建構術後再住院預測模型-以裂孔性視網膜剝離為例

Construct Predictive Models for the Postoperative Readmissions-A Case Study of Rhegmatogenous Retinal Detachment

江嘉祥(Chia-Hsiang Chiang)

指導教授 ： 張俊郎

國立虎尾科技大學/管理學院/工業工程與管理研究所/碩士(2013年)

摘要

在台灣視網膜剝離為民眾失明的主要原因之一，每年估計約一千至二千名病例罹患視網膜剝離，其發生率高居遠東各國之冠。根據相關文獻指出，可能與近視人口快速成長有關。根據視網膜剝離發生原因，可分成三種：裂孔性視網膜剝離、牽引性視網膜剝離和滲出性視網膜剝離，其中裂孔性視網膜剝離為一般所稱的視網膜剝離，且必頇儘快進行手術治療。故本研究將針對裂孔性視網膜剝離做進一步的探討。當病患在接受首次手術後，可能會在相同位置或其它部位再次發生視網膜剝離，且多數於一年內發生(再住院率約10~20%)，導致需接受二次或多次以上的手術。因此如何有效評估視網膜剝離手術病患術後是否再住院，對相關醫療機構、人員、病患及其家屬皆有實質上的幫助，且有助於醫療資源的分配與控制。

本研究以全民健康保險研究資料庫2007年至2010年進行裂孔性視網膜剝離手術且一年內再住院之病患做為研究對象，並經由文獻蒐集來彙整其相關影響因子。透過倒傳遞類神經網路、支援向量機、粒子群演算法、基因邏輯斯迴歸及其相互結合等方法建構出七組預測模型以及利用案例式推理技術建構評估系統。由研究結果顯示，在成對樣本T檢定比較中，各模型皆有顯著差異，其中以倒傳遞類神經網路及基因邏輯斯迴歸結合倒傳遞類神經網路模型之預測績效最為優異，在平均準確度與平均ROC曲線下面積(Area Under the Receiver Operative Characteristic Curve, AURC)皆有85%以上與0.81以上；在案例式推理系統方面，以基因邏輯斯迴歸所分析的權重值導入案例式推理系統之評估績效為最佳，其平均相似度為99.58%，系統準確度為82.66%，ROC曲線下面積為0.7372。故本研究之結果，將可做為預防術後再住院之參考依據，且對術後照護品質的提昇有實質的幫助。

關鍵字

裂孔性視網膜剝離 ； 倒傳遞類神經網路 ； 支援向量機 ； 粒子群演算法 ； 基因邏輯斯迴歸 ； 案例式推理技術

並列摘要

In Taiwan, retinal detachment is one of the main causes for people’s blindness. It is estimated approximate 1,000-2,000 patients suffering from such disease with the highest incidence among countries of the Far East. Relevant literature has indicated such phenomenon might be related to the rapid growth of myopia population. Causes of retinal detachment can be divided into three types: rhegmatogenous, traction and exudative retinal detachment, in which rhegmatogenous retinal detachment, commonly known as retinal detachment, must be treated with surgery as soon as possible. Thus, this study would focus on rhegmatogenous retinal detachment to carry out a further discussion. Patients’ retinal detachment may reoccur at the same location after receiving the first time surgery and the majority of them occur within one year (readmission rate at about 10 to 20%) which results in the need to accept another one or more surgeries. Therefore, effective evaluation of postoperative retinal detachment readmission will provide substantial assistance to relevant medical institutions, staffs, patients and their families as well as help medical resources allocation and control.

This research focused on readmission patients within one year after receiving RRD operation whose data were provided by 2007-2010 National Health Insurance Research Database as well as collected and compiled related impact factors through literature study. This study used Back Propagation Neural Network (BPN), Support Vector Machine (SVM), Particle Swarm Optimization (PSO), Genetic Algorithm-Logistic Regression, (GALR) and their mutual combination methods to construct seven groups of prediction models and applied Case Based Reasoning (CBR) to build evaluation system. Research findings showed that individual model in the paired samples T-test comparison presented significant differences, in which the prediction performance of Back Propagation Neural Network and Genetic Algorithm-Logistic Regression combining with Back Propagation Neural Network model was the most outstanding, at an average accuracy and Average Area under the Receiver Operative Characteristic Curve (AURC) more than 85% and 0.81, respectively. In the case based reasoning systems, the evaluation performance of introducing the weight value analyzed by GALR was the best, with an average similarity of 99.58%, system accuracy of 82.66% and 0.7372 of area under the curve of ROC. Therefore, results of this study should be able to be used as a reference for the prevention of postoperative readmission as well as to provide substantial improvement for the quality of postoperative care.

並列關鍵字

Rhegmatogenous Retinal Detachment ； Back Propagation Neural Network ； Support Vector Machine ； Particle Swarm Optimization ； Genetic Algorithm-Logistic Regression ； Case Based Reasoning

參考文獻

1. 中央健康保險局 (2013)，「健保18年珍惜醫療資源」，取自http://www.nhi.gov.tw/resource/Webdata/23516_1_NO.102%E5%85%A8%E6%B0%91%E5%81%A5%E4%BF%9D.pdf，參考日期：2013/05/06。

2. 中央健康保險局 (2013)，「支付制度」，http://www.nhi.gov.tw/webdata/webdata.aspx?menu=17&menu_id=659&WD_ID=897&webdata_id=4025，參考日期：2013/05/06。

3. 中央健康保險局 (2013)，「醫療給付」，http://www.nhi.gov.tw/webdata/webdata.aspx?menu=17&menu_id=1023&WD_ID=1043&webdata_id=805，參考日期：2013/05/07。

4. 日月明眼科，「視網膜」，取自http://www.lasik-wavefront.com.tw/retina.html，參考日期：2013/05/21。

5. 王庭荃、楊長興 (2008)，「醫師年資、醫療服務量與消化性潰瘍治療效果之相關研究」，臺灣公共衛生雜誌，27卷，1期，頁57-66。