大數據分析糖尿病患者使用糖尿病藥物後得病因果關係和風險比較以桃園某地區醫院為例

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：12

、訪客IP：3.144.36.141

姓名

鄭仁威(Ren-Wei Jheng) 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

大數據分析糖尿病患者使用糖尿病藥物後得病因果關係和風險比較以桃園某地區醫院為例
(Causal relationship and risk comparison of diabetic patients′ illness after using diabetes medication using big data analytics: A case study of a hospital in Taoyuan, Taiwan)

相關論文

★ 中草藥BP004誘導管腔A型乳腺癌細胞凋亡	★ 藉由微陣列基因晶片以探討中草藥BP011w對於抑制肺腺癌細胞株爬行及轉移之機制
★ 鑑別可應用在病理與臨床之肺腺癌與鱗狀上皮細胞肺癌的生物標記	★ 中國傳統醫藥蒙古黃耆在HCT116結腸癌細胞體外和體內實驗呈現腫瘤抑制作用
★ 泰莫西芬與BP012W乙醇分離物之協同作用造成強化管狀A型乳腺癌細胞凋亡影響	★ 揭示CEP55基因在大腸直腸癌轉移中所扮演的角色
★ BP016W-新型食道鱗狀上皮細胞癌候選藥物	★ 傳統中藥複方FY001W是三陰性乳腺癌新型的候選藥物
★ BP023W在頭頸癌中的細胞毒性與調控機制	★ 藉由L1000?表達圖譜數據來解釋中醫分類方法中的屬性和歸經
★ Solasodine，BP010W成份之一，抑制肺癌的遷移和侵襲能力	★ 蒙古黃耆對大腸癌影響並降低miR-29a的表現量之研究
★ 利用生物資訊策略找出普濟方內加速傷口癒合的新配方	★ 藉助模塊化網路策略尋找普濟方之治療疾病核心配方
★ 探索 BP010W 的治療潛力:基於雌激素信號通路, 以澳洲茄胺作為抑製劑研究肺癌細胞遷移的綜合分析

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 (2029-1-8以後開放)

摘要(中)

糖尿病是患者體內的血糖水平異常高，台灣在亞洲地區18歲以上成人糖尿病盛行率11.1%為最高的國家，使用口服藥物的病患人數占糖尿病用藥人數的80%以上。透過醫院所提供的疾病代碼和用藥代碼來分析疾病與藥物的因果關係，在大數據的資料中透過卡方檢定、勝算比和格蘭傑因果關係這些統計分析來判斷使用不同的治療藥物是否會讓疾病得病風險增加或是下降。分析後文獻報導使用糖尿病藥物得腎功能不良所致疾患風險下降，使用胰島素藥物得充血性心臟衰竭風險下降，使用非胰島素藥物得肝炎風險下降，使用雙胍類得混合性高脂血症風險下降。其中我們找到文獻中沒發現的關係，使用糖尿病藥物得腎絞痛風險下降，使用胰島素藥物得支氣管性肺炎風險下降，使用非胰島素藥物得其他慢性過敏性結膜炎風險下降，使用雙胍類得散光風險下降。從結果來看分析的流程不只可以驗證已知結果，並且發現未知的藥物與疾病關係，未來可以加入更多資料讓分析更精準，透過其他外部監測裝置的輔助，能夠告知病人接下來可能得病的風險並提早做預防。

摘要(英)

Diabetes is characterized by high blood sugar levels. Taiwan has the highest prevalence of diabetes in Asia, with a rate of 11.1% among adults over 18 years old. The majority of diabetes patients, over 80%, use oral medications for treatment. By analyzing disease and medication codes using statistical methods like chi-square tests, odds ratios, and Granger causality in big data, we can determine the causal relationship between diseases and drugs and assess whether different treatment drugs increase or decrease the risk of disease. Based on the literature analysis, it has been reported that diabetes drugs reduce the risk of diseases related to poor renal function, insulin drugs reduce the risk of congestive heart failure, non-insulin drugs reduce the risk of hepatitis, and biguanides reduce the risk of mixed hyperlipidemia. Among the findings, we discovered relationships that were not previously documented in the literature. The use of diabetes drugs was associated with a reduced risk of renal colic, insulin drugs were associated with a reduced risk of bronchopneumonia, non-insulin drugs were associated with a reduced risk of other chronic allergic conjunctivitis, and biguanides were associated with a reduced risk of astigmatism. These findings indicate that the analysis process has the potential to verify known results and uncover previously unknown relationships between drugs and diseases. Increasing the amount of data in future analyses can further enhance the accuracy of the findings. With the aid of additional external monitoring devices, patients can be alerted to potential disease risks in advance and take proactive precautions.

關鍵字(中)

★ 糖尿病
★ 數據分析
★ 共病關係
★ 胰島素藥物
★ 卡方檢定
★ 格蘭傑因果關係

關鍵字(英)

★ Diabetes
★ Data Analysis
★ Comorbidities
★ Insulin Medication
★ Chi-square test
★ Granger causality test

論文目次

目錄
中文摘要 I
ABSTRACT II
誌謝 III
目錄 IV
表目錄 VI
圖目錄 VII
一、緒論 1
1-1研究背景 1
1-2 研究動機 2
1-3研究目的 3
二、文獻探討 4
2-1糖尿病 4
2-2大數據分析 5
2-3 胰島素藥物治療 6
2-4非胰島素藥物治療 7
2-5藥物副作用導致疾病 8
三、研究材料與方法 10
3-1研究流程 10
3-2研究資料 10
3-2 國際疾病分類代碼 11
3-3 解剖學治療學及化學分類系統 15
3-4 程式語言 17
3-5卡方檢定 18
3-6格蘭傑因果檢定 19
3-7研究方法 20
四、結果 22
4-1二型糖尿病與糖尿病藥物關係 22
4-2二型糖尿病共病疾病 22
4-3二型糖尿病用藥與第二疾病的共病關係 23
4-4胰島素用藥共病疾病 23
4-5 非胰島素藥物共病疾病 24
4-6 非胰島素藥物次分類共病疾病 25
五、討論 27
5-1分析遇到的問題 27
5-2分析後的發現 29
5-3未來展望 33
六、參考文獻 37
表目錄
表 1. 桃園某地區看診紀錄範例 48
表 2. 病人患病檔範例 49
表 3. 病人用藥檔範例 50
表 4. ICD- 9代碼分類及代碼範圍 51
表 5. ATC 代碼編碼以及內容 52
表 6. 二型糖尿病與糖尿病藥物因果關係 53
表 7. 二型糖尿病與第二疾病卡方檢定 54
表 8. 糖尿病藥物與第二個疾病因果關係 62
表 9. 胰島素藥物導致疾病因果關係(1/2) 63
表 10. 非胰島素藥物有因果關係疾病(1/2) 65
表 11. 醫院使用非胰島素藥物情況 67
表 12. 非胰島素藥物次分類疾病風險 69

圖目錄
圖 1. 實驗總流程圖 41
圖 2. 二型糖尿病患者篩選流程圖 42
圖 3. (A)Disease與Medicine期望值 (B) 卡方檢定(chi-square test) 43
圖 4. 二型糖尿病與糖尿病用藥關係流程圖 44
圖 5. 卡方檢定和格蘭傑因果關係檢定流程圖 45
圖 6. 糖尿病藥物與第二疾病流程圖 46
圖 7. 醫院使用非胰島素藥物 47

參考文獻

[1] J. R. Petrie, T. J. Guzik, and R. M. Touyz, "Diabetes, hypertension, and cardiovascular disease: clinical insights and vascular mechanisms," Canadian Journal of Cardiology, vol. 34, no. 5, pp. 575-584, 2018.
[2] C.-Y. Chou, D.-Y. Hsu, and C.-H. Chou, "Predicting the onset of diabetes with machine learning methods," Journal of Personalized Medicine, vol. 13, no. 3, p. 406, 2023.
[3] K.-T. Chen, C.-J. Chen, E. W. Gregg, M. M. Engelgau, and K. V. Narayan, "Prevalence of type 2 diabetes mellitus in Taiwan: ethnic variation and risk factors," Diabetes research and clinical practice, vol. 51, no. 1, pp. 59-66, 2001.
[4] K. Kaul, J. M. Tarr, S. I. Ahmad, E. M. Kohner, and R. Chibber, "Introduction to diabetes mellitus," Diabetes: an old disease, a new insight, pp. 1-11, 2013.
[5] A. D. A. P. P. Committee and A. D. A. P. P. Committee:, "2. Classification and diagnosis of diabetes: Standards of Medical Care in Diabetes—2022," Diabetes care, vol. 45, no. Supplement_1, pp. S17-S38, 2022.
[6] J. Yang et al., "Brief introduction of medical database and data mining technology in big data era," Journal of Evidence-Based Medicine, vol. 13, pp. 57 - 69, 2020.
[7] S. Dağaşan and O. Erbaş, "Insulin structure, function and diabetes models in animals," Journal of Experimental and Basic Medical Sciences, vol. 1, no. 3, pp. 096-101, 2020.
[8] J. M. Tibaldi, "Evolution of insulin: from human to analog," The American journal of medicine, vol. 127, no. 10, pp. S25-S38, 2014.
[9] Y. Du, Y.-J. Zhu, Y.-X. Zhou, J. Ding, and J.-Y. Liu, "Metformin in therapeutic applications in human diseases: Its mechanism of action and clinical study," Molecular Biomedicine, vol. 3, no. 1, p. 41, 2022.
[10] T. Lavabre-Bertrand and J.-L. Faillie, "The discovery of hypoglycaemic sulphonamides–Montpellier, 1942," Therapies, vol. 76, no. 6, pp. 559-566, 2021.
[11] U. Hossain, A. K. Das, S. Ghosh, and P. C. Sil, "An overview on the role of bioactive α-glucosidase inhibitors in ameliorating diabetic complications," Food and chemical toxicology, vol. 145, p. 111738, 2020.
[12] S. V. Arnold, S. E. Inzucchi, J. B. Echouffo-Tcheugui, F. Tang, C. S. Lam, L. S. Sperling, and M. Kosiborod, "Understanding contemporary use of thiazolidinediones: an analysis from the diabetes collaborative registry," Circulation: Heart Failure, vol. 12, no. 6, p. e005855, 2019.
[13] D. J. Drucker, "Mechanisms of action and therapeutic application of glucagon-like peptide-1," Cell metabolism, vol. 27, no. 4, pp. 740-756, 2018.
[14] R. Yin, Y. Xu, X. Wang, L. Yang, and D. Zhao, "Role of dipeptidyl peptidase 4 inhibitors in antidiabetic treatment," Molecules, vol. 27, no. 10, p. 3055, 2022.
[15] S. Feijóo-Bandín et al., "Role of sodium-glucose co-transporter 2 inhibitors in the regulation of inflammatory processes in animal models," International Journal of Molecular Sciences, vol. 23, no. 10, p. 5634, 2022.
[16] M. Feng, H. Lv, X. Xu, J. Wang, W. Lyu, and S. Fu, "Efficacy and safety of dapagliflozin as monotherapy in patients with type 2 diabetes mellitus: a meta-analysis of randomized controlled trials," Medicine, vol. 98, no. 30, 2019.
[17] B. Jones, S. R. Bloom, T. Buenaventura, A. Tomas, and G. A. Rutter, "Control of insulin secretion by GLP-1," Peptides, vol. 100, pp. 75-84, 2018.
[18] W. H. Organization, International classification of diseases:[9th] ninth revision, basic tabulation list with alphabetic index. World Health Organization, 1978.
[19] J. DiSantostefano, "International classification of diseases 10th revision (ICD-10)," The Journal for Nurse Practitioners, vol. 5, no. 1, pp. 56-57, 2009.
[20] M. S. C. Almeida, L. F. d. Sousa Filho, P. M. Rabello, and B. M. Santiago, "International Classification of Diseases–11th revision: from design to implementation," Revista de Saúde Pública, vol. 54, p. 104, 2020.
[21] I. S. Sketris, C. J. Metge, J. L. Ross, M. E. MacCara, D. G. Comeau, G. C. Kephart, and J. L. Blackburn, "The use of the World Health Organisation anatomical therapeutic chemical/defined daily dose methodology in Canada," Drug information journal: DIJ/Drug Information Association, vol. 38, no. 1, pp. 15-27, 2004.
[22] G. Van Rossum and F. L. Drake, An introduction to Python. Network Theory Ltd. Bristol, 2003.
[23] M. L. McHugh, "The chi-square test of independence," Biochemia medica, vol. 23, no. 2, pp. 143-149, 2013.
[24] D. C. Howell, "Chi-Square Test: Analysis of Contingency Tables," ed, 2011.
[25] A. K. Seth, A. B. Barrett, and L. Barnett, "Granger causality analysis in neuroscience and neuroimaging," Journal of Neuroscience, vol. 35, no. 8, pp. 3293-3297, 2015.
[26] L. Colas-Campas et al., "The rs2108622 polymorphism is related to the early risk of ischemic stroke in non-valvular atrial fibrillation subjects under oral anticoagulation," The Pharmacogenomics Journal, vol. 18, no. 5, pp. 652-656, 2018.
[27] S. W. Quist, A. V. van Schoonhoven, S. J. Bakker, M. Pochopień, M. J. Postma, J. M. van Loon, and J. H. Paulissen, "Cost-effectiveness of finerenone in chronic kidney disease associated with type 2 diabetes in The Netherlands," Cardiovascular Diabetology, vol. 22, no. 1, p. 328, 2023.
[28] Z. Cai et al., "Metformin potentiates nephrotoxicity by promoting NETosis in response to renal ferroptosis," Cell Discovery, vol. 9, no. 1, p. 104, 2023.
[29] I. Kapoor, S. M. Sarvepalli, D. D’Alessio, D. S. Grewal, and M. Hadziahmetovic, "GLP-1 receptor agonists and diabetic retinopathy: A meta-analysis of randomized clinical trials," Survey of Ophthalmology, 2023.
[30] D. Control and C. T. R. Group, "The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus," New England journal of medicine, vol. 329, no. 14, pp. 977-986, 1993.
[31] L. Shen et al., "Insulin treatment and clinical outcomes in patients with diabetes and heart failure with preserved ejection fraction," European journal of heart failure, vol. 21, no. 8, pp. 974-984, 2019.
[32] T. Sen et al., "Effects of the SGLT2 inhibitor canagliflozin on plasma biomarkers TNFR-1, TNFR-2 and KIM-1 in the CANVAS trial," Diabetologia, vol. 64, no. 10, pp. 2147-2158, 2021.
[33] V. Perkovic et al., "Canagliflozin and renal outcomes in type 2 diabetes and nephropathy," New England Journal of Medicine, vol. 380, no. 24, pp. 2295-2306, 2019.
[34] D. Lovic et al., "Sodium-glucose cotransporter 2 inhibitors: potential cardiovascular and mortality benefits," Cardiovascular & Haematological Disorders-Drug Targets (Formerly Current Drug Targets-Cardiovascular & Hematological Disorders), vol. 18, no. 2, pp. 114-119, 2018.
[35] W.-Q. Ma, X.-J. Sun, Y. Zhu, and N.-F. Liu, "Metformin attenuates hyperlipidaemia-associated vascular calcification through anti-ferroptotic effects," Free Radical Biology and Medicine, vol. 165, pp. 229-242, 2021.
[36] P.-C. Tsai et al., "Metformin reduces hepatocellular carcinoma incidence after successful antiviral therapy in patients with diabetes and chronic hepatitis C in Taiwan," Journal of Hepatology, vol. 78, no. 2, pp. 281-292, 2023.
[37] S. B. Han, H. K. Yang, and J. Y. Hyon, "Influence of diabetes mellitus on anterior segment of the eye," Clinical interventions in aging, pp. 53-63, 2019.
[38] Y. Kawahara et al., "Effects of sulfonylureas on periodontopathic bacteria-induced inflammation," Journal of Dental Research, vol. 99, no. 7, pp. 830-838, 2020.
[39] K. Ejiri et al., "The effect of luseogliflozin and alpha-glucosidase inhibitor on heart failure with preserved ejection fraction in diabetic patients: rationale and design of the MUSCAT-HF randomised controlled trial," BMJ open, vol. 9, no. 3, p. e026590, 2019.
[40] N. Fukaya, K. Mochizuki, M. Shimada, and T. Goda, "The α-glucosidase inhibitor miglitol decreases glucose fluctuations and gene expression of inflammatory cytokines induced by hyperglycemia in peripheral leukocytes," Nutrition, vol. 25, no. 6, pp. 657-667, 2009.
[41] T. Nozue et al., "Effects of sitagliptin on coronary atherosclerosis in patients with type 2 diabetes-A serial integrated backscatter-intravascular ultrasound study," American Journal of Cardiovascular Disease, vol. 6, no. 4, p. 153, 2016.
[42] N. Chattipakorn, N. Apaijai, and S. C. Chattipakorn, "Dipeptidyl peptidase-4 inhibitors and the ischemic heart: additional benefits beyond glycemic control," International Journal of Cardiology, vol. 202, pp. 415-416, 2016.
[43] M.-T. Wang et al., "The impact of DPP-4 inhibitors on long-term survival among diabetic patients after first acute myocardial infarction," Cardiovascular Diabetology, vol. 16, no. 1, pp. 1-11, 2017.
[44] X. Hu, X. Wang, and X. Xue, "Therapeutic perspectives of CD26 inhibitors in imune-mediated diseases," Molecules, vol. 27, no. 14, p. 4498, 2022.
[45] D. Paul, G. Sanap, S. Shenoy, D. Kalyane, K. Kalia, and R. K. Tekade, "Artificial intelligence in drug discovery and development," Drug discovery today, vol. 26, no. 1, p. 80, 2021.
[46] L. Zhang, J. Tan, D. Han, and H. Zhu, "From machine learning to deep learning: progress in machine intelligence for rational drug discovery," Drug discovery today, vol. 22, no. 11, pp. 1680-1685, 2017.
[47] Y. Wang, Y. Guo, Q. Kuang, X. Pu, Y. Ji, Z. Zhang, and M. Li, "A comparative study of family-specific protein–ligand complex affinity prediction based on random forest approach," Journal of computer-aided molecular design, vol. 29, pp. 349-360, 2015.

指導教授

蘇立仁(Li-Jen Su)

審核日期

2024-1-9

推文