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題名：健康資訊化的大數據分析研究-以記憶力、注意力及睡眠失調為例
作者：潘庭問
作者(外文)：Dinh-Van Phan
校院名稱：元智大學
系所名稱：資訊管理學系
指導教授：詹前隆
學位類別：博士
出版日期：2018
主題關鍵詞：Memory；Attention；Sleep Disorder；Deep learning；Big data；machine learning；Taiwan’s National Health Insurance Data；記憶力；注意力；睡眠障礙；深度學習；機器學習；大數據；台灣全民健康保險研究資料庫
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大數據分析為我們生活中的許多領域創造了機會和挑戰，例如科學、商業、醫療，健康保健等等。因此，如果我們了解大數據分析技術，可以在業界中創造優勢，在研究中增加更多價值。我們利用健保資料庫(National Health Insurance Research Database, NHIRD)分析了醫療保健和疾病診斷中的大數據，已做了3項研究，分別為日常身體活動對記憶力和注意力的影響、每日睡眠對記憶力和注意力的影響以及2001至2010年台灣氣喘患者的隊列研究。最後本研究在2002至2010年氣喘患者的睡眠障礙預測中，應用了傳統的機器學習和深度學習的演算法進行比較。
本文指出了日常體力活動，日常睡眠和記憶力、注意力之間的關聯。結果表明，記憶容量、注意力測試時間（TSAT）和高活動持續時間（VATD）之間存在顯著的負相關。在測試日前一天燃燒的卡路里分別為（r = 0.289, r = 0.254, r = -0.272, r = -0.176），而在測試日期前一周燃燒的卡路里值分別為（r = 0.268, r = 0.241, r = -0.364, r = -0.395）。此外，最佳區分在高活動持續時間的正常、低注意力之臨界值為測試前一天攝取之熱量≥2283卡路里、活動分鐘數≥ 20分鐘，或至測試前一周為止，每週攝取之總熱量≥13,640卡路里、每週活動分鐘數≥76分鐘。
該研究亦表明在測試日期和測試日期前一周內，記憶力與清醒次數之間存在顯著的負相關（分別為r = -0.153, r = -0.391）；而在測試日期和測試日期前一周的睡眠時間與記憶力顯著正相關（分別為r = 0.127，r = 0.370）；此外，在測試日期之前平均睡眠時間大於6小時42分鐘或者在測試日期前一周平均每天睡眠時間大於6小時37分鐘的受試者會有更好的記憶力。
在氣喘的研究中，年平均百分比變化（Average annual percentage changes, AAPCs）顯示，從2001年到2010年急診（ED）和非急診（non-ED）的氣喘患者數量分別顯著增加為2.3％和4.6％。依照醫院分類的氣喘就醫率顯示，當地醫院和其他醫院呈現出顯著的增長趨勢（AAPCs=15.3％），並且在兒童組的盛行率有最為顯著的增加（AAPCs=3.9％）。
將三種廣泛運用在各領域的機器學習算法如K-鄰近演算法、支援向量機、隨機森林以及深度學習方法應用於2002至2010年的氣喘隊列研究中預測睡眠障礙。結果表明，卷積神經網路（Convolutional Neural Networks, CNN）在上述的方法中達到最高的準確率（>92.3％）
Big data analysis has been creating opportunities and challenges for many fields in our lives such as science, business, medical, healthcare, etc. Thus, if we know big data analysis techniques, we may create advantages in business, add more values in research, etc. This dissertation focused on the analysis of the big data in health care and disease diagnosis. It surveys the effect of daily physical activities as well as daily sleep on memory and attention capacities. This dissertation also studied asthma patient cohort from 2001-2010 in Taiwan base on NHIRD. Eventually, it applied traditional machine learning algorithms and deep learning to sleep disorder prediction in new asthma cohort from 2002-2010.
This dissertation indicated associations between daily physical activities, daily sleep and memory, attention capacity. The results show that there are significant negative correlations between memory capacity, time spent on the attention test (TSAT) and very active time duration (VATD), calories burnt on the day before the test date (r = 0 289, r = 0 254, r = −0 272, r = −0 176, respectively) and during the week before the test date (r = 0 268, r = 0 241, r = −0 364, r = −0 395, respectively). In addition, the thresholds to best discriminate between normal-to-good and low attention capacity of the VATD and the calories burnt per day were ≥2283 calories, ≥20 minutes on the day before testing, or ≥13,640 calories per week, ≥76 minutes per week during the week before the test date. The findings indicate the short-term effects that VATD and calories burnt on the day before or during the week before the test date significantly and negatively associated with memory and attention capacities of college students.
This study also indicates a significant negative correlation between memory capacity and awake count on the test date and during the week before the test date (r = −0.153, r = −0.391, respectively). However, the minutes asleep on the test date and during the week before the test date significantly positively associates with memory capacity (r = 0.127, r = 0.370, respectively). Furthermore, spending > 6 hours and 42 minutes asleep on the test date or > 6 hours and 37 minutes asleep per day on average during the week before the test date result in better memory capacity.
Average annual percentage changes (AAPCs) in asthma cohort has shown that the number of asthma patients visiting emergency (ED) and non-emergency (non-ED) clinics significantly increased (2.3% and 4.6%, respectively) from 2001-2010. The asthma visits classified by hospital level showed that the local hospitals and others exhibited a significant increasing trend (AAPC=15.3%). The annual prevalence of children group was the highest significantly increased (AAPC=3.9%).
Three widely-used machine learning algorithms K-Nearest Neighbors, Support Vector Machine, Random forest, and deep learning models were applied to the new asthma cohort from 2002-2010 for predicting sleep disorder. The results show that CNN models achieve the highest accuracy (>92.3%).
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臺灣人文及社會科學引文索引資料庫

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