以多重測量值案例式推理方法建立肝癌患者治療後復發預測模組

Model establishment of predicting recurrent status of liver cancer patients using multiple measurements case-based reasoning method

邱相茹(Hsiang-Ju Chiu)

指導教授 ： 賴飛羆

國立臺灣大學/電機資訊學院/生醫電子與資訊學研究所/碩士(2013年)

https://doi.org/10.6342/NTU.2013.10425

摘要

臨床資料含有許多有用的醫療相關資訊，如果能夠分享其中蘊含的資訊，無論是對於病患或是醫師都會有所助益。但是臨床資料會隨著病人回診檢驗，而有所增加或改變，因此可能會有多重測量而造成難以分析病人全部臨床資料的問題。此研究基於案例式推理架構，提出多重測量案例式推理法來分析多重的臨床資料。可在多重測量的情況下，尋找相似的病患，主要在預測針對一年內第一次接受治療的肝癌病患復發的情形，我們將病患資料隨機分成四組，各組進行交叉驗證，並將四組結果平均分析。最後預測出的模組取決於四組平均準確值較好的表現。並分析比較傳統案例式推理及多重測量案例式推理的結果，根據標準差的結果，我們認為模組加入多重的臨床資料案例式推理的結果較傳統案例式推理能趨於穩定。多重測量案例式推理敏感度的平均值也較傳統案例式推理好，此研究於各個不同的組合參數如計算特徵的五種演算法，不同的臨床天數週期，不同的權重來計算預測模組。
此研究可提供檢索相似病患的預測模組給其他有需要的使用者，例如病患或是醫療人員以供參考。

關鍵字

臨床資料 ； 案例式推理 ； 多重測量 ； 交叉驗證 ； 標準差

並列摘要

Due to the progress of medicine, clinical data are increased very rapidly and biochemistry laboratory items are multiply measured with the subsequent consultations of patients. These multiple measurements clinical data may become another problem during analysis. This study proposes a practicable method to appropriately handle the clinical data with multiple measurements. Based on the case-based reasoning (CBR) method, we propose a multiple measurements CBR (MMCBR) method, extended from single measurement CBR (SingleCBR), for analyzing clinical data. The research target of this study is the prediction of recurrent status of liver cancer patients after receiving the first treatment in one year. We randomly separated dataset into four subsets, and the average results of classification using three-fold cross validation in four random datasets are analyzed, respectively. The results show models with better performance in the mean accuracy of four random datasets. Combination CBR could produce comparable results with SingleCBR and might have better stability than that of SingleCBR according to the standard deviation of accuracy. The mean sensitivities of MMCBR and Combination CBR in most combinations are better than those of SingleCBR. In this study, five feature selection approaches, different time periods of clinical data merging, and different weights are examined for establishing a predictive model.

並列關鍵字

Clinical data ； case-based reasoning (CBR) ； multiple measurements ； cross-validation ； standard deviation

參考文獻

[4] B. E. Himes, Y. Dai, I. S. Kohane, S. T. Weiss, and M. F. Ramoni, “Prediction of chronic obstructive pulmonary disease (COPD) in asthma patients using electronic medical records,” J Am Med Inform Assoc, vol. 16, no. 3, pp. 371-9, May-Jun, 2009.

[5] R.-H. Lin, and C.-L. Chuang, “A hybrid diagnosis model for determining the types of the liver disease,” Comput. Biol. Med., vol. 40, no. 7, pp. 665-670, 2010.

[6] R. H. Lin, “An intelligent model for liver disease diagnosis,” Artif Intell Med, vol. 47, no. 1, pp. 53-62, Sep, 2009.

[9] M. M. Richter, and A. Aamodt, “Case-based reasoning foundations,” Knowl. Eng. Rev., vol. 20, no. 3, pp. 203-207, 2005.

[10] S. Shiu, and S. K. Pal, “Foundations of Soft Case-Based Reasoning,” 2004.