當人們身體出現某些疾病徵候、或對某些疾病相關資訊感興趣時，其經常透過網路搜尋欲瞭解疾病徵候所對應之相關資訊。然而，人們透過網路蒐集疾病相關資訊之內容繁雜與數量眾多，其篩選欲瞭解之疾病資訊之過程經常耗費許多時間與精力；另一方面，人們所蒐集之疾病資訊並未呈現其之參考性，導致人們需反覆比對疾病資訊之內容，以判斷各疾病資訊之內容的可信度，以至於人們無法準確與快速掌握疾病相關資訊。於前述過程中，人們往往需耗費時間搜尋、比對、彙整疾病資訊。
人們由網路所蒐集之疾病相關資訊內容繁雜且不全面，其往往需耗費時間搜尋、比對、彙整疾病資訊。為解決上述之問題，本研究乃發展一套「以文件內容為基礎之疾病文件推論與整合」模式，此模式乃整合多個疾病資訊並提供各細節資訊之參考價值，其可呈現各疾病類別之完整資訊，人們可依據本研究所發展的模式快速取得完整之疾病資訊，減少人們於搜尋、比對、彙整疾病資訊之時間。
為解決上述之問題，本研究乃先蒐集老年人常見疾病之文件，並解析此些文件之表達特徵。之後，本研究乃根據此些表達特徵之結果發展一套「以文件內容為基礎之疾病文件推論與整合」模式。而「以文件內容為基礎之疾病文件推論與整合」模式可分為「疾病文件特徵屬性擷取」、「疾病文件分類」、「疾病文件參考價值推論」、「疾病文件特徵屬性整合與呈現其細項參考價值」等四個階段。其中，「疾病文件屬性擷取」階段主要乃擷取疾病文件所具備之重要特徵屬性；「疾病文件分類」階段乃利用K Nearest Neighbor分類法將第一階段所結構化之疾病文件歸屬至所對應之疾病類別；「疾病文件參考價值推論」階段乃以粒子群演算法推論各疾病文件之參考價值，以利後續推論各特徵屬性內容之參考價值；「疾病文件特徵屬性整合與呈現其細項參考價值」階段乃以先前階段所取得之結果整合各疾病類別之相同特徵屬性的細項屬性內容與呈現各特徵屬性之細項屬性內容的參考價值。
整體而言，此模式可整合多個疾病資訊並提供各細節資訊之參考價值，其可呈現各疾病類別之完整資訊，人們可依據本研究所發展的模式快速取得完整之疾病資訊，減少人們於搜尋、比對、彙整疾病資訊之時間，而此結果亦可作為人們預防疾病相關措施之參考。

As people experience physical discomfort, they tend to search for the related medical information through the Internet. However, the searched information might not present the all aspects of the situation. As a result, people have to spend a lot of time in searching and reading the related medical information in order to collect the completed information. To solve the problem, this study collected the documents of common diseases for the elderly and analyzed the key features of these documents. Based on the analysis, the study develops a model "for aggregation of disease documents. As a whole, based on the proposed model, people can efficiently acquire the completed medical information and the time required to search for the medical information can be reduced.

摘要 I
ABSTRACT II
目錄 III
圖目錄 V
表目錄 VII
第一章、研究背景 1
1.1 研究動機與目的 1
1.2 研究步驟 4
1.3 研究定位 7
第二章、文獻回顧 10
2.1 疾病文件特徵擷取 10
2.1.1 以統計方法為基礎之疾病文件特徵擷取 10
2.1.2 以時頻分析方法為基礎之疾病文件特徵擷取 15
2.1.3 以詞庫比對法為基礎之疾病文件特徵擷取 19
2.2 疾病文件分類 20
2.2.1 依監督式學習演算法分類疾病文件 20
2.2.2 依深度學習演算法分類疾病文件 28
2.2.3 依特定方法分類疾病文件 30
2.3 疾病文件摘要 32
2.3.1 單一疾病文件之內容摘要 32
2.3.2 多份疾病文件之內容摘要 42
2.4 小結 46
第三章、以文件內容為基礎之疾病文件參考價值推論與整合模式 48
3.1 現行疾病文件內容解析 49
3.1.1 疾病文件之特徵屬性解析 50
3.1.2 疾病文件之特徵屬性表達方式解析 53
3.2 疾病文件特徵屬性擷取 68
3.3 疾病文件分類 87
3.4 疾病文件參考價值推論 91
3.5 疾病文件特徵屬性整合與呈現其細項參考價值 105
第四章、績效驗證與分析 113
4.1 模式驗證方式說明 113
4.2 驗證結果分析 117
第五章、結論與未來展望 136
5.1 論文總結 136
5.2 未來發展 138
參考文獻 140
附錄A、現行疾病文件內容解析前置作業 146
附錄B、驗證資料說明 228
附錄C、模式於第二階段各週期之績效驗證結果 386

1. Amin, H. U., Malik, A. S., Kamel, N. and Hussain, N., 2016, “A Novel Approach Based on Data Redundancy for Feature Extraction of EEG Signals,” Brain Topography, Vol. 29, pp 207-217.
2. Bekir, P. and Alper, K. U., 2015, “Classification of Medical Documents According to Diseases,” Signal Processing and Communications Applications Conference, IEEE, Turkey, DOI: 10.1109.
3. Brignole, M., Moya, A., Menozzi, C. and Sutton, R., 2005, “Proposed Electrocardiographic Classification of Spontaneous Syncope Documented by an Implantable Loop Recorder,” EP Europace, Vol. 7, pp 14-18.
4. Chapman, B. E., Lee, S., Kang, H. P. and Chapman, W. W., 2011, “Document-Level Classification of CT Pulmonary Angiography Reports Based on an Extension of the ConText Algorithm,” Journal of Biomedical Informatics, Vol. 44, pp 244-253.
5. Chen, L. and Nayak, R., 2015, “Injury Narrative Text Classification Using Factorization Model,” BMC Medical Informatics and Decision Making, Vol. 15, S5.
6. Chen, P. and Verma, R., 2006, “A Query-based Medical Information Summarization System Using Ontology Knowledge,” IEEE International Symposium on Computer-Based Medical Systems, USA, pp 37-42.
7. Diomaiuta, C., Mercorella, M. and Ciampi, M., 2017, “A Novel System for the Automatic Extraction of a Patient Problem Summary,” Symposium on Computers and Communications, pp. 182-186.
8. Ebadollahi, S. and Chang, S. F., 2002, “Echocardiogram Videos: Summarization, Temporal Segmentation and Browsing,” International Conference on Image Processing, IEEE, USA, DOI:10.1109.
9. Elhadad, N. and McKeown, K. R., 2001, “Towards Generating Patient Specific Summaries of Medical Articles,” Proceedings of NAACL-2001 Workshop, DOI: 10.3115.
10. Eltoukhy, M. M., Samir, B. B. and Faye, I., 2012, “A Statistical Based Feature Extraction Method for Breast Cancer Diagnosis in Digital Mammogram Using Multiresolution Representation,” Computers in Biology and Medicine, Vol. 42, pp 123-128.
11. Ergen, B. and Baykara, M., 2014, “Texture Based Feature Extraction Methods for Content Based Medical Image Retrieval Systems,” Bio-Medical Materials and Engineering, Vol. 24, pp 3055-3062.
12. Feblowitz, J. C., Wright, A. and Samal, L., 2011, “Summarization of Clinical Information: A Conceptual Model,” Journal of Biomedical Informatics, Vol.44, pp 688-699.
13. Fiszman, M., Demner-Fushman, D., Kilicoglu, H. and Rindflesch, T. C., 2009, “Automatic Summarization of MEDLINE Citations for Evidence Based Medical Treatment: A Topic-Oriented Evaluation,” Journal of Biomedical Informatics, Vol.42, pp 801-813.
14. Fiszman, M., Rindflesch, T. C. and Kilicoglu, H., 2004, “Summarization of an Online Medical Encyclopedia,” Medinfo, pp 506-510.
15. Fiszman, M., Rindflesch, T. C. and Kilicoglu, H., 2009, “Abstraction Summarization for Managing the Biomedical Research Literature,” Proceedings of the HLT-NAACL Workshop on Computational Lexical Semantics, pp 200-205.
16. Ghaddar, B. and Naoum-Sawaya, J., 2017, “High Dimensional Data Classification and Feature Selection Using Support Vector Machines,” European Journal of Operational Research, Vol. 265, pp. 993-1004.
17. Goldstein, A. and Shahar, Y., 2016, “An Automated Knowledge-Based Textual Summarization System for Longitudinal, Mclinical Data,” Journal of Biomedical Informatics, Vol.61, pp 159-175.
18. Hassanpoura, S. and Langlotz, C. P., 2016, “Information Extraction from Multi-institutional Radiology Reports,” Artificial Intelligence in Medicine, Vol. 66, pp 29-39.
19. Jafarpour, S., Sedghi, Z. and Amirani, M. C., 2012, “A Robust Brain MRI Classification with GLCM Features,” International Journal of Computer Applications, Vol. 37, pp 1-5.
20. Jindal, R. and Taneja, S., 2015, “A Lexical Approach for Text Categorization of Medical Documents,” Procedia Computer Science, Vol. 46, pp 314-320.
21. Johnson, D. B., Zou, Q. and Dionisio, J. D., 2002, “Modeling Medical Content for Automated Summarization,” Annals of the New York Academy of Sciences, Vol. 980, pp 247-258.
22. Kamal, M. M. and Sultana, K. Z., 2012, “A Comprehensive Tool for Text Categorization and Text Summarization in Bioinformatics,” The 15th International Conference on Computer and Information Technology, IEEE, Bangladesh, DOI: 10.1109.
23. Kaur, B. and Jindal, S., 2014, “An Implementation of Feature Extraction over Medical Images on OpenCV Environment,” 2014 International Conference on Devices, Circuits and Communications, DOI: 10.1109.
24. Lambin, P. and Rios-Velazquez, E., 2012, “Radiomics: Extracting More Information from Medical Images Using Advanced Feature Analysis,” European Journal of Cancer, Vol. 48, pp 441-446.
25. Liang, H. and Nartimo, I., 1998, “A Feature Extraction Algorithm Based on Wavelet Packet Decomposition for Heart Sound Signals,” International Symposium on IEEE-SP, pp 93-96.
26. Liu, Y., Aickelin, U., Feyereisl, J. and Durrant, L. G., 2012, “Wavelet Feature Extraction and Genetic Algorithm for Biomarker Detection in Colorectal Cancer Data,” Knowledge-Based Systems, Vol. 37, pp 502-514.
27. Maity, M., Dhane, D. and Mungle, T., 2017, “Web-Enabled Distributed Health-Care Framework for Automated Malaria Parasite Classification: An E-Health Approach,” Journal of Medical Systems, Vol. 41, pp. 1-18.
28. Maniruzzaman, M., Kumar, N. and Abedin, M. M., 2017, “Comparative Approaches for Classification of Diabetes Mellitus Data: Machine Learning Paradigm,” Computer Methods and Programs in Biomedicine, Vol. 152, pp. 23-34.
29. Meystre, S. and Haug, P. J., 2006, “Natural Language Processing to Extract Medical Problems from Electronic Clinical Documents: Performance Evaluation,” Journal of Biomedical Informatics, Vol. 39, pp 589-599.
30. Moen, H., Peltonen, L. M., Heimonen, J., Airola, A., Pahikkala, T., Salakoski, T. and Salanterä, S, 2016, “Comparison of Automatic Summarisation Methods for Clinical Free Text Notes,” Artificial intelligence in Medicine, Vol. 67, pp 25-37.
31. Mohamed, N. S., Zainudin, S. and Othman, Z. A., 2017, “Metaheuristic Approach for an Enhanced mRMR Filter Method for Classification Using Drug Response Microarray Data,” Expert Systems with Applications, Vol. 90, pp. 224-231.
32. Madheswaran, M. and Dhas, D. A. C., 2015, “Classification of Brain MRI Images Using Support Vector Machine with Various Kernels,” Biomedical Research, Vol. 26, pp 505-513.
33. Napolitano, G., Marshall, A., Hamilton, P. and Gavin, A. T., 2016, “Machine Learning Classification of Surgical Pathology Reports and Chunk Recognition for Information Extraction Noise Reduction,” Artificial Intelligence in Medicine, Vol. 70, pp 77-83.
34. Özel, S. A., 2011, “A Web Page Classification System Based on a Genetic Algorithm Using Tagged-Terms as Features,” Expert Systems with Applications, Vol. 38, No. 4, pp. 3407-3415.
35. Pechenizkiy, M., Tsymbal, A, Puuronen, S and Pechenizkiy, O., 2006, “Class Noise and Supervised Learning in Medical Domains: The Effect of Feature Extraction,” 19th IEEE International Symposium on Computer-Based Medical Systems, pp 708-713.
36. Plaza, L., Díaz, A. and Gervás, P., 2011, “A Semantic Graph-Based Approach to Biomedical Summarisation,” Artificial Intelligence in Medicine, Vol.53, pp 1-14.
37. Raji, A., Golda Jeyasheeli, P. and Jenitha, T., 2016, “IoT Based Classification of Vital Signs Data for Chronic Disease Monitoring,” 10th International Conference on Intelligent Systems and Control, IEEE, India, DOI: 10.1109.
38. Reeve, L. H. and Han, H., 2007, “The Use of Domain Specific Concepts in Biomedical Text Summarization,” Information Processing & Management, Vol. 43, pp 1765-1776.
39. Ross, N. E., 2006, “Automated Image Processing Method for the Diagnosis and Classification of Malaria on Thin Blood Smears,” Medical and Biological Engineering and Computing, Vol. 44, pp. 427-436.
40. Saini, R., Bindal, N. and Bansal, P., 2015, “Classification of Heart Diseases from ECG Signals Using Wavelet Transform and KNN Classifier,” 2015 International Conference on Computing, Communication & Automation, IEEE, India, DOI: 10.1109
41. Sarkar, K., 2009, “Using Domain Knowledge for Text Summarization in Medical Domain,” International Journal of Recent Trends in Engineering, Vol. 1, pp 200-205.
42. Sarkar, K., Nasipuri, M. and Ghose, S., 2011, “Using Machine Learning for Medical Document Summarization,” International Journal of Database Theory and Application, Vol.4, pp 31-48.
43. Scott, D., Hallett, C. and Fettiplace, R., 2013, “Data-to-Text Summarisation of Patient Records: Using Computer-Generated Summaries to Access Patient Histories,” Patient Education and Counseling, Vol.92, pp 153-159.
44. Shi, Z., Melli, G. and Wang, Y., 2007, “Question Answering Summarization of Multiple Biomedical Documents,” Advances in Artificial Intelligence, pp 284-295.
45. Tong, D. L. and Schierz, A. C., 2011, “Hybrid Genetic Algorithm Neural Network: Feature Extraction for Unpreprocessed Microarray Data,” Artificial Intelligence in Medicine, Vol. 53, pp 47-56.
46. Udomhunsakul, S. and Wongsita, P., 2004, “Feature Extraction in Medical MRI Images,” Cybernetics and Intelligent Systems, Vol. 1, pp 340-344.
47. Vasantha, M., Bharathi, V. S. and Dhamodharan, R., 2010, “Medical Image Feature, Extraction, Selection and Classification,” International Journal of Engineering Science and Technology, Vol. 2, pp 2071-2076.
48. Verma, R., Chen, P. and Lu, W., 2007, “A Semantic Free Text Summarization System Using Ontology Knowledge,” Document Understanding Conference, DOI:10.1109.
49. Wang, X., Yang, W. and Weinreb, J., 2017, “Searching for Prostate Cancer by Fully Automated Magnetic Resonance Imaging Classification. Deep Learning versus Non-deep Learning,” Scientific Reports 7, DOI: 10.1038
50. Wei, L., Xing, P., Zeng, J., Chen, J., Su, R. and Guo, F., 2017, “Improved Prediction of Protein-protein Interactions Using Novel Negative Samples, Features, and an Ensemble Classifier,” Artificial Intelligence in Medicine, Vol. 83, pp 67-74.
51. Yetisgen-Yildiz, M. and Pratt, W., 2005, “The Effect of Feature Representation on MEDLINE Document Classification,” AMIA Annual Symposium Proceedings, Vol. 2005, pp 849-853.
52. Yin, L., 2010, “Document Classification for Mining Host Pathogen Protein-Protein Interactions,” Artificial Intelligence in Medicine, Vol. 49, pp. 155-160.
53. Zhang, L. W., Liu, X. and Wang, J., 2017, “Prediction of Malignant and Benign Lung Tumors Using a Quantitative Radiomic Method,” Acta Automatica Sinica, Vol.43, pp 2109-2114.