Automatic Prediction of Rheumatoid Arthritis Disease Activity from the Electronic Medical Records

PLoS ONE

Volumn 8, Issue 8, 2013, Pages

  Lin, Chen ^a   Karlson, Elizabeth W ^b,c   Canhao, Helena ^d   Miller, Timothy A ^a,c   Dligach, Dmitriy ^a,c   Chen, Pei Jun ^a   Perez, Raul Natanael Guzman ^b   Shen, Yuanyan ^e   Weinblatt, Michael E ^b,c   Shadick, Nancy A ^b,c   Plenge, Robert M ^b,c   Savova, Guergana K ^a,c  

^a BOSTON CHILDREN S HOSPITAL   (United States)

^b BRIGHAM AND WOMEN S HOSPITAL   (United States)

^c HARVARD MEDICAL SCHOOL   (United States)

^d INSTITUTO DE MEDICINA MOLECULAR   (Portugal)

^e HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; CONTROLLED STUDY; DISEASE ACTIVITY; ELECTRONIC MEDICAL RECORD; GENETIC ASSOCIATION; HUMAN; KERNEL METHOD; LEARNING ALGORITHM; MACHINE LEARNING; MAJOR CLINICAL STUDY; PATIENT IDENTIFICATION; PHARMACOGENETICS; PREDICTION; RECEIVER OPERATING CHARACTERISTIC; RHEUMATOID ARTHRITIS; SUPPORT VECTOR MACHINE; TASK PERFORMANCE; UNIFIED MEDICAL LANGUAGE SYSTEM;

ANTIRHEUMATIC AGENTS; ARTHRITIS, RHEUMATOID; ARTIFICIAL INTELLIGENCE; DATA MINING; DISEASE PROGRESSION; ELECTRONIC HEALTH RECORDS; HUMANS; ROC CURVE; SUPPORT VECTOR MACHINES;

EID: 84881569536     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0069932     Document Type: Article

References (51)

1. Orme ME, Macgilchrist KS, Mitchell S, Spurden D, Bird A, (2012) Systematic review and network meta-analysis of combination and monotherapy treatments in disease-modifying antirheumatic drug-experienced patients with rheumatoid arthritis: analysis of American College of Rheumatology criteria scores 20, 50, and 70. Biologics 6: 429-464.
2. Singh JA, Cameron DR, (2012) Summary of AHRQ's comparative effectiveness review of drug therapy for rheumatoid arthritis (RA) in adults-an update. J Manag Care Pharm 18: S1-18.
3. Schmitz S, Adams R, Walsh CD, Barry M, FitzGerald O, (2012) A mixed treatment comparison of the efficacy of anti-TNF agents in rheumatoid arthritis for methotrexate non-responders demonstrates differences between treatments: a Bayesian approach. Ann Rheum Dis 71: 225-230.
4. Pierreisnard A, Issa N, Barnetche T, Richez C, Schaeverbeke T, (2012) Meta-analysis of clinical and radiological efficacy of biologics in rheumatoid arthritis patients naive or inadequately responsive to methotrexate. Joint Bone Spine.
5. Singh JA, Furst DE, Bharat A, Curtis JR, Kavanaugh AF, et al. (2012) 2012 update of the 2008 American College of Rheumatology recommendations for the use of disease-modifying antirheumatic drugs and biologic agents in the treatment of rheumatoid arthritis. Arthritis Care Res (Hoboken) 64: 625-639.
6. Collier D, Grant R, Estey G, Surrao D, Chueh HC, et al. (2009) Physician ability to assess rheumatoid arthritis disease activity using an electronic medical record-based disease activity calculator. Arthritis Rheum 61: 495-500.
7. Iannaccone CK, Lee YC, Cui J, Frits ML, Glass RJ, et al. (2011) Using genetic and clinical data to understand response to disease-modifying anti-rheumatic drug therapy: data from the Brigham and Women's Hospital Rheumatoid Arthritis Sequential Study. Rheumatology (Oxford) 50: 40-46.
8. Van der Heijde D, van't Hof M, van Riel P, Theunisse L, Lubberts E, et al. (1990) Judging disease activity in clinical practice in rheumatoid arthritis: first step in the development of a disease activity score. Ann Rheum Dis 49: 916-920.
9. Van der Heijde D, van't Hof M, van Riel P, van de Putte L, (1993) Development of a disease activity score based on judgment in clinical practice by rheumatologists. J Rheumatol 20: 579-581.
10. Hripcsak G, Friedman C, Alderson P, DuMouchel W, Johnson S, et al. (1995) Unlocking clinical data from narrative reports: a study of natural language processing. Ann Intern Med 122: 681-688.
11. Demner-Fushman D, Chapman W, McDonald C, (2009) What can natural language processing do for clinical decision support? J Biomed Inform 42: 760-772.
12. Meyestre S, Haug P, (2006) Natural language processing to extract medical problems from electronic clinical documents: performance evaluation. J Biomed Inform 39: 589-599.
13. Fiszman M, Chapman W, Evans SR, Haug PJ, (1999) Automatic identification of pneumonia related concepts on chest x-ray reports. Proc AMIA Symp pp. 67-71.
14. Fiszman M, Chapman W, Aronsky D, Evans RS, et al. (2000) Automatic detection of acute bacterial pneumonia from chest x-ray reports. J Am Med Inform Assoc 7: 593-604.
15. Liao K, Cai T, Gainer V, Goryachev S, Zeng-Thretler Q, et al. (2010) Electronic Medical Records for Discovery Research in Rheumatoid Arthritis. Arthritis Care & Research 62: 1120-1127.
16. Uzuner O, (2009) Recognizing obesity and comorbidities in sparse data. J Am Med Inform Assoc 16: 561-570.
17. Aronson A, (2001) Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program. Proc AMIA Symp pp. 17-21.
18. Unified Medical Language System (UMLS). Available: http://www.nlm.nih.gov/research/umls/. Accessed 2013 Jul 9.
19. Jurafsky D, Martin J (2009) Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition. Upper Saddle River, NJ: Pearson Prentice Hall.
20. Garla V, Brandt C, (2012) Ontology-Guided Feature Engineering for Clinical Text Classification. Journal of Biomedical Infromatics (in press).
21. Bejan C, Xia F, Vanderwende L, Wurfel MM, Yetisgen-Yildiz M, (2012) Pneumonia identification using statistical feature selection. J Am Med Inform Assoc.
22. Joachims T, (1998) Text categorization with support vector machines: Learning with many relevant features. Machine Learning: ECML-98 1398/1998: 137-142.
23. Ma S, Huang J, (2008) Penalized feature selection and classification in bioinformatics. Briefings in Bioinformatics 9: 392-403.
24. Sayes Y, Inza I, Larranaga P, (2007) A review of feature selection techniques in bioinformatics. Bioinformatics 23: 2507-2517.
25. Zhao Z, Morstatter F, Sharma S, Alelyani S, Anand A, et al. (2010) Advancing Feature Selection Research - ASU Feature Selection Repository. TR-10-007.
26. Yang Y, Pedersen J, (1997) A comparative study on feature selection in text categorization. Proc Int'l Conf on Machine Learning (ICML) pp. 412-420.
27. Lin C, Miller T, Dligach D, Savova G (2012) Feature Engineering and Selection for Rheumatoid Arthritis Disease Activity Classification Using Electronic Medical Records. ICML Workshop on Machine Learning for Clinical Data Analysis. Edingburgh, UK.
28. Lin C, Miller T, Dligach D, Plenge RM, Karlson EW, et al. (2012) Maximal Information Coefficient for Feature Selection for Clinical Document Classification (extended abstract). ICML Workshop on Machine Learning for Clinical Data. Edingburgh, UK.
29. Carroll R, Thompson W, Eyler A, Mandelin AM, Cai T, et al. (2012) Portability of an algorithm to identify rheumatoid arthritis in electronic health records. J Am Med Inform Assoc 19: e162-e169.
30. van Gestel A, Prevoo M, van't Hof M, van Rijswijk MH, van de Putte LB, et al. (1996) Development and validation of the European League Against Rheumatism response criteria for rheumatoid arthritis. Comparison with the preliminary American College of Rheumatology and the World Health Organization/International League Against Rheumatism Criteria. Arthritis Rheum 39: 34-40.
31. Hripcsak G, Rothschild AS, (2005) Agreement, the f-measure, and reliability in information retrieval. J Am Med Inform Assoc 12: 296-298.
32. Apache Clinical Text Analysis and Knowledge Extraction System (cTAKES). Available: http://ctakes.apache.org. Accessed 2013 Jul 9.
33. Savova G, Masanz J, Ogren P, Zheng J, Sohn S, et al. (2010) Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications. J Am Med Inform Assoc 17: 507-513.
34. Greenwood P, Nikulin M (1996) A guide to chi-squared testing. New York: John Wiley & Sons.
35. Hall M (1999) Correlation-based Feature Selection for Machine Learning. Hamilton, NewZealand: Dept. of Computer Science, University of Waikato.
36. Goldberg D (1989) Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley Pub. Co.
37. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, et al. (2009) The WEKA Data Mining Software: An Update;. SIGKDD Explorations 11: 10-18.
38. Smolen J, Breedveld F, Eberl G, Jones I, Leeming M, et al. (1995) Validity and reliability of the twenty-eight-joint count for the assessment of rheumatoid arthritis activity. Arthritis Rheum 38: 38-43.
39. Beigman E, Klebanov B (2009) Learning with Annotation Noise; 2-7 August 2009; Suntec, Singapore. pp. 280-287.
40. le Cessie S, van Houwelingen J, (1992) Ridge Estimators in Logistic Regression. Applied Statistics 41: 191-201.
41. John G, Langley P (1995) Estimating Continuous Distributions in Bayesian Classifiers. Eleventh Conference on Uncertainty in Artificial Intelligence. San Mateo. pp. 338-345.
42. Witten I, Frank E (2005) Data Mining: Practical Machine Learning Tools and Techniques: Morgan Kaufmann.
43. Platt J (1998) Fast Training of Support Vector Machines using Sequential Minimal Optimization. In: B. Schoelkopf CB, ASmola, editor. Advances in Kernel Methods- Support Vector Learning.
44. Keerthi S, Shevade S, Bhattacharyya C, Murthy K, (2001) Improvements to Platt's SMO Algorithm for SVM Classifier Design. Neural Computation 13: 637-649.
45. Uestuen B, Melssen W, Buydens L, (2006) Facilitating the application of Support Vector Regression by using a universal Pearson VII function based kernel. Chemometrics and Intelligent Laboratory Systems 81: 29-40.
46. Hastie T, Tibshirani R, Friedman JH (2009) The elements of statistical learning: data mining, inference, and prediction. New York: Springer. xxii, 745 p. p.
47. DeLong E, DeLong D, Clarke-Pearson D, (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44: 837-845.
48. Quinlan JR (1993) C4.5: programs for machine learning. San Mateo, Calif.: Morgan Kaufmann Publishers. x, 302 p. p.
49. Bishara A, Hittner J, (2012) Testing the Significance of a Correlation With Nonnormal Data: Comparison of Pearson, Spearman, Transformation, and Resampling Approaches. Psychol Methods.
50. Mooijaart A, Satorra A, (2009) On insensitivity of the chi-square model test to non-linear misspecification in structural equation models. Psychometrika 74: 443-455.
51. Reshef D, Reshef Y, Finucane H, Grossman S, McVean G, et al. (2011) Detecting novel associations in large data sets. Science 334: 1518-1524.