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研究生:宋晧遠
研究生(外文):Hao-Yuan Song
論文名稱:應用類神經網路在入院階段預測心血管患者住院天數
論文名稱(外文):Using Artificial Neural Network to Predict the Length of Stay for Cardiovascular Patients in the Pre-admission Stage
指導教授:蔡佩芳蔡佩芳引用關係
指導教授(外文):Pei-Fang Tsai
口試委員:王逸琦邱垂昱陳凱瀛
口試委員(外文):Yi-Chi WangChui-Yu ChiuKai-Ying Chen
口試日期:2012-06-05
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:工業工程與管理系碩士班
學門:工程學門
學類:工業工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:中文
論文頁數:60
中文關鍵詞:住院天數預測心血管疾病類神經網路醫療資源管理
外文關鍵詞:Length of Hospital PredictionCardiovascularArtificial Neural NetworkManagement of Medical Resource
相關次數:
  • 被引用被引用:5
  • 點閱點閱:261
  • 評分評分:
  • 下載下載:42
  • 收藏至我的研究室書目清單書目收藏:0
患者的入院規則和病床分配方式,都可對醫院的資源利用率產生顯著的影響。一個入院排程有效率的醫院,可以提高病床周轉率並減少不必要的佔用,提高護理質量。對於醫院的病床管理,在患者入院階段識別出住院期間可能發生的風險,除了有助於病床的最佳利用,還可使病床的分配更佳公平。在患者入院階段更好的識別會影響住院長度的關鍵因素,或是能夠針對個案預測其住院長度,對於想要使入院規則有效率並優化醫療資源管理的醫院是很有用的。本研究使用類神經網路建構預測模型,試圖在心血管疾病患者入院階段判斷其預測住院天數;由於心血管疾病包含多個主診斷種類,部分主診斷間存在顯著差異,因此將住院案例分為兩組樣本分別建立住院預測模型,並根據結果進行分析。目的在於評估使用患者的基本病歷特徵,在入院階段預測住院天數的有效性,並評估各預測變數對預測準確性的影響。實驗結果顯示,兩組樣本在入院階段的預測準確程度皆達到一定水準;平均住院天數較短、個案筆數集中的試驗樣本,準確預測比例為88.9%;平均住院天數較長、個案筆數分散的試驗樣本,準確預測筆數也達到70.2%,已可作為輔助醫院進行醫療管理和患者入院排程的參考資訊。

Patient admission and inpatient bed allocation policy can have sophisticate influence on resource utilization for any hospital. With an effective admission process, a hospital can increase its turnover rate, reduce unnecessary bed occupancy, and improve the quality of care. In hospital bed management, early identification of patients’ risk during hospitalization could not only facilitate optimal use of hospital beds, but also ration fair mixes inpatient admissions. A better recognition in critical factors before admission that determine length of stay (LOS), or a capacity to predict an individual patient’s LOS, could promote the development of efficient admission policy and optimize resource management in hospitals.
Using artificial neural network models, this research tried to predict the LOS for patients in cardiology department during the pr-admission stage. We analyzed clinical records of patients discharged from the Cardiology department in a medical center in Taipei during Oct 2010 to Dec 2010. Three main diagnosis of cardiovascular diseases considered are coronary atherosclerosis, heart failure, and acute myocardial infarction. The results showed that the prediction of LOS from our model using pre-admission factors only was more accurate for patients with coronary atherosclerosis. With 88.9% of the prediction were within one-day tolerance for coronary atherosclerosis patients and 70.2% for heart failure or acute myocardial infarction patients within three-day tolerance.

第一章、緒論...................1
1.1 研究背景與動機...................1
1.2 研究目的...................4
1.3 論文架構...................5
第二章 文獻探討...................6
2.1 住院長度預測相關文獻...................6
2.2 類神經網路於預測住院長度相關文獻...................10
2.3 精確預測住院天數結果衡量方式...................12
2.4 類神經網路方法...................13
第三章、研究方法...................21
3.1 資料收集...................21
3.2 住院天數預測變數...................24
3.3 類神經網路輸入變數...................26
3.4使用倒傳遞演算法建立住院天數預測模型...................28
第四章、研究結果...................33
4.1 資料處理...................33
4.2 類神經網路訓練與學習...................36
4.3 比較不同設定之預測結果...................37
4.4 比較單一預測網路與兩組預測網路...................43
4.5 結果分析與比較...................44
第五章 結論與未來研究方向...................49
5.1 結論...................49
5.2未來研究方向...................51
參考文獻...................53
附錄A:各類次診斷歸類結果...................57
附錄B:使用MATLAB建構類神經網路預測模型指令...................59


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