以地區醫院病例探討桃園之地域族群與疾病之差別

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姓名

余佳杭(Chia-Hang Yu) 查詢紙本館藏

畢業系所

系統生物與生物資訊研究所

論文名稱

以地區醫院病例探討桃園之地域族群與疾病之差別
(Investigation of the Differences between Regional Groups and Diseases in Taoyuan from the Regional Hospital Records)

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摘要(中)

在近年，大數據研究已經逐漸成為顯學。利用資料探勘分析我們能夠找尋不同因子間互相的關聯，藉由數據佐證猜測。在醫學領域方面，我們能夠觀察疾病與可能因子間的關係。根據這些結果，可以為我們提供策略改善醫療，增進未來生醫領域的進步。
我們實驗室與壢新醫院合作，取得他們的歷年門診資料，其中的訊息包含病患的年齡、性別、教育程度與居住地，藉由這些大量門診資料，我們可利用機器學習的方法為患者進行分群，並篩選其中的有用規則。
本篇的研究著重於探討桃園不同居住地的患病情形，我將桃園地區依照行政區界線區分為沿海地區與內陸地區，並利用決策樹分群探討兩個地區的居民患病率差異，最終用統計學方法判斷其顯著性，最終篩選出兩個地區患病率差異規則顯著的族群。
在世界各地沿海與內陸患病情形有差異的原因主要為環境汙染、飲食習慣、城鄉差距、天災、個人習慣等，我將桃園地區之結果與世界上其他地方的結果做比較，交叉找尋在桃園地區沿海與內陸患病率差異的原因。
將所得之結果給予地區醫院進行參考，方便地區醫院未來針對這些特殊族群提供相應的醫療照顧與方案。

摘要(英)

In recent years, big data research has gradually become a prominent study. Using data mining, we can find the correlation between different factors, and use data to support the guess. In the medical field, we can observe the relationship between disease and possible factors. Based on these results, it can provide us with strategies to improve medical care and enhance the progress in the field of biomedicine in the future.
Our laboratory cooperates with Landseed Hospital to obtain their outpatient data over the years. The information includes the patient′s age, gender, education level and place of residence. With these large amounts of outpatient data, we can use machine learning methods for patient group and filter useful rules.
We focus on the prevalence of Taoyuan in different places of residence. We divided the Taoyuan area into coastal and inland areas according to the administrative area classification, and used decision trees to discuss the differences in the prevalence of residents in the two areas. Judging the significance of the study method, and finally screening out the demographic groups with significant differences in disease in the two regions.
The main reasons for the difference in the prevalence between coastal and inland areas around the world are environmental pollution, eating habits, urban-rural gaps, natural disasters, personal habits, etc. We compared the results of the Taoyuan area with the results of other parts of the world, and cross-examined the reasons for the difference in the prevalence of coastal and inland areas in the Taoyuan area.
Give the results to the regional hospitals for reference, so that the regional hospitals can provide corresponding medical care and programs for these special demographic groups in the future.

關鍵字(中)

★ 機器學習
★ 資料探勘
★ 關聯性
★ 門診疾病
★ 地域差異
★ 電子資料庫

關鍵字(英)

★ Machine learning
★ Data mining
★ Relevance
★ Outpatient disease
★ Regional difference
★ Electronic database

論文目次

Chinese abstract i
English abstract ii
List of Figures v
List of Tables vi
Chapter 1 Introduction 1
1-1 Motivation and Goal 1
1-2 Landseed′s outpatient database 2
Chapter 2 Methods and Material 3
2-1 Flow chart and methods 3
2-2 Data preprocessing 4
2-2-1 Data cleaning 4
2-2-2 Combine data into categories 4
2-2-3 Gender 5
2-2-4 Age level 5
2-2-5 Education level 5
2-2-6 Coastal or not 6
2-2-7 Disease history 6
2-3 WEKA 7
2-4 Decision tree 8
2-5 Database sampling 8
2-6 Category cross analysis 9
2-6-1 Relative risk and Odds ratio 9
2-6-2 Chi-square test and Yates correction 10
Chapter 3 Results 12
3-1 Basic statistics 12
3-2 Decision tree 14
3-2-1 Original data gram 14
3-2-2 Smaller data gram 22
Chapter 4 Discussion 27
References 28

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指導教授

吳立青(Li-Ching Wu)

審核日期

2020-8-20

推文