Identification and Progression of Heart Disease Risk Factors in Diabetic Patients from Longitudinal Electronic Health Records

BioMed Research International

Volumn 2015, Issue , 2015, Pages

  Jonnagaddala, Jitendra ^a,b   Liaw, Siaw Teng ^a   Ray, Pradeep ^b   Kumar, Manish ^a   Dai, Hong Jie ^c   Hsu, Chien Yeh ^d,e  

^a UNIVERSITY OF NEW SOUTH WALES   (Australia)

^b UNIVERSITY OF NEW SOUTH WALES   (Australia)

^c NATIONAL TAITUNG UNIVERSITY   (Taiwan)

^d TAIPEI MEDICAL UNIVERSITY   (Taiwan)

^e NATIONAL TAIPEI UNIVERSITY OF NURSING AND HEALTH SCIENCES   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

2,4 THIAZOLIDINEDIONE DERIVATIVE; ACETYLSALICYLIC ACID; AMYLIN; ANGIOTENSIN RECEPTOR ANTAGONIST; BETA ADRENERGIC RECEPTOR BLOCKING AGENT; CALCIUM CHANNEL BLOCKING AGENT; CHOLESTEROL; DIPEPTIDYL CARBOXYPEPTIDASE INHIBITOR; DIPEPTIDYL PEPTIDASE IV INHIBITOR; EZETIMIBE; GLUCAGON LIKE PEPTIDE 1 RECEPTOR AGONIST; GLUCOSE; HEMOGLOBIN A1C; HYDROXYMETHYLGLUTARYL COENZYME A REDUCTASE INHIBITOR; INSULIN; MEGLITINIDE; METFORMIN; NICOTINIC ACID; NITRIC ACID DERIVATIVE; THIAZIDE DIURETIC AGENT; THIENOPYRIDINE DERIVATIVE;

ARTICLE; CARDIOVASCULAR RISK; CHOLESTEROL BLOOD LEVEL; DIABETES MELLITUS; DIABETIC PATIENT; DISEASE COURSE; ELECTRONIC MEDICAL RECORD; FAMILY HISTORY; GLUCOSE BLOOD LEVEL; HEART DISEASE; HEMOGLOBIN BLOOD LEVEL; HUMAN; HYPERLIPIDEMIA; HYPERTENSION; MACHINE LEARNING; OBESITY; RISK ASSESSMENT; SMOKING; WAIST CIRCUMFERENCE; ALGORITHM; ARTIFICIAL INTELLIGENCE; COMPUTER SYSTEM; DATA MINING; DIABETES COMPLICATIONS; ELECTRONIC HEALTH RECORD; HEART DISEASES; PATHOLOGY; RISK FACTOR;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SYSTEMS; DATA MINING; DIABETES COMPLICATIONS; ELECTRONIC HEALTH RECORDS; HEART DISEASES; HUMANS; RISK FACTORS;

EID: 84941073116     PISSN: 23146133     EISSN: 23146141     Source Type: Journal    
DOI: 10.1155/2015/636371     Document Type: Article

References (31)

1. D. Yach, C. Hawkes, C. L. Gould, and K. J. Hofman, "The global burden of chronic diseases: overcoming impediments to prevention and control, " The Journal of the American Medical Association, vol. 291, no. 21, pp. 2616-2622, 2004.
2. J. Mackay and G. A. Mensah, The Atlas of Heart Disease and Stroke, WHO, 2004.
3. L. Ohno-Machado, P. Nadkarni, and K. Johnson, "Natural language processing: Algorithms and tools to extract computable information from EHRs and from the biomedical literature, " Journal of theAmericanMedical InformaticsAssociation, vol. 20, no. 5, p. 805, 2013.
4. J. Jonnagaddala, H.-J. Dai, P. Ray, and S.-T. Liaw, "Mining electronic health records to guide and support good clinical decision support systems, " in Improving Health Management through Clinical Decision Support Systems, J. Moon and M. P. Galea, Eds., IGI-Global, 2015.
5. Y.-C. Chang, H.-J. Dai, J. C.-Y. Wu, J.-M. Chen, R. T.-H. Tsai, and W.-L. Hsu, "TEMPTING system: A hybrid method of rule andmachine learning for temporal relation extraction in patient discharge summaries, " Journal of Biomedical Informatics, vol. 46, supplement, pp. S54-S62, 2013.
6. M. Fiszman, G. Rosemblat, C. B. Ahlers, and T. C. Rindflesch, "Identifying risk factors for metabolic syndrome in biomedical text, " AMIA Annual Symposium Proceedings, vol. 2007, pp. 249-253, 2007.
7. S. Goryachev, H. Kim, and Q. Zeng-Treitler, "Identification and extraction of family history information from clinical reports, " AMIA Annual Symposium Proceedings, vol. 2008, pp. 247-251, 2008.
8. J. Jonnagaddala, S. Liaw, P. Rayb, M. Kumarc, and H. Dai, "TMUNSW: identification of disorders and normalization to SNOMED-CT terminology in unstructured clinical notes, " in Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 15), Association for Computational Linguistics, 2015.
9. J. Jonnagaddala, S.-T. Liaw, P. Ray, M. Kumar, and H.-J. Dai, "HTNSystem: hypertension information extraction system for unstructured clinical notes, " in Technologies and Applications of Artificial Intelligence, S.-M. Cheng and M.-Y. Day, Eds., vol. 8916 of LectureNotes in Computer Science, pp. 219-227, Springer, 2014.
10. S. Kraus, C. Blake, and S. L. West, "Information extraction from medical notes, " in Medinfo 2007: Proceedings of the 12th World Congress on Health (Medical) Informatics. Building Sustainable Health Systems, IOS Press, 2007.
11. G. K. Savova, J. J. Masanz, P. V. Ogren et al., "Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): Architecture, component evaluation and applications, " Journal of the American Medical Informatics Association, vol. 17, no. 5, pp. 507-513, 2010.
12. H. Xu, S. P. Stenner, S. Doan, K. B. Johnson, L. R. Waitman, and J. C. Denny, "MedEx: A medication information extraction system for clinical narratives, " Journal of the American Medical Informatics Association, vol. 17, no. 1, pp. 19-24, 2010.
13. Q. T. Zeng, S. Goryachev, S. Weiss, M. Sordo, S. N. Murphy, and R. Lazarus, "Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language processing system, " BMC Medical Informatics and Decision Making, vol. 6, article 30, 2006.
14. R. J. Byrd, S. R. Steinhubl, J. Sun, S. Ebadollahi, and W. F. Stewart, "Automatic identification of heart failure diagnostic criteria, using text analysis of clinical notes from electronic health records, " International Journal of Medical Informatics, vol. 83, no. 12, pp. 983-992, 2014.
15. J. Jonnagaddala, S. T. Liaw, P. Ray, M. Kumar, N. W. Chang, and H. J. Dai, "Coronary artery disease risk assessment from unstructured electronic health records using text mining, " Journal of Biomedical Informatics, In press.
16. G. K. Savova, P. V. Ogren, P. H. Duffy, J. D. Buntrock, and C. G. Chute, "Mayo clinic NLP system for patient smoking status identification, " Journal of the American Medical Informatics Association, vol. 15, no. 1, pp. 25-28, 2008.
17. N.-W. Chang, H.-J. Dai, J. Jonnagaddala, C.-W. Chen, and W.-L. Hsu, "A context-Aware approach for progression tracking of medical concepts in electronic medical records, " Journal of Biomedical Informatics, In press.
18. A. Stubbs, C. Kotfila, H. Xu, and O. Uzuner, "Practical applications for NLP in clinical research: The 2014 i2b2/UTHealth shared tasks, " in Proceedings of the i2b2 Shared Task and Workshop Challenges inNatural Language Processing for Clinical Data, 2014.
19. A. Stubbs and O. Uzuner, "Annotating risk factors for heart disease in clinical narratives for diabetic patients, " Journal of Biomedical Informatics, 2015.
20. A. R. Aronson and F.-M. Lang, "An overview of MetaMap: historical perspective and recent advances, " Journal of the AmericanMedical InformaticsAssociation, vol. 17, no. 3, pp. 229-236, 2010.
21. A. R. Aronson, "Effective mapping of biomedical text to the UMLS Metathesaurus: The MetaMap program, " Proceedings of the AMIA Symposium, pp. 17-21, 2001.
22. G. H. John and P. Langley, "Estimating continuous distributions in Bayesian classifiers, " in Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann Publishers, 1995.
23. M. A. Hall, Correlation-Based Feature Selection for Machine Learning, The University ofWaikato, 1999.
24. H.-J. Dai, S. Syed-Abdul, C.-W. Chen, and C.-C. Wu, "Recognition and evaluation of clinical section headings in clinical documents using token-based formulation with conditional random fields, " BioMed Research International. In press.
25. C. Sutton and A. McCallum, "An introduction to conditional random fields for relational learning, " in Introduction to Statistical Relational Learning, L. Getoor and B. Taskar, Eds., pp. 93-128, MIT Press, 2006.
26. Y. Yang and X. Liu, "A re-examination of text categorization methods, " in Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 99), pp. 42-49, ACM, Berkeley, Calif, USA, August 1999.
27. L. Song, D. Li, X. Zeng, Y. Wu, L. Guo, and Q. Zou, "nDNA-prot: identification of DNA-binding proteins based on unbalanced classification, " BMC Bioinformatics, vol. 15, no. 1, article 298, 2014.
28. H. Harkema, J.N. Dowling, T. Thornblade, and W.W. Chapman, "ConText: An algorithm for determining negation, experiencer, and temporal status fromclinical reports, " Journal of Biomedical Informatics, vol. 42, no. 5, pp. 839-851, 2009.
29. Y. Wu, J. C. Denny, S. T. Rosenbloom, R.A. Miller, D. A. Giuse, and H. Xu, "A comparative study of current clinical natural language processing systems on handling abbreviations in discharge summaries, " AMIA Annual Symposium Proceedings, vol. 2012, pp. 997-1003, 2012.
30. H. Xu, P. D. Stetson, and C. Friedman, "A study of abbreviations in clinical notes, " AMIA Annual Symposium Proceedings, vol. 2007, pp. 821-825, 2007.
31. J. Patrick, M. Sabbagh, S. Jain, and H. Zheng, "Spelling correction in clinical notes with emphasis on first suggestion accuracy, " in Proceedings of the 2nd Workshop on Building and Evaluating Resources for Biomedical Text Mining, March 2010.