建構一個結合疾病網路與氣候關聯疾病暨藥物影響性的互動式視覺化系統

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姓名

張伯任(Po-Jen Chang) 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

建構一個結合疾病網路與氣候關聯疾病暨藥物影響性的互動式視覺化系統
(Construction of an interactive and visual system: combine disorders with climate changes and prescribing drugs effect into disease network)

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摘要(中)

台灣擁有著名的全民健保計畫，並且幾乎涵蓋了所有的台灣公民。不同於其他保險公司，醫院的全民健保病歷資料儲存了所有不論是富有、貧窮、年輕、老年病人的ICD9 疾病代碼。本研究針對地區醫院的病歷資料進行分析，並且整合當地的天氣資訊。地區醫院具有特定看診人口、天氣型態的區域特性，因此本研究結果更能體現地區醫院所在之地域的獨特疾病型態。透過分析各天氣條件下看診人數的變化趨勢，我們可以瞭解疾病好發於何種氣候狀況，並且探索新的、潛在的季節性疾病。許多疾病彼此之間可能是相互關聯的，而這樣的關聯性可以透過分析臨床資料來取得，並且透過結合所有可能關聯的疾病來衍生出疾病網路。然而可能有許多因子可能涉及並影響疾病間的關聯性，藥物就是其中一個重要的因素。本研究分析開立藥物與疾病間的影響性，並結合至疾病網路，建立藥物-疾病網路。我們的研究協助醫生在進行臨床診斷時根據環境與藥物等因子來決定治療方針，並進行後續風險評估。

摘要(英)

Taiwan has a famous National Health Insurance (NHI) program which covers almost all citizens. Unlike other data from insurance company, the NHI diagnosis record of hospitals store International Statistical Classification of Diseases and Related Health Problems version 9 (ICD9) codes of every patients no matter rich, poor, young, or old, which form a unbiased sampling of disease occurrences. This study analyzed NHI medical record of local hospital and integrated with local weather condition information. Local hospital had a characteristic with specific population of admission and weather conditions, our result represent unique patterns of diseases occurrence on local hospital region. The seasonal predilection of diseases could be discovered through analyzing the variation of number of admission on different weather conditions, also investigated potential new seasonal diseases. The diseases network could be derived from analyzing the relevance between diseases by clinical data. However, several factors may involve the relevance of diseases, and drug is one of important factor. This study further analyzed prescribing drug effect relevance of diseases, and combined into disease network to construct a drugs-disorders network. Our research assisted doctors to make decision on treatment strategy and evaluate risk when clinical diagnosis according to environment and prescribing drug factors.

關鍵字(中)

★ 疾病網路
★ 季節性疾病
★ 藥物影響性
★ 醫療紀錄
★ ICD9

關鍵字(英)

★ disease network
★ seasonal disease
★ effect of drugs
★ medical record
★ ICD9

論文目次

中文摘要 I
English abstract II
致謝 III
Tables of content IV
List of figures V
List of tables VI
Chapter 1 Introduction 1
Chapter 2 Material and Methods 4
2-1 Collection of medical and medication administration record 4
2-2 Collection of climate data 4
2-3 Construct disease network 4
2-3-1 Data preprocessing 4
2-3-2 To separate row data based on ICD9 codes 6
2-3-3 Disease pairs generated and date difference of diseases pairs 7
2-3-4 Statistics and directivity of disease pairs 10
2-4 Analysis of seasonal disease 13
2-5 Analysis of drug affects the development of following stage diseases 14
2-6 Thresholds of disease pairs 15
2-7 To identify effects of drugs 16
Chapter 3 Results 17
3-1 Website 17
3-1-1 Summary table of diseases 18
3-1-2 Summary table of result of seasonal disease analysis 20
3-1-3 Summary table of information of drugs and prescribing 21
3-1-4 Interactive network visualization 22
3-2 Result of seasonal disease analysis 25
3-3 Case study: Atorvastatin affects development of following stage diagnosis 31
3-3-1 Information of Atorvastatin and prescribing in CGH 31
3-3-2 Common adverse effects of atorvastatin calcium found in our research 33
3-3-3 Possible adverse effects of atorvastatin calcium found in our research 34
3-3-4 Other possibly promote or inhibit adverse effects of atorvastatin calcium 36
3-3-5 Atorvastatin-induced renal failure acute and 38
Chapter 4 Discussion and conclusion 40
References 43

Figure 1. Procedure of data integration 5
Figure 2. Procedure of data separation Two examples shown in ICD9-7847 and ICD9-4019 6
Figure 3. Construction of disease pairs 8
Figure 4. Date differences calculation and separation of diseases pair 9
Figure 5. An example of direction definition in disease pair (ICD9-57420 and ICD9-5699) 12
Figure 6. The analysis diagram of seasonal disease 13
Figure 7. Selection of disease pairs 15
Figure 8. Four modules of website system 17
Figure 9. Screen of our website 19
Figure 10. Relationship between diseases and climate 20
Figure 11. The information of prescribing drugs 21
Figure 12. To introduce the components of network visualization in our system 23
Figure 13. The diagrams of exploring subnetworks 24
Figure 14. Top 10 positive temperature-related diseases based in monthly patients admission 27
Figure 15. Top 10 negative sunshine-related diseases based on monthly patients 29
Figure 16. Overview of drugs-diseases subnetwork via atorvastatin calcium 33
Figure 17. Possible adverse effects in promotion and inhibition subnetwork of atorvastatin calcium 37
Figure 18. Development of renal failure acute and CKD related to atorvastatin prescribing 39

Table 1. Prescribed drugs related disease pairs 14
Table 2. The effect of prescribing drugs related disease pairs 16
Table 3. Classification of ICD-9 categories 18
Table 4. Top 10 positive temperature-related diseases or symptoms 26
Table 5. Top 10 positive temperature-related diseases based on monthly patients admission 27
Table 6. Top 5 temperature-related ICD9 categories 28
Table 7. Top 10 negative sunshine-related diseases or symptoms based on result of all diagnosis 28
Table 8. Top 10 negative sunshine-related diseases based on monthly patients admission 29
Table 9. Top 5 sunshine-related ICD9 categories 30
Table 10. Prescriptions of atorvastatin calcium (Lipitor) are associated with clinical usage 32
Table 11. Common adverse effects of atorvastatin calcium found in our research 34
Table 12. Possible adverse effects of atorvastatin calcium 35

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指導教授

吳立青(Li-Ching Wu)

審核日期

2016-7-27

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