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研究生: 李紹瑋
研究生(外文): Shao-Wei Lee
論文名稱: 運用整體擴展技術於職能治療之小樣本研究
論文名稱(外文): A case study of testing the functioning level of psychiatric activities using the mega-trend-diffusion technique
指導教授: 蔡東亦
學位類別: 碩士
校院名稱: 樹德科技大學
系所名稱: 資訊管理系碩士班
論文出版年: 2011
畢業學年度: 99
語文別: 中文
論文頁數: 64
中文關鍵詞: 小樣本類神經網路職能治療整體趨勢擴散
外文關鍵詞: Small sample, Daily Living Function Scale, Neural Network,MTD
相關次數:
  • 被引用:0
  • 點閱:15
  • 評分:*****
  • 下載:3
  • 書目收藏:0
慢性精神病患在日常活動方面一直都存在著很多問題,這是職能治療師一直以來所關心的問題,然而,由於對於精神病患的病情測驗並不能測驗太多次,因為會導致病患對於測驗題目過於熟悉,影響測驗結果。
類神經網路,因其具有高速計算能力、高容記憶力、學習能力、容錯能力等,所以應當適合用於本研究,然而,類神經網路通常需要大量的訓練樣本,以供建立學習模式,這是很難在早期階段收集到的訓練系統。
因此,在本文中,當遇到樣本數不足的困境時,Li等人(2007),提出了整體趨勢擴散(mega-trend-diffusion;MTD),使得預測更為精確。根據研究結果顯示,這有可能會迅速發展成為精神科醫師在數據不足時的預測工具。


The functioning level of psychiatric patients’ activities of daily living has long been a major issue that many psychiatric occupational therapists are concerned about. However, because many patients have been tested multiple times on their functioning in daily living activities, they became so familiar with test questions that the results of their tests tend to be skewed. Neural networks are one of the learning models widely applied to implement this task. However, neural networks generally require a great amount of training data to establish the learning model, which is difficult to collect in the early stages of a manufacturing system. Therefore, in this paper, for cases when the collected data is insufficient, a procedure proposed by Li et al. (2007), called mega-trend-diffusion, is applied to make the forecast more precise. The results reveal that it is possible to rapidly develop a model of production with limited data from clinical practitioners.

摘要  I
ABSTRACT  II
誌謝  III
目錄  IV
圖目錄  VI
表目錄  VII
第一章 緒論  1
1.1 研究動機  1
1.2 研究目的  5
1.3 研究流程  6
第二章 文獻探討  7
2.1 慢性精神病患  7
2.1.1 慢性精神病患之定義  7
2.1.2 慢性精神病患之相關特性  8
2.1.3 慢性精神病患之社會功能  9
2.1.4 慢性精神病患之生活品質  10
2.1.5 慢性精神病患之經濟功能  11
2.1.6 慢性精神病患持續性的照護需求  11
2.2.1 職能治療的歷史發展  13
2.2.2 職能治療之定義  14
2.2.3 職能治療的服務對象  15
2.2.4 職能治療的服務範圍  15
2.2.5 職能治療人員的基本特性  16
2.3 褚氏日常生活評量表  23
2.3.1 測驗目的  25
2.3.2 測驗內容  25
2.3.3 常模、信效度  25
2.3.4 計分解釋  26
2.4 小樣本之學習  27
2.4.1 虛擬樣本(Virtual Samples)  27
2.4.2 資料擴展  28
2.4.3 貝氏網路(Bayesian Networks)  33
2.5 類神經網路  40
2.5.1 類神經網路的發展歷程  41
2.5.2 類神經網路的構成要素  41
2.5.3 類神經網路的能力  43
2.5.4 類神經網路學習模式  44
2.5.5 倒傳遞類神經網路  45
2.6 小結  47
第三章 研究方法  48
3.1 研究步驟  48
3.2 Phythia 執行步驟  53
第四章 實驗結果  57
第五章 結論與建議  59
參考文獻  60



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