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研究生:曾筱珽
研究生(外文):Tseng, Hsiao-Ting
論文名稱:以機器學習技術建置憂鬱症病患失智預測臨床決策支援系統
論文名稱(外文):A Clinical Decision Support System of Dementia Prediction for Depressive Disorder Patients Using Machine Learning Techniques
指導教授:黃興進黃興進引用關係張怡秋張怡秋引用關係
指導教授(外文):Hwang, Hsin-GinnChang, I-Chiu
口試委員:洪萬富林妙聰古政元
口試委員(外文):Hung, Won-FuLin, Miao-TsongKu, Cheng-Yuan
口試日期:2018-05-16
學位類別:博士
校院名稱:國立交通大學
系所名稱:資訊管理研究所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2018
畢業學年度:106
語文別:英文
論文頁數:108
中文關鍵詞:憂鬱症失智症疾病嚴重度機器學習臨床決策支援系統
外文關鍵詞:Depressive DisorderDementiaDisease SeverityMachine LearningClinical Decision Support System
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世界衛生組織指出憂鬱症乃21世紀三大疾病之一,也是精神科患者數最為眾多的疾病別,有研究指出憂鬱症患者相較於非憂鬱症患者,在日後罹患失智症的可能性大增,可見憂鬱症與失智症兩者關係之密切,又憂鬱症患者若日後併發失智症則更易面臨失智帶來的失能。然而,雖然目前有許多相關研究指出憂鬱症是導致失智症的重要原因之一,許多學者也亟欲探討「憂鬱症病史會增加罹患失智症的機率嗎?」的答案,然而這些研究結果並不一致,且目前尚未有研究以實證方式提出憂鬱症患者的失智症預測模型以供臨床實務之用,故本研究將藉由機器學習技術的應用建立憂鬱症患者後續罹患失智症的預測模型,以協助患者與相關醫護人員協助預測憂鬱症患者失智的可能,進而制訂早期干預措施和預防措施。本研究的資料來源為健康保險資料庫之LHID 2000,該資料庫內容乃一承保抽樣歸人檔。內容為隨機抽取100萬個2000年時我國健康保險的所有在保者之各年所有就醫申報資料,此乃本研究之母體。本研究從中選擇2000年至2004年由精神科醫師開立,且尚未有失智症診斷的憂鬱症新發個案作為本研究樣本,並從資料庫蒐集與整理樣本的基本資料與診斷相關資料作為本研究後續分析之用。接著,應用機器學習技術進行憂鬱症患者後續罹患失智症的預測模式之比較性分析,並從疾病嚴重度與年齡差異的觀點切入以探討對於憂鬱症病患後續罹患失智症的預測模型與預測結果之差異,並加入時間性因素的考量,找出前後共病產生的預測。接著,承續以上研究結果為基礎建置一「憂鬱症患者失智預測臨床決策支援系統」以協助醫師與病患臨床決策之用。
The World Health Organization identified depressive disorder as one of the three major diseases in the 21st century and it is one of the most common diseases encountered by psychiatry. Studies have shown that patients with depression are more likely than non-depression to have dementia in the future. There is an association between depression and dementia. Patients with depression may have dementia in the future and easier to face the disability caused by dementia. However, some studies have indicated that, compared to other people, patients with depressive disorder have a higher risk of suffering from dementia. From the above reasoning to infer the depressive disorder and dementia may exist a correlation. However, although there are many related studies that point out that depressive disorder is one of the important factor of dementia, many researchers are also anxious to explore the answer of "Will history of depressive disorder increase the risk of dementia in the future?", however, these findings are not consistent. In addition, there has been no study of evidence-based construction of dementia prediction model of depressive disorder patients for clinical practice. Therefore, this study will use machine-learning techniques to construct a follow-up dementia prediction model for depressive disorder patients to assist depressive disorder patients and their medical staffs to predict his/her possible risk of suffering from dementia, and then develop early intervention and prevention measures. The data source of this study is NHIRD LHID 2000. In NHIRD LHID 2000, it random select 1 million people who is insured in 2000, and collected all of their medical data. Moreover, these 1 million people is the population of this study. The study select samples from the NHIRD LHID 2000 database, whose order is diagnosis by psychiatrists in 2000 to 2004, and excluded those diagnosed depressive disorder in 1996 to 1999, in order to ensure research samples in this study are new depression cases. Then, to retrieve the demographic data and diagnostic related data from the database for follow-up analysis. Next, we used machine-learning techniques to analyze the prediction result of follow-up risk of dementia in patients with depression. In addition, this study also compared the follow-up dementia prediction results for depressive disorder patients from the viewpoint of disease severity and age differences. In addition, this study also added temporal considerations to identify the predictors of comorbidity. Based on the above results and findings to develop a depressive disorder patients’ dementia prediction clinical decision support system to assist physicians and patients in clinical decision-making.
摘要 II
ABSTRACT IV
誌 謝 VI
TABLE OF CONTENTS VII
LIST OF TABLES IX
LIST OF FIGURES XI
1. INTRODUCTION 1
1.1. Research Background and Motivation 1
1.2. Research Purpose and Questions 5
1.3. Research Process 7
1.4. Significance of the Dissertation 8
1.5. Organization of the Dissertation 10
2. REVIEW OF RELATED WORKS 13
2.1. Big Data in Medicine 13
2.2. Depressive Disorder 17
2.3. Dementia 20
2.4. Machine Learning 32
2.5. Disease Prevention Applications of Decision Support System 43
3. RESEARCH METHODOLOGY 45
3.1. Stage 1: Machine Learning Techniques 45
3.2. Stage 2: System Development and Evaluation 52
4. RESEARCH RESULTS 61
4.1. Descriptive analysis results 61
4.2. Depressive Disorder Patients' Dementia Prediction Model: Results of Association Rules Mining 67
4.3. Depressive Disorder Patients' Dementia Prediction Model: Results of Classification Mining 80
4.4. Development and Demonstration of Depressive Disorder Patients Dementia Prediction Clinical Decision Support System 84
5. CONCLUSIONS 90
5.1. Conclusions and Discussion 90
5.2. Research Contributions 92
5.3. Research Limitations 93
5.4. Future Study 94
REFERENCES 95
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