Service-Oriented Medical System for Supporting Decisions with Missing and Imbalanced Data

IEEE Journal of Biomedical and Health Informatics

Volumn 18, Issue 5, 2014, Pages 1533-1540

  Zieba, Maciej ^a  

^a WROCLAW UNIVERSITY OF TECHNOLOGY   (Poland)

Author keywords

Data models; Indexes; Informatics; Knowledge based systems; Medical diagnostic imaging; Training; Web services

Indexed keywords

DATA STRUCTURES; DIAGNOSIS; INFORMATICS; KNOWLEDGE BASED SYSTEMS; MEDICAL EDUCATION; MEDICAL IMAGING; MEDICAL PROBLEMS; PERSONNEL TRAINING; SUPPORT VECTOR MACHINES; VECTOR SPACES; WEB SERVICES; WEBSITES;

COST-SENSITIVE SUPPORT VECTOR MACHINES; DECISION SYSTEMS; DIAGNOSTIC PROBLEM; IMBALANCED CLASS; INDEXES; MEDICAL DIAGNOSTIC IMAGING; MEDICAL SYSTEMS; SERVICE ORIENTED;

LEARNING SYSTEMS;

HUMAN; HUMAN EXPERIMENT; MODEL; SUPPORT VECTOR MACHINE; DECISION SUPPORT SYSTEM; ELECTRONIC MEDICAL RECORD; MEDICAL INFORMATICS; THEORETICAL MODEL;

DECISION SUPPORT SYSTEMS, CLINICAL; ELECTRONIC HEALTH RECORDS; HUMANS; MEDICAL INFORMATICS COMPUTING; MODELS, THEORETICAL; SUPPORT VECTOR MACHINES;

EID: 84927172908     PISSN: 21682194     EISSN: 21682208     Source Type: Journal    
DOI: 10.1109/JBHI.2014.2322281     Document Type: Article

References (18)

1. P.M. Nadkarni and R. A. Miller, "Service-oriented architecture in medical software: Promises and perils," J. Amer. Med. Informat. Assoc., vol. 14, no. 2, pp. 244-246, 2007.
2. P. ŚwiĄtek, P. Stelmach,A. Prusiewicz, andK. Juszczyszyn, "Service composition in knowledge-based SOA systems," New Generation Comput., vol. 30, no. 2-3, pp. 165-188, 2012.
3. A. Wright and D. F. Sittig, "SANDS: A service-oriented architecture for clinical decision support in a national health information network," J. Biomed. Informat., vol. 41, no. 6, pp. 962-981, 2008.
4. S. W. Tu, J. R. Campbell, J. Glasgow, M. A. Nyman, R. McClure, J. Mc-Clay, C. Parker, K. M. Hrabak, D. Berg, T. Weida, J. G. Mansfield, M. A. Musen, and R. M. Abarbanel, "The SAGE guideline model: Achievements and overview," J. Amer. Med. Informat. Assoc., vol. 14, no. 5, pp. 589-598, 2007.
5. K. Brzostowski, J. Drapła, A. Grzech, and P. ŚwiĄtek, "Adaptive decision support system for automatic physical effort plan generation: Data-driven approach," Cybern. Syst., vol. 44, no. 2-3, pp. 204-221, 2013.
6. A. Grzech, P. ŚwiĄtek, and P. Rygielski, "Dynamic resources allocation for delivery of personalized services," in Software Services for e-World. New York, NY, USA: Springer, 2010, pp. 17-28.
7. P. J. Garcia-Laencina, J. L. Sancho-Gomez, and A. R. Figueiras-Vidal, "Pattern classification with missing data: A review, " Neural Comput. Appl., vol. 19, no. 2, pp. 263-282, Sep. 2009.
8. L. Garg, J. Dauwels, A. Earnest, and K. P. Leong, "Tensor based methods for handling missing data in quality-of-life questionnaires," IEEE J. Biomed. Health Informat., vol. PP, no. 99, p. 1, doi: 10.1109/JBHI.2013.2288803.
9. L. Nanni, A. Lumini, and S. Brahnam, "A classifier ensemble approach for the missing feature problem," Artif. Intell. Med., vol. 55, no. 1, pp. 37-50, 2012.
10. H. He, and E. A. Garcia, "Learning from imbalanced data," IEEE Trans. Knowl. Data Eng., vol. 21, no. 9, pp. 1263-1284, Sep. 2009.
11. N.V. Chawla, K.W. Bowyer, and L. O. Hall, "SMOTE: Synthetic minority over-sampling technique," J. Artif. Intell. Res., vol. 16, pp. 321-357, 2002.
12. N. V. Chawla, A. Lazarevic, L. O. Hall, and K. Bowyer, "SMOTEBoost: Improving prediction of the minority class in boosting," in Proc. Principles Knowl. Discovery Databases, 2003, pp. 107-119.
13. D. Tao, X. Tang, X. Li, and X. Wu, "Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval," IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 7, pp. 1088-1099, Jul. 2006.
14. M. Kukar, and I. Kononenko, "Cost-sensitive learning with neural networks," in Proc. 13th Eur. Conf. Artif. Intell., 1998, pp. 445-449.
15. M. Ziȩba, J. M. Tomczak, J. ŚwiĄtek, and M. Lubicz, "Boosted SVM for extracting rules from imbalanced data in application to prediction of the post-operative life expectancy in the lung cancer patients," Appl. Soft Comput., vol. 14, Part A, pp. 99-108, 2014.
16. J. M. Tomczak and A. Gonczarek, "Decision rules extraction from data stream in the presence of changing context for diabetes treatment," Knowl. Inf. Syst., vol. 34, no. 3, pp. 521-546, 2013.
17. E. Strumbelj, Z. Bosníc, I. Kononenko, B. Zakotnik, and C. G. Kuhar, "Explanation and reliability of prediction models. The case of breast cancer recurrence," Knowl. Inf. Syst., vol. 24, no. 2, pp. 305-324, 2010.
18. K. Bache, and M. Lichman. (2013). UCI machine learning repository [Online]. Available: http://archive.ics.uci.edu/ml