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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 歐陽彥正 | |
dc.contributor.author | Yu-Jen Hsu | en |
dc.contributor.author | 許又仁 | zh_TW |
dc.date.accessioned | 2021-06-13T03:31:01Z | - |
dc.date.available | 2013-08-03 | |
dc.date.copyright | 2011-08-03 | |
dc.date.issued | 2011 | |
dc.date.submitted | 2011-07-29 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/32085 | - |
dc.description.abstract | 在醫學研究上,共存疾病為針對單一病人之指標疾病相關的附加疾病。目前有許多研究皆已證實單一疾病控管並非是病人最佳的治療方針,而共存疾病的診斷能提供更完備的病人健康狀態。因此探索指標疾病之可能共存疾病是相當重要的研究議題,亦能落實預防勝於治療之預防醫學理念。
臨床試驗與觀察性研究為主要的兩種共存疾病之研究方法。臨床試驗為一種需經妥善設計的研究流程,普遍上能提供較強制性的研究證據。然而,進行臨床試驗所花費的時間與經費是研究團隊最關注的問題;隨機分配處理至處理組與對照組更易引發道德爭議。觀察性研究為在現存的資料庫中所進行之研究,相較於臨床試驗,能耗費較少的時間與經費。但資料庫之收集過程常無進行觀察性研究的團隊參與,研究結果的信賴度容易因無嚴格的抽樣流程而受質疑。上述討論說明臨床試驗與觀察性研究能互相補足彼此優缺點,因此使用觀察性研究能提供一些線索以進行臨床試驗的設計。 本研究提出一種樣式探勘演算法,透過結合以族群為基數之大型醫療資料庫,偵測指標疾病可能相關之共存疾病。研究亦以老人型失智症為研究對象,討論演算法的共存疾病偵測效能。由研究結果說明使用本研究所提之樣式探勘演算法所偵測的關聯樣式,能提供醫學人士有價值的線索,可作為後續臨床試驗的重要設計依據。 | zh_TW |
dc.description.abstract | In medical study, comorbidities refer to the additional diseases a patient may suffer other than the primary disease of concern. In recent years, many medical studies have concluded that focusing on one single disease is not the most effective strategy for disease treatment and diagnosis of comobidities can provide a more comprehensive picture of the health condition of the patient. Accordingly, identifying possible comorbidities of the primary disease of concern is an issue of great significance and is essential for development of preventive medicine.
Clinical trial and observational study are the two principal methods for analysis of comorbidity. A clinical trial, which is conducted with a well-designed procedure, generally can provide compelling evidences. However, the cost and time involved in conducting a clinical trial may become a major concern for a research team to carry out such a study. Furthermore, a clinical trial typically involves randomly assigning the patients to the treated group or the control group and therefore may lead to an ethical controversy. On the other hand, an observational study is normally conducted with an existing dataset and therefore is substantially less costly and time consuming in comparison with a clinical trial. However, as the dataset employed in an observational study typically has been collected without the involvement of the research teams that use the dataset, the reliability of the results may be questioned, especially when the samples have not been selected with a rigorous procedure. The discussions above show that the clinical trial and the observational study complement each other in terms of their merits and deficiencies. Accordingly, an observational study can be conducted to collect some clues for the design of clinical trial. This thesis proposes a pattern mining algorithm for identifying the possible comobidities of the primary disease of concern in population-based mediccal databases. This thesis also discusses the effects of applying the proposed algorithm to identify the comobidities of senile dementia. Experimental results show that the comorbidity patterns identified by the proposed pattern mining algorithm provide medical personnel with valuable clues for designing follow-up clinical trials. | en |
dc.description.provenance | Made available in DSpace on 2021-06-13T03:31:01Z (GMT). No. of bitstreams: 1 ntu-100-R98945022-1.pdf: 823539 bytes, checksum: 415036f16fda67f0f91dc89cc488e6f7 (MD5) Previous issue date: 2011 | en |
dc.description.tableofcontents | 謝辭 I
摘要 II Abstract III 目錄 V 圖目錄 VII 表目錄 VIII Chapter 1緒論 1 Chapter 2相關研究 5 2.1健保資料庫 5 2.2應用健保資料庫之研究 7 2.3應用健保資料庫之資料探勘研究 10 2.3.1Association rule mining 11 2.4應用本研究之資料探勘演算法 13 2.4.1PrefixSpan 13 2.4.2K-means 14 Chapter 3研究方法 16 3.1資料集 16 3.2研究方法流程 17 3.2.1資料收集 18 3.2.1.1樣本抽樣 18 3.2.1.2疾病代碼過濾 19 3.2.2樣式探勘 21 3.2.2.1全類別探勘 22 3.2.2.2單一類別探勘 24 3.2.2.3全疾病探勘 25 3.2.3樣式評估 26 3.2.3.1樣式評估指標 26 3.2.3.2樣式篩選 27 3.2.4樣式彙整 30 3.2.4.1樣式分群 30 Chapter 4實驗結果與討論 34 4.1資料收集之結果 34 4.1.1探勘樣本之抽樣結果 34 4.1.2疾病代碼過濾之結果評估 35 4.2樣式評估之結果 36 4.2.1全類別探勘之樣式評估 36 4.2.2單一類別探勘之樣式評估 38 4.2.3全疾病探勘之樣式評估 43 4.3樣式彙整之結果 46 Chapter 5結論與未來展望 50 5.1結論 50 5.2未來展望 51 參考文獻 52 | |
dc.language.iso | zh-TW | |
dc.title | 應用樣式探勘演算法分析大型臨床資料庫之共存疾病 | zh_TW |
dc.title | A Novel Pattern-Based Comorbidity Mining Algorithm in Large-Scale Medical Database | en |
dc.type | Thesis | |
dc.date.schoolyear | 99-2 | |
dc.description.degree | 碩士 | |
dc.contributor.coadvisor | 黃乾綱 | |
dc.contributor.oralexamcommittee | 張天豪,孫維仁 | |
dc.subject.keyword | 共存疾病,預防醫學,臨床試驗,觀察性研究,樣式探勘,大型醫療資料庫, | zh_TW |
dc.subject.keyword | Comorbidity,Prevention medicine,Clinical trial,Observational study,Pattern mining,Large-scale medical database, | en |
dc.relation.page | 55 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2011-07-29 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 生醫電子與資訊學研究所 | zh_TW |
顯示於系所單位: | 生醫電子與資訊學研究所 |
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