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    Please use this identifier to cite or link to this item: http://ccur.lib.ccu.edu.tw/handle/A095B0000Q/488


    Title: 診斷重度憂鬱症之患者後續更改診斷為雙極性疾患之預測因子分析:一個全國性的回溯型研究;Predictors for Switch From Unipolar Major Depressive Disorder to Bipolar Disorder: A Nationwide Population-based Retrospective Cohort Study
    Authors: 沈正哲;Shen, Cheng-Che
    Contributors: 資訊管理系醫療資訊管理研究所
    Keywords: 重度憂鬱症;雙極性疾患;全民健康保險資料庫;資料探勘;關聯規則;分類與迴歸樹法;major depressive disorder;bipolar disorder;national health insurance database;data mining;association rule mining;classification and regression tree
    Date: 2017
    Issue Date: 2019-07-17
    Publisher: 資訊管理系醫療資訊管理研究所
    Abstract: 重度憂鬱症及雙極性疾患為主要的兩種情緒疾患,這兩種疾病有不同的治療策略及預後,然而,雙極性疾患可能以鬱症發作作為初次發病的表現,因而被診斷為重度憂鬱症,進一步造成治療失敗,先前的研究也指出有一定比例被診斷為重度憂鬱症的患者,在追蹤一段時間後,會被診斷成雙極性疾患,這類「隱藏」的雙極性疾患患者可能是導致治療失敗的原因之一。  本研究為使用台灣健康保險資料庫所做的全國性研究,從台灣健康保險資料庫中找出診斷為重度憂鬱症的個案,並追蹤後續更改診斷為雙極性疾患的比率,同時利用診斷重度憂鬱症前的臨床資料,找出與診斷轉換相關的預測因子,本研究使用關聯規則來發掘與診斷轉換相關的臨床特徵,並使用分類與迴歸樹法來發展診斷轉換的風險分類模組。  本研究一共納入2820位重度憂鬱症個案,其中61%為女性個案,在追蹤期間,536位個案被診斷為雙極性疾患 (19%)。在關聯規則的結果顯示,2月、3月、4月、5月、8月、9月、10月、11月的平均精神科門診次數較多、年齡介於20歲至39歲間、每年平均住院次數較多、每年平均使用的鎮靜劑種類較多等變數,和診斷轉換較為相關。此外,分類與迴歸樹法發現利用總精神科門診次數、3月平均門診次數、2月平均精神科門診次數、8月平均精神科門診次數、秋季平均門診次數、以及平均使用鎮靜劑種類等六個變數,能將重度憂鬱症個案後續是否會更改診斷為雙極性疾患的風險分為高、中、低三組,此風險分類模組可被便捷地在臨床情境中使用,並協助醫師早期正確地診斷出雙極性疾患患者,以安排適當地治療。
    Unipolar major depressive disorder (MDD) and bipolar disorder are two major mood disorders. The two disorders have different treatment strategies and prognoses. However, bipolar disorder may begin with depression and could be diagnosed as MDD at the initial stage which may contribute to treatment failure. Previous studies indicated that a significant proportion of patients who were diagnosed with MDD will over time develop bipolar disorder. This kind of hidden bipolar disorder may contribute to the treatment resistance observed in MDD patients. In this population-based study, our aim is to investigate the rate and risk factors for a diagnostic change from unipolar MDD to bipolar disorder during a 10-year follow up using Taiwan National Health Insurance Research Database (NHIRD). Association rule mining (ARM) was used to discover the associations of bipolar conversion and clinical characteristics before enrollment. Furthermore, a risk stratification model was also developed for MDD to bipolar conversion by using the classification and regression trees (CART) method. There are 2820 MDD patients enrolled in our study, among whom 60.1% were women. During follow-up period, 536 patients was diagnosed with bipolar disorder (19.0%). The results of ARM showed that variables including mean psychiatric outpatient visits of February, March, April, May, August, September, October, and November, age between 20 and 39 years, mean annual hospitalizations, and mean annual use of benzodiazepine, composed association rules discovered in our work. Furthermore, the CART method identified 6 variables (total psychiatric outpatient visits, mean outpatient visits of March, mean psychiatric outpatient visits of fall, February, and August, and mean annual use of benzodiazepines) as significant predictors of risk of bipolar conversion. Using these variables, we could group patients into low, intermediate, or high risk for bipolar conversion. The risk stratification model can be easily applied in clinical practice and help to identify patients with bipolar disorder early and to arrange appropriate treatment for these patients.
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