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標題: | 基於文本利用遞歸神經網絡之阿茲海默症快篩系統 A Screening System for Alzheimer’s Disease Based on Speech Transcript Using Recurrent Neural Networks |
作者: | Sheng-Yi Hong 洪昇毅 |
指導教授: | 傅立成(Li-Chen Fu) |
關鍵字: | 阿茲海默症,輕度認知障礙,快篩系統,遞歸神經網絡,神經心理測驗, Mild Cognitive Impairment,Screening system,Recurrent Neural Networks,Neuropsychological test, |
出版年 : | 2019 |
學位: | 碩士 |
摘要: | 阿茲海默症已然成為世界性的醫療問題,據統計,阿茲海默症已成為全美第六大的死因,也是超過65歲長者的第五大死因,此外,患者數在近年有急遽增長的跡象,亦造成全球醫療的重大負擔。因此,一個得以輔助醫生快速診斷的快篩系統是迫切需要的。在此論文中,我們提出了一個基於語言神經心理測驗的快篩系統,此系統使用詞向量去表示長者測試過程中回答的內容,並使用遞歸神經網絡搭配注意力機制進行分類,比起過去需要萃取語意與語法特徵,並需要額外進行特徵選取的系統,本系統可以更加自動化而不需要專家的協助。使用10折交叉驗證,在區分242筆美國健康長者的作答及257筆患有阿茲海默症的美國長者作答,可以有0.83的準確度;而區分43筆患有輕度認知障礙的美國長者作答及43筆美國健康長者的作答,也有0.71的準確度。測試在各40位的台灣健康及患有阿茲海默症的長者,其準確度甚至可以高達0.89;而區分各30位的健康及患有輕度認知障礙的台灣長者,也能有0.8的準確度。 Alzheimer's disease has become one of the biggest challenges in the healthcare system worldwide. Researches have shown that Alzheimer’s disease is the sixth leading cause of death in the United States and even the fifth leading cause among people aged 65 and older. Therefore, a screening system that can help the doctor to diagnose Alzheimer’s disease is demanded. In this thesis, we proposed a screening system based on the transcripts of speeches spoken by subjects undertaking a neuropsychology test. While most of the related studies have utilized extracted syntactic and semantic features and relied on a feature selection process, the proposed system used word vectors as the representation of a spoken speech, and Recurrent Neural Networks together with attention mechanism as the classifier. Using ten times 10-fold cross validation on an open dataset with 242 speeches samples spoken by healthy controls and 257 samples spoken by subjects with Alzheimer's disease in the USA, a mean accuracy of 0.83 is achieved in our work. And the classification of 43 healthy subjects and 43 subjects with Mild Cognitive Impairment, the model can still achieve 0.71 of accuracy. On the other hand, validate on 40 Taiwanese subjects with AD and 40 healthy Taiwanese subjects, and 30 Taiwanese subjects with MCI and 30 healthy Taiwanese subjects, accuracy of 0.89 and accuracy of 0.8 can be achieved, respectively. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/73981 |
DOI: | 10.6342/NTU201903654 |
全文授權: | 有償授權 |
顯示於系所單位: | 資訊工程學系 |
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