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研究生:陳珮瑩
研究生(外文):Pei-Ying Chen
論文名稱:運用類神經網路預測環境因子於氣喘病人之影響
論文名稱(外文):Predicting the effect of the environment parameters in asthma patients using Artificial Neural Networks
指導教授:李友專李友專引用關係
指導教授(外文):Yu-Chuan Li
學位類別:碩士
校院名稱:國立陽明大學
系所名稱:生物醫學資訊研究所
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:2011
畢業學年度:99
語文別:中文
論文頁數:52
中文關鍵詞:氣喘類神經網路
外文關鍵詞:AsthmaArtificial Neural Networks
相關次數:
  • 被引用被引用:1
  • 點閱點閱:250
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  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:1
人類的生活與環境息息相關,快速且不穩定的氣候變遷,讓人們也產生許多與氣候、天氣相關的疾病,氣喘就是其中之一。近年來全球罹患氣喘的人口持續上昇,其死亡率也持續攀升中,如何防範未然,實為不容輕忽的問題。本研究目的在使用醫院之資料,結合易取得之環境因子,以資料採礦(Data Mining)的方式,發展一氣喘預測模式,期望該模型能達到降低病徵發作之可能。
為達到以上之目的,本研究使用北部某醫學中心自2007年1月至2009年12月為期三年之急診資料及門診就醫資料,將該次就醫主要診斷碼為493.*之病患資料抽出,分別整理出其以該年度經整理並計算出該天氣喘發作機率,將其與環境中的空氣因子及氣象因子結合,使用WEKA工具訓練類神經網路的模型,以建立起環境因子與氣喘發作之關連性。
結果顯示,雖此模型於急診之中未能顯現有效之預測(準確率66.57%,敏感性0.666,特異性0.601,AUC: 0.682),但施行於門診之中能得到不錯之預測成效(準確率75.18%,敏感性0.752,特異性0.757,AUC: 0.846)。本研究僅以方便取得之環境因子即可瞭解其對於病人之影響高達七成以上,未來更應近一步研究分析,以達到氣喘發作預警、降低病徵發作之目的。
Our life is closely related to the environment. Climate change getting fast and unstable, so people are getting climate-related diseases such as asthma. According to World Health Organization (WHO) estimates, 235 million people suffer from asthma globally, and the prevalence of asthma is increasing. Asthma, become a world-wild health problem. We use existing data in hospital and combine with the environment parameters to find an appropriate model for assist asthma patients in asthma attack control.
To reach this propose, patients who were diagnosed as asthma (ICD-9 code: 493.*) by physicians from emergency department (ED) and outpatient department (OPD) during January 2007 to December 2009 were selected. According to the patient data, we can classify all the date to high-risk attack date or low-risk attack date, and we use Atmospheric parameters and Air pollution parameters as variables to predict asthma attack. Through the data preprocessing, feature selection and neural network adjust, we can decide the final predict variables and perform the best prediction of the model.
Although the performance of the ED model is not good enough (Accuracy: 66.57%, Sensitivity: 0.666, Specificity: 0.601, AUC: 0.682), but the result from the OPD is much better (Accuracy: 75.18%, Sensitivity: 0.752, Specificity: 0.757, AUC: 0.846).
In this study, asthma attack could be predicted by the easy-get environment parameters, the accuracy performance is higher than 70%. In future studies, we could apply this model in clinical practice to help asthma patients.

論文電子檔著作權授權書 i
論文審定同意書 ii
誌謝 iii
中文摘要 iv
英文摘要 v
目錄 vi
表目錄 viii
圖目錄 ix
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 3
第二章 文獻探討 4
2.1 氣喘相關概念 4
2.2 氣候因素的影響 7
2.3 空氣汙染的影響 9
2.4 類神經網路介紹 11
第三章 材料與方法 15
3.1 資料來源 15
3.2 資料前處理方式 17
3.3 預測變項 19
3.4 研究方法 22
3.5 預測模型的評估 26
第四章 研究結果 28
4.1 研究資料描述 28
4.2 變數選擇結果 35
4.3 參數調整 40
第五章 結果討論及建議 44
5.1 討論 44
5.2 結論與建議 47
參考文獻 49
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