebaro K. In defense of one-vs-all classification. J Mach Learn Res 5 (2004) 101-141
32. Bickel P.J., and Levina E. Some theory for Fisher's Linear Discriminant function, "naive Bayes", and some alternatives when there are many more variables than observations. Bernoulli 10 (2004) 989-1010
33. Polat K., and Güneş S. A novel hybrid intelligent method based on C4.5 decision tree classifier and one-against-all approach for multi-class classification problems. Expert Syst Appl 36 (2009) 1587-1592
34. Freund Y., and Schapire R.E. A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55 (1997) 119-139
35. Quinlan J.R. Induction of decision trees. Mach Learn 1 (1986) 81-106
36. Quinlan J.R. C4.5: programs for machine learning (1993), Morgan Kaufman, San Mateo, California
37. Shannon C.E., and Weaver W. The mathematical theory of communication (1949), University of Illinois Press, Urbana
38. Yang Y, Liu X. A re-examination of text categorization methods. In: Proceedings of SIGIR international conference on R&D in information retrieval; 1999. p. 42-9.
39. Freund Y, Schapire RE. Experiments with a new boosting algorithm. In: Machine learning: proceedings of the 13th international conference; 1996. p. 148-56.
40. Sebastiani F. Machine learning in automated text categorization. ACM Comput Surv 34 (2002) 1-47
41. Salton G., and McGill M.J. Introduction to modern information retrieval (1983), McGraw-Hill, New York
42. Grishman R, Sundheim B. Message understanding conference-6: a brief history. In: 16th conference on computational linguistics. Copenhagen, Denmark: Association for Computational Linguistics; 1996. p. 466-71.
43. Hirschman L. The evolution of evaluation: lessons from the message understanding conferences. Comput Speech Lang 12 (1998) 281-305
44. Uzuner O., Goldstein I., Luo Y., and Kohane I. Identifying patient smoking status from medical discharge records. J Am Med Inf Assoc 15 (2008) 14-24
45. Pestian JP, Brew C, Matykiewicz P, Hovermale DJ, Johnson N, Cohen KB, et al. A shared task involving multi-label classification of clinical free text. In: ACL: BioNLP. Prague: Association for Computational Linguistics; 2007. p. 97-104.
46. Standards for privacy of individually identifiable health information. Department of Health & Human Services; 2000 [45 CFR §160 and 164].