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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 賴飛羆 | |
dc.contributor.author | Chien-Han Kuo | en |
dc.contributor.author | 郭建漢 | zh_TW |
dc.date.accessioned | 2021-06-16T02:37:44Z | - |
dc.date.available | 2018-07-29 | |
dc.date.copyright | 2015-07-29 | |
dc.date.issued | 2015 | |
dc.date.submitted | 2015-07-24 | |
dc.identifier.citation | 1. Ping, Xiao-Ou, et al. 'A multiple measurements case-based reasoning method for predicting recurrent status of liver cancer patients.' Computers in Industry 69 (2015): 12-21.
2. Tseng, Yuh-Min, et al. 'Multiple Time Series Clinical Data Processing for Classification with Merging Algorithm and Statistical Measures.' (2014). 3. Su, Wei-Ti, et al. 'Multiple Time Series Data Processing for Classification with Period Merging Algorithm.' Procedia Computer Science 37 (2014): 301-308. 4. Liang, Ja-Der, et al. 'Recurrence predictive models for patients with hepatocellular carcinoma after radiofrequency ablation using support vector machines with feature selection methods.' Computer methods and programs in biomedicine 117.3 (2014): 425-434. 5. Hand, D., Mannila, H., Smyth, P., Principles of data mining. MIT, 2001. 6. The Technology Review Ten, MIT Technology Review (January/February 2001). 7. Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P., The KDD process of extracting useful knowledge from volumes of data. Commun. ACM 39(11):27–34, 1996. 8. Berger, A., and Berger, C., Data mining as a tool for research and knowledge development in nursing. Comput. Inform. Nurs. 22(3):123–131, 2004. 9. Harper, P. R., A review and comparison of classification algorithms for medical decision making. Health Policy 71:315–331, 2005. 10. Stel, V. S., Pluijm, S. M., Deeg, D. J., Smit, J. H., Bouter, L. M., and Lips, P., A classification tree for predicting recurrent falling in community-dwelling older persons. J. Am. Geriatr. Soc. 51:1356–1364, 2003. 11. Adam, B. L., Qu, Y., Davis, J. W., Ward, M. D., Clements, M. A., Cazares, L. H., et al., Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men. Cancer Res. 62:3609–3614, 2002. 12. Bellazzi, R., and Zupan, B., Predictive data mining in clinical medicine: current issues and guidelines. Int. J. Med. Inform. 77:81–97, 2008. 13. Ichise, R., and Numao Learning, M., First-order rules to handle medical data. NII Journal 2:9–14, 2001. 14. Yoo, Illhoi, et al. 'Data mining in healthcare and biomedicine: a survey of the literature.' Journal of medical systems 36.4 (2012): 2431-2448 15. Selby JV, Ferrara A, Karter AJ, Liu J, Ackerson LM. Developing a prediction rule from automated clinical databases to identify high-risk patients in a large population with diabetes. Diabetes Care 2001;24:1547—55. 16. Yeh, Jinn-Yi, Tai-Hsi Wu, and Chuan-Wei Tsao. 'Using data mining techniques to predict hospitalization of hemodialysis patients.' Decision Support Systems 50.2 (2011): 439-448. 17. Batal, Iyad, et al. 'A pattern mining approach for classifying multivariate temporal data.' Bioinformatics and Biomedicine (BIBM), 2011 IEEE International Conference on. IEEE, 2011. 18. Lin, Y., et al. 'Time-to-Event Predictive Modeling for Chronic Conditions using Electronic Health Records.' (2014): 1-1. 19. Jacob, Shomona Gracia, and R. Geetha Ramani. 'Discovery of Knowledge Patterns in Clinical Data through Data Mining Algorithms: Multi-class Categorization of Breast Tissue Data.' International Journal of Computer Applications (IJCA) 32.7 (2011): 46-53. 20. Dagliati, Arianna, et al. 'Temporal data mining and process mining techniques to identify cardiovascular risk-associated clinical pathways in Type 2 diabetes patients.' Biomedical and Health Informatics (BHI), 2014 IEEE-EMBS International Conference on. IEEE, 2014. 21. Shouval, R., et al. 'Application of machine learning algorithms for clinical predictive modeling: a data-mining approach in SCT.' Bone marrow transplantation 49.3 (2014): 332-337. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/54043 | - |
dc.description.abstract | 射頻燒灼術是一種治療肝癌的方法。雖然在手術後可以根除腫瘤,但是肝癌復發是一項非常重要的議題。在這項研究裡,我們收集了因為肝癌而接受過射頻燒灼術治療病人們的就診臨床資料,並用來建立預測射頻燒灼術後肝癌復發與否的模型。這些臨床資料是以縱向資料的形式存在,包含兩種特徵,靜態特徵與時序特徵。我們提出了一種叫做動態區間分割的資料前處理方法,並結合時間摘要法,從原始的時序特徵萃取摘要化的元特徵。我們使用動態區間分割法把給定的時間區間切成若干個分區,然後使用時間摘要法計算每個分區的統計量值。完成上述兩個方法以後,可以得到新的元特徵。結合新的元特徵和原本的靜態特徵得到新的資料。利用支持向量機作為分類器,並結合模擬退火和隨機森林兩種特徵選取的方法建立預測模型。 | zh_TW |
dc.description.abstract | Radiofrequency ablation (RFA) is a common treatment for the hepatocellular carcinoma (HCC). Recurrence of HCC is an important issue despite effective treatments with tumor eradication. In this study, for those who had HCC and were treated by RFA, their clinical data are collected to build predictive models which can be used to predict the recurrence of HCC patients after RFA treatment. These clinical data are in the form of longitudinal data, which consists of static features and temporal features. We develop a data preprocessing method called Dynamic Period Slicing (DPS) combined with temporal abstraction (TA) to extract the high-level features from the original temporal features. We use DPS to divide a given time period into several partitions, then use quantitative TA to calculate the statistical measurements from the values in a given partition. After implementing the above two methods, we can obtain new meta-features, then combine the meta-features with original static features to form new processed data. Support Vector machine (SVM) was selected as the classifier to build predictive models with simulated annealing and random forest feature selection methods. | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T02:37:44Z (GMT). No. of bitstreams: 1 ntu-104-R02922154-1.pdf: 1218896 bytes, checksum: 9d817cb00ec46d91023bc4669f4e3ab6 (MD5) Previous issue date: 2015 | en |
dc.description.tableofcontents | 誌謝 i
中文摘要 ii Abstract iii Chapter 1 Introduction 1 1.1 Background 1 1.2 Motivation 3 Chapter 2 Literature Review 4 Chapter 3 Method 6 3.1 Data Collection 8 3.1.1 Data Source 8 3.1.2 Data Format 9 3.2 Temporal Abstraction 9 3.3 Dynamic Period Slicing 12 3.4 Data Merging 18 3.5 Feature Selection 20 3.5.1 Simulated Annealing 21 3.5.2 Random Forest 24 3.6 Classification 25 3.6.1 Linear Classifier 25 3.6.2 Hard Maximal Margin Classifier 26 3.6.3 Support Vector Machine 27 Chapter 4 Experiment and Results 31 4.1 Experiment 31 4.2 Performance Evaluation 34 4.3 Result 35 Chapter 5 Conclusion 39 Reference 40 | |
dc.language.iso | en | |
dc.title | 動態區間切割演算法使用於不規則縱向資料探勘 | zh_TW |
dc.title | Irregularly Spaced Longitudinal Data Mining Using a Dynamic Period Slicing Algorithm | en |
dc.type | Thesis | |
dc.date.schoolyear | 103-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 莊仁輝,鄭一鴻,陳啟煌,蔡坤霖 | |
dc.subject.keyword | 動態區間分割,時間摘要法,射頻燒灼術,肝癌,支持向量機, | zh_TW |
dc.subject.keyword | Dynamic Period Slicing,Temporal Abstraction,RFA,Liver cancer,SVM, | en |
dc.relation.page | 42 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2015-07-24 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
顯示於系所單位: | 資訊工程學系 |
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