(54.236.58.220) 您好!臺灣時間:2021/03/05 00:10
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果

詳目顯示:::

我願授權國圖
: 
twitterline
研究生:蘇煒迪
研究生(外文):Wei-Ti Su
論文名稱:以時間合併演算及統計法處理多重時間序列資料與多重資料分類
論文名稱(外文):Multiple Time Series Data Processing for Classification with Period Merging Algorithm and Statistical Measures
指導教授:賴飛羆賴飛羆引用關係
口試委員:莊仁輝蔡坤霖陳宜君許凱平
口試日期:2014-06-13
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:生醫電子與資訊學研究所
學門:工程學門
學類:生醫工程學類
論文種類:學術論文
論文出版年:2014
畢業學年度:102
語文別:英文
論文頁數:40
中文關鍵詞:時間序列時間摘要肝癌射頻燒灼術預測模型
外文關鍵詞:Time SeriesTemporal abstractionLiver cancerRFAPredictive model
相關次數:
  • 被引用被引用:0
  • 點閱點閱:142
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
至2012年為止,癌症位居國人死因第一位已經31年了,其中肝癌比例更是居高不下;慢性肝病及肝硬化也位居十大死因之一,肝病如此嚴重的情況下,治療方法便相當重要。射頻燒灼術是治療肝癌的一種方法,近年來,更是越趨重要。對於那些因為肝癌而接受射頻燒灼術治療的人們,我們收集其就診的臨床檢驗資料,作為預測射頻燒灼術後肝癌復發與否的模型。這些龐大的臨床資料,先以不同的時間序列做整理,而後再利用時間摘要法將資料內容進一步轉化,於是在不同的時間序列內,就會有不同內容的時間摘要。此篇論文的目標在於探討不同時間序列內時間摘要法對於發展預測模型的成效。我們將原始資料統整為原始值、時間摘要法處理後,以及原始+時間摘要法雙處理值3類,並利用支持向量機作為分類發展預測模型的機器。結果顯示,除了不同的時間序列能有不同的統計結果外,時間摘要法也有效果,時間摘要法處理後的值,以及原始+時間摘要法雙處理值兩類對敏感度及特異度的提升有幫助。

Cancer has been ranked first in the causes of death for 31 consecutive years in Taiwan. Radiofrequency ablation (RFA) is a treatment for hepatocellular carcinoma (HCC) and it becomes one of important therapies for HCC these years. For those who had HCC and were treated by RFA, their clinical data are collected to build predictive models which can be used in predicting the recurrence or not of liver cancer after RAF treatment. Clinical data with multiple measurements are merged based on different time periods and these data are further transformed based on temporal abstraction (TA). Data processed by TA reveal variations of clinical data with different time points. The goal of this study is to evaluate whether clinical data handled by TA could increase performance of predictive models. Different data sets are used in developing predictive models, including clinical data which are not processed by TA called the original data set, clinical data which are processed by TA called the TA data set, and combination of the original data set and the TA data set called the TA+original data set. Support vector machine (SVM) was selected as a classifier to develop predictive models. The results demonstrate data sets processed by TA provide benefit for predictive models.

誌謝 i
中文摘要 ii
ABSTRACT iii
CONTENTS iv
LIST OF FIGURES vi
LIST OF TABLES vii
Chapter 1 Introduction 1
1-1 Motivations 1
1-2 Literature Review 1
Chapter 2 Methods 4
2-1 Data Source 6
2-2 The Algorithm for Merging Multiple Features Based on Time Period 7
2-3 Temporal Abstraction 9
2-4 Classification Methods 11
2-5 Performance Evaluation 12
Chapter 3 Results 13
Chapter 4 Discussion 25
4-1 16 Features have been treated with TA 25
4-2 26 Features have been treated with TA 27
4-3 Reverse TA value settings 30
4-4 Periods 34
Chapter 5 Conclusion 36
Chapter 6 Future Work 37
REFERENCE 38


1.Ministry of Health and Welfare, E.Y., Taiwan, Causes of death in Taiwan, 2012, M.o.H.a.W. Department of Statistics, Editor. 2013, Department of Statistics, Ministry of Health and Welfare.
2.Shiina, S., et al., Radiofrequency ablation for hepatocellular carcinoma: 10-year outcome and prognostic factors. The American journal of gastroenterology, 2012. 107(4): p. 569-577.
3.Han, J. and M. Kamber, Data mining: concepts and techniques. 2 ed. The Morgan Kaufmann Series in Data Management Systems, ed. J. Gray. 2006 San Francisco: Morgan Kaufmann.
4.Lenzerini, M., Data integration: a theoretical perspective, in Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems. 2002, ACM: Madison, Wisconsin. p. 233-246.
5.Hancock, A.A., et al., Data normalization before statistical analysis: keeping the horse before the cart. Trends in Pharmacological Sciences, 1988. 9(1): p. 29-32.
6.Shahar, Y., A framework for knowledge-based temporal abstraction. Artificial intelligence, 1997. 90(1): p. 79-133.
7.Stacey, M. and C. McGregor, Temporal abstraction in intelligent clinical data analysis: A survey. Artificial Intelligence in Medicine, 2007. 39(1): p. 1-24.
8.Hoppner, F., Knowledge discovery from sequential data. 2003, PhD thesis, Technical University Braunschweig, Germany.
9.Moerchen, F. Algorithms for time series knowledge mining. in Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. 2006. ACM.
10.Wu, S.-Y. and Y.-L. Chen, Mining nonambiguous temporal patterns for interval-based events. Knowledge and Data Engineering, IEEE Transactions on, 2007. 19(6): p. 742-758.
11.Moskovitch, R. and Y. Shahar. Medical temporal-knowledge discovery via temporal abstraction. in AMIA Annual Symposium Proceedings. 2009. American Medical Informatics Association.
12.Sacchi, L., et al., Data mining with Temporal Abstractions: learning rules from time series. Data Mining and Knowledge Discovery, 2007. 15(2): p. 217-247.
13.Batal, I., et al. A pattern mining approach for classifying multivariate temporal data. in Bioinformatics and Biomedicine (BIBM), 2011 IEEE International Conference on. 2011. IEEE.
14.Keogh, E., et al., Segmenting time series: A survey and novel approach. Data mining in time series databases, 2004. 57: p. 1-22.
15.Batal, I. and M. Hauskrecht. Constructing classification features using minimal predictive patterns. in Proceedings of the 19th ACM international conference on Information and knowledge management. 2010. ACM.
16.Cortes, C. and V. Vapnik, Support-Vector Networks. Machine learning, 1995. 20(3): p. 273-297.
17.Boser, B.E., I.M. Guyon, and V.N. Vapnik, A training algorithm for optimal margin classifiers, in Proceedings of the fifth annual workshop on Computational learning theory. 1992, ACM: Pittsburgh, Pennsylvania, United States. p. 144-152.
18.Meyer, D., F. Leisch, and K. Hornik, The support vector machine under test. Neurocomputing, 2003. 55(1-2): p. 169-186.
19.Chang, C.-C. and C.-J. Lin, LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol., 2011. 2(3): p. 1-27.


QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
系統版面圖檔 系統版面圖檔