臺灣博碩士論文加值系統

With the non-stop increases in medical treatment fees, dealing with the high financial costs has become a burden for the Bureau of National Health Insurance (BNHI) of Taiwan. Thus, with the objective of reducing expenses, it introduced in 2007 the Diagnosis Related Group, a system that classifies medical cases into groups based on the International Classification of Diseases (ICD), Ninth Revision, Clinical Modification. The economic survival of a hospital relies on the reimbursements received from the BNHI, which in turn depend on the accuracy and completeness of the content of the discharge summaries as well as the correctness of their ICD codes, so it is essential to improve the quality of coding of disease classification specialists. This study develops an ICD code advisory system that performs knowledge discovery from discharge summaries with the objective of providing a means of knowledge representation and supporting disease classification specialists in the coding process. Natural language processing and information retrieval techniques based on Zipf’s Law are applied to process the content of discharge summaries, and fuzzy formal concept analysis is used to analyze and represent the relationships between the medical terms identified. The resulting advisory system analyzes discharge summaries and suggests ICD codes supported by a certainty factor that can be used as reference during the coding process.

ABSTRACT I
ACKNOWLEDGEMENTS II
LIST OF TABLES V
LIST OF FIGURES VI
CHAPTER 1. INTRODUCTION 1
1.1 BACKGROUND 1
1.2 RESEARCH MOTIVATION 1
1.3 RESEARCH OBJECTIVE 2
1.4 SCOPE AND LIMITATIONS 3
1.5 STRUCTURE OF THESIS 4
CHAPTER 2. LITERATURE REVIEW 5
2.1 DISEASE CLASSIFICATION 5
2.1.1 Disease classification system in Taiwan 6
2.1.2 Diagnosis Related Group 6
2.1.3 Related works 7
2.2 FORMAL CONCEPT ANALYSIS 7
2.2.1 Formal context 8
2.2.2 Formal concept 8
2.2.3 Concept lattice 9
2.2.4 Fuzzy formal concept analysis 11
2.2.5 Related works 12
2.3 INFORMATION RETRIEVAL 13
2.3.1 Zipf’s law 13
2.3.2 Term weighting 15
CHAPTER 3. RESEARCH METHOD 16
3.1 NATURAL LANGUAGE PROCESSING 16
3.2 TERM FILTERING AND EXPERT INTERVIEW 17
3.3 FEATURE EXTRACTION 18
3.3.1 Attribute selection 18
3.3.2 Term weighting 20
3.4 FUZZY FORMAL CONCEPT ANALYSIS 20
3.5 EVALUATION 21
CHAPTER 4. SYSTEM DEVELOPMENT AND RESULTS 24
4.1 SYSTEM DEVELOPMENT 24
4.2 RESULTS 27
4.2.1 Implication rules 28
4.2.2 ICD code prediction 29
4.2.3 System evaluation 31
CHAPTER 5. CONCLUSION 34
REFERENCES 36

Adamic, L. A., & Huberman, B. A. Zipf's law and the Internet. Glottometrics, 3, 143-150, 2002.
Baeza-Yates, R., & Ribeiro-Neto, B. Modern Information Retrieval: Addison-Wesley, 1999.
Baneyx, A., Charlet, J., & Jaulent, M.-C. Building an ontology of pulmonary diseases with natural language processing tools using textual corpora. International Journal of Medical Informatics, 76(2-3), 208-215, 2007.
Boucher-Ryan, P. d., & Bridge, D. Collaborative Recommending using Formal Concept Analysis. Knowledge-Based Systems, 19(5), 309-315, 2006.
Carpineto, C., & Romano, G. Exploiting the Potential of Concept Lattices for Information Retrieval with CREDO. Journal of Universal Computer Science, 10(8), 985-1013, 2004.
Chu, S., & Cesnik, B. Knowledge representation and retrieval using conceptual graphs and free text document self-organisation techniques. International Journal of Medical Informatics, 62(2-3), 121-133, 2001.
Diaz-Agudo, B., & Gonzalez-Calero, P. A. Formal Concept Analysis as a Support Technique for CBR. Knowledge-Based Systems, 14(3), 163-171, 2001.
Formica, A. Ontology-based concept similarity in Formal Concept Analysis. Information Sciences, 176(18), 2624-2641, 2006.
Greengrass, E. Information Retrieval: A survey, 2000.
Hulse, J. V., Khoshgoftaar, T. M., & Napolitano, A. Experimental perspectives on learning from imbalanced data. Proceedings of the 24th international conference on Machine learning 2007.
Japkowicz, N., & Stephen, S. The class imbalance problem: A systematic study. Intelligent Data Analysis, 6(5), 429-449, 2002.
Jiang, G., Ogasawara, K., Endoh, A., & Sakurai, T. Context-based ontology building support in clinical domains using formal concept analysis. International Journal of Medical Informatics, 71(1), 71-81, 2003.
Kalda, J., Sakki, M., Vainu, M., & Laan, M. Zipf's law in human heartbeat dynamics. arXiv.org, physics/0110075, 2001.
Krashakov, S. A., Teslyuk, A. B., & Shchur, L. N. On the universality of rank distributions of website popularity. Computer Networks, 50, 1769-1780, 2006.
Leister, J. E., & Stausberg, J. Comparison of cost accounting methods from different DRG systems and their effect on health care quality. Health Policy 74(1), 46-55, 2005.
Luhn, H. P. The Automatic Creation of Literature Abstracts. IBM Journal of Research and Development, 2(2), 157-165, 1958.
Muller, M. L., Burkle, T., Irps, S., Roeder, N., & Prokosch, H.-U. The diagnosis related groups enhanced electronic medical record. International Journal of Medical Informatics, 70(3), 221-228, 2003.
Priss, U. Formal Concept Analysis in Information Science. In B. Cronin (Ed.), Annual Review of Information Science and Technology (Vol. 40, pp. 521-543), 2006.
Salton, G., & Buckley, C. Term-Weighting Approaches in Automatic Text Retrieval. Information Processing and Management, 24(5), 513-523 1988.
Shortliffe, E. H., & Buchanan, B. G. A model of inexact reasoning in medicine. Mathematical Biosciences, 23(3-4), 351-379, 1975.
Siciliani, L. Selection of treatment under prospective payment systems in the hospital sector. Journal of Health Economics, 25(3), 479-499, 2006.
Steinbusch, P. J. M., Oostenbrink, J. B., Zuurbier, J. J., & Schaepkens, F. J. M. The risk of upcoding in casemix systems: A comparative study. Health Policy 81(2-3), 289-299, 2007.
Surjan, G. Questions on validity of International Classification of Diseases-coded diagnoses. International Journal of Medical Informatics, 54(2), 77-95, 1999.
Sutherland, J. M., & Botz, C. K. The effect of misclassification errors on case mix measurement. Health Policy, 79(2-3), 195-202, 2006.
Tho, Q. T., Hui, S. C., & Cao, T. H. A Fuzzy FCA Approach for Citation-based Document Retrieval. Proceedings of the 2004 IEEE Conference on Cybernetics and Intelligent Systems, Singapore, 2004.
Tho, Q. T., Hui, S. C., Fong, A. C. M., & Cao, T. H. Automatic Fuzzy Ontology Generation for Semantic Web. IEEE Transactions on Data and Knowledge Engineering, 18(6), 842-856, 2006.
Weng, S.-S., Tsai, H.-J., Liu, S.-C., & Hsu, C.-H. Ontology construction for information classification . Expert Systems with Applications, 31(1), 1-12, 2006.
Wille, R. Restructuring lattice theory: an approach based on hierarchies of concepts. In I. Rival (Ed.), Ordered sets (pp. 445-470). Reidel: Dordrecht-Boston, 1982.
Wolff, K. E. A first course in formal concept analysis - how to understand line diagrams. 7th Conference on the Scientific Use of Statistical Software, Stuttgart, 1993.
Yevtushenko, S. A. System of data analysis "Concept Explorer". Proceedings of the 7th national conference on Artificial Intelligence KII-2000, Russia, 2000.
Zipf, G. K. Selective studies and the Principle of Relative Frequency in Language. Cambridge, Massachusetts: MIT Press, 1932.