博碩士論文 111423047 詳細資訊




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姓名 楊于璇(Yu-Hsuan Yang)  查詢紙本館藏   畢業系所 資訊管理學系
論文名稱 腦中風患者之心房顫動預測模型開發與驗證:使用自然語言處理與機器學習技術
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摘要(中) 突發性腦血管疾病,俗稱中風,是全球第二大致命原因和第三大導致失能的原因,而在台灣則是第五大致命原因。其中,最常復發者為缺血性中風。心房顫動是中風的潛在風險因素之一,由於其具有陣發性或無症狀的特點,難以在短時間內進行檢測,這可能導致患者未能及時採取應對措施,進一步增加再次中風的風險。考慮到醫療資源有限,因此需要建立一個良好的風險預測模型,以幫助醫師更詳細地對高風險患者進行檢查。本研究的目的是利用電子病歷中的結構化和非結構化資料,透過不同的分類技術建立預測模型,並使用實際的電子病歷數據進行驗證。除了在聯新國際醫院進行內部驗證外,考慮到模型的通用性,另外將使用嘉義基督教醫院的資料進行外部驗證。
摘要(英) Acute cerebrovascular disease, commonly known as stroke, ranks as the second leading cause of death globally and the third leading cause of disability. In Taiwan, it is the fifth leading cause of death. The most common recurrence among these cases is ischemic stroke. Atrial fibrillation is identified as one of the key risk factors for stroke. However, due to its intermittent or asymptomatic presentation, detecting it promptly is challenging, potentially delaying necessary interventions and heightening the risk of recurrent stroke. Given these challenges and the constraints of medical resources, it is imperative to develop a robust risk prediction model. Such a model would aid physicians in conducting thorough assessments of high-risk patients. This study aims to leverage both structured and unstructured data from electronic health records to develop predictive models using various classification techniques. Validation will be performed using real-world electronic health record data. Internal validation will initially take place within the same hospital, with external validation planned using data from other hospitals to ensure the model′s generalizability across different settings.
關鍵字(中) ★ 心房顫動
★ 腦中風
★ 電子病歷
★ 文字探勘
★ 臨床支援決策
關鍵字(英) ★ Atrial Fibrillation
★ Stroke
★ Electronic Medical Records
★ Text Mining
★ Clinical decision support system
論文目次 摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 vi
表目錄 vii
第一章、緒論 1
1.1 研究背景 1
1.2 研究動機 3
1.3 研究目的 5
第二章、文獻探討 6
2.1 電子病歷用於臨床決策支援之研究 6
2.2 心房顫動預測應用之研究 9
2.3 結構化與非結構化資料用於預測模型之研究 14
第三章、研究方法 17
3.1 資料來源 19
3.2 依變數定義 20
3.3 自變數定義 22
3.4 文字前處理 24
3.4.1 Term Frequency-Inverse Document Frequency (TF-IDF) 25
3.4.2 Doc2Vec (D2V) 26
3.4.3 MetaMap 27
3.4.4 Bidirectional Encoder Representations from Transformers (Bert) 29
3.5 訓練集與測試集切分 30
3.6 分類技術 31
3.6.1 支援向量機(Support Vector Machine, SVM) 32
3.6.2 簡單貝氏(Naive Bayes, NB) 32
3.6.3 決策樹(Decision Tree, DT) 33
3.6.4 隨機森林(Random Forest, RF) 33
3.6.5 邏輯迴歸(Logistic Regression, LR) 35
3.6.6 K-近鄰演算法(K-Nearest Neighbor, KNN) 35
3.6.7 極限梯度提升(Extreme Gradient Boosting, XGB) 35
3.6.8 類別型梯度提升(Categorical Boosting, CatBoost) 36
3.6.9 輕量化梯度提升(Light Gradient Boosting Machine, LightGBM) 36
3.7 實驗設計與分析技術 36
3.8 預測模型評估指標 38
第四章、實驗結果 40
4.1 敘述式統計與檢定 40
4.2 實驗結果 42
4.2.1 實驗1 45
4.2.2 實驗2 47
4.2.3 實驗3 49
4.3 討論 53
第五章、研究結論與建議 66
5.1 研究結論 66
5.2 研究限制 67
5.3 未來研究方向與建議 67
第六章、參考文獻 68
附錄一 96
附錄二 97
附錄三 98
附錄四 99
附錄五 100
附錄六 101
附錄七 102
參考文獻 Alonso, A., Agarwal, S. K., Soliman, E. Z., Ambrose, M., Chamberlain, A. M., Prineas, R. J., & Folsom, A. R. (2009). Incidence of atrial fibrillation in whites and African-Americans: The Atherosclerosis Risk in Communities (ARIC) study. American Heart Journal, 158(1), 111–117. https://doi.org/10.1016/j.ahj.2009.05.010
Alonso, A., Krijthe, B. P., Aspelund, T., Stepas, K. A., Pencina, M. J., Moser, C. B., Sinner, M. F., Sotoodehnia, N., Fontes, J. D., Janssens, A. C. J. W., Kronmal, R. A., Magnani, J. W., Witteman, J. C., Chamberlain, A. M., Lubitz, S. A., Schnabel, R. B., Agarwal, S. K., McManus, D. D., Ellinor, P. T., … Benjamin, E. J. (2013). Simple Risk Model Predicts Incidence of Atrial Fibrillation in a Racially and Geographically Diverse Population: The CHARGE‐AF Consortium. Journal of the American Heart Association, 2(2), e000102. https://doi.org/10.1161/JAHA.112.000102
Alpert, J. S. (2019). The Electronic Medical Record: Beauty and the Beast. The American Journal of Medicine, 132(4), 393–394. https://doi.org/10.1016/j.amjmed.2018.12.004
Apple, S. J., Flomenbaum, D., Parker, M., Chhikara, S., Stolarov, A., Moser, J., Mathai, S. V., Seo, J., Ferrick, N., Chudow, J. J., Di Biase, L., Krumerman, A., & Ferrick, K. J. (2023). Low Utility of Short-Term Rhythm Assessment Before Long-Term Rhythm Monitoring in Patients With Cryptogenic Stroke. The American Journal of Cardiology, 202, 151–159. https://doi.org/10.1016/j.amjcard.2023.06.040
Aronson, A. R. (2001). Effective mapping of biomedical text to the UMLS Metathesaurus: The MetaMap program. Proceedings. AMIA Symposium, 17–21.
Aronson, A. R., & Lang, F.-M. (2010). An overview of MetaMap: Historical perspective and recent advances. Journal of the American Medical Informatics Association, 17(3), 229–236. https://doi.org/10.1136/jamia.2009.002733
Bandrowski, A., Brinkman, R., Brochhausen, M., Brush, M. H., Bug, B., Chibucos, M. C., Clancy, K., Courtot, M., Derom, D., Dumontier, M., Fan, L., Fostel, J., Fragoso, G., Gibson, F., Gonzalez-Beltran, A., Haendel, M. A., He, Y., Heiskanen, M., Hernandez-Boussard, T., … Zheng, J. (2016). The Ontology for Biomedical Investigations. PLOS ONE, 11(4), e0154556. https://doi.org/10.1371/journal.pone.0154556
Behera, R. K., Jena, M., Rath, S. K., & Misra, S. (2021). Co-LSTM: Convolutional LSTM model for sentiment analysis in social big data. Information Processing & Management, 58(1), 102435. https://doi.org/10.1016/j.ipm.2020.102435
Benjamin, E. J., Levy, D., Vaziri, S. M., D’Agostino, R. B., Belanger, A. J., & Wolf, P. A. (1994). Independent risk factors for atrial fibrillation in a population-based cohort. The Framingham Heart Study. JAMA, 271(11), 840–844.
Bergström, L., Irewall, A.-L., Söderström, L., Ögren, J., Laurell, K., & Mooe, T. (2017). One-Year Incidence, Time Trends, and Predictors of Recurrent Ischemic Stroke in Sweden From 1998 to 2010: An Observational Study. Stroke, 48(8), 2046–2051. https://doi.org/10.1161/STROKEAHA.117.016815
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017). Classification And Regression Trees (1st ed.). Routledge. https://doi.org/10.1201/9781315139470
Bucci, T., Proietti, M., Shantsila, A., Romiti, G. F., Teo, W.-S., Park, H.-W., Shimizu, W., Tse, H.-F., Lip, G. Y. H., & Chao, T.-F. (2023). Integrated Care for Atrial Fibrillation Using the ABC Pathway in the Prospective APHRS-AF Registry. JACC: Asia, 3(4), 580–591. https://doi.org/10.1016/j.jacasi.2023.04.008
Campbell, B. C. V., De Silva, D. A., Macleod, M. R., Coutts, S. B., Schwamm, L. H., Davis, S. M., & Donnan, G. A. (2019). Ischaemic stroke. Nature Reviews Disease Primers, 5(1), 70. https://doi.org/10.1038/s41572-019-0118-8
Chalfoun, N., Pierobon, J., Rosemas, S. C., Fox, J., Albano, A., Banno, J., Brunner, M., Corner, K., Dahu, M., Dandamudi, S., Davis, A. T., Elmouchi, D., Jawad, W., Khan, M., Min, J., Rai, V., Rosema, S., Sagorski, R., & Gauri, A. (2022). A cost comparison of atrial fibrillation monitoring strategies after embolic stroke of undetermined source. American Heart Journal Plus: Cardiology Research and Practice, 21, 100195. https://doi.org/10.1016/j.ahjo.2022.100195
Chamberlain, A. M., Gersh, B. J., Alonso, A., Chen, L. Y., Berardi, C., Manemann, S. M., Killian, J. M., Weston, S. A., & Roger, V. L. (2015). Decade-long Trends in Atrial Fibrillation Incidence and Survival: A Community Study. The American Journal of Medicine, 128(3), 260-267.e1. https://doi.org/10.1016/j.amjmed.2014.10.030
Chamberlain, A. M., Roger, V. L., Noseworthy, P. A., Chen, L. Y., Weston, S. A., Jiang, R., & Alonso, A. (2022). Identification of Incident Atrial Fibrillation From Electronic Medical Records. Journal of the American Heart Association, 11(7), e023237. https://doi.org/10.1161/JAHA.121.023237
Chang, P.-C., Wen, M.-S., Chou, C.-C., Wang, C.-C., & Hung, K.-C. (2022). Atrial fibrillation detection using ambulatory smartwatch photoplethysmography and validation with simultaneous holter recording. American Heart Journal, 247, 55–62. https://doi.org/10.1016/j.ahj.2022.02.002
Chao, T.-F., Liu, C.-J., Tuan, T.-C., Chen, T.-J., Hsieh, M.-H., Lip, G. Y. H., & Chen, S.-A. (2018). Lifetime Risks, Projected Numbers, and Adverse Outcomes in Asian Patients With Atrial Fibrillation: A Report From the Taiwan Nationwide AF Cohort Study. Chest, 153(2), 453–466. https://doi.org/10.1016/j.chest.2017.10.001
Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. https://doi.org/10.1145/2939672.2939785
Chiang, C.-E., Wu, T.-J., Ueng, K.-C., Chao, T.-F., Chang, K.-C., Wang, C.-C., Lin, Y.-J., Yin, W.-H., Kuo, J.-Y., Lin, W.-S., Tsai, C.-T., Liu, Y.-B., Lee, K.-T., Lin, L.-J., Lin, L.-Y., Wang, K.-L., Chen, Y.-J., Chen, M.-C., Cheng, C.-C., … Chen, S.-A. (2016). 2016 Guidelines of the Taiwan Heart Rhythm Society and the Taiwan Society of Cardiology for the management of atrial fibrillation. Journal of the Formosan Medical Association, 115(11), 893–952. https://doi.org/10.1016/j.jfma.2016.10.005
Chiang, J.-H., Lin, J.-W., & Yang, C.-W. (2010). Automated evaluation of electronic discharge notes to assess quality of care for cardiovascular diseases using Medical Language Extraction and Encoding System (MedLEE). Journal of the American Medical Informatics Association, 17(3), 245–252. https://doi.org/10.1136/jamia.2009.000182
Chiu, C.-C., Wu, C.-M., Chien, T.-N., Kao, L.-J., Li, C., & Chu, C.-M. (2023). Integrating Structured and Unstructured EHR Data for Predicting Mortality by Machine Learning and Latent Dirichlet Allocation Method. International Journal of Environmental Research and Public Health, 20(5), 4340. https://doi.org/10.3390/ijerph20054340
Choi, Y., Kim, Y., Kim, S. E., & Lee, J.-H. (2023). Association of non-sustained atrial tachycardia and its duration in 24-h Holter monitoring with embolic stroke of unknown source. Journal of the Neurological Sciences, 447, 120610. https://doi.org/10.1016/j.jns.2023.120610
Connolly, S. J., Ezekowitz, M. D., Yusuf, S., Eikelboom, J., Oldgren, J., Parekh, A., Pogue, J., Reilly, P. A., Themeles, E., Varrone, J., Wang, S., Alings, M., Xavier, D., Zhu, J., Diaz, R., Lewis, B. S., Darius, H., Diener, H.-C., Joyner, C. D., & Wallentin, L. (2009). Dabigatran versus Warfarin in Patients with Atrial Fibrillation. New England Journal of Medicine, 361(12), 1139–1151. https://doi.org/10.1056/NEJMoa0905561
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. https://doi.org/10.1007/BF00994018
Cox, D. R. (1958). The Regression Analysis of Binary Sequences. Journal of the Royal Statistical Society: Series B (Methodological), 20(2), 215–232. https://doi.org/10.1111/j.2517-6161.1958.tb00292.x
Dehghan, A., Yang, Q., Peters, A., Basu, S., Bis, J. C., Rudnicka, A. R., Kavousi, M., Chen, M.-H., Baumert, J., Lowe, G. D. O., McKnight, B., Tang, W., De Maat, M., Larson, M. G., Eyhermendy, S., McArdle, W. L., Lumley, T., Pankow, J. S., Hofman, A., … Folsom, A. R. (2009). Association of Novel Genetic Loci With Circulating Fibrinogen Levels: A Genome-Wide Association Study in 6 Population-Based Cohorts. Circulation: Cardiovascular Genetics, 2(2), 125–133. https://doi.org/10.1161/CIRCGENETICS.108.825224
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. https://doi.org/10.48550/ARXIV.1810.04805
Dong, G., & Liu, H. (Eds.). (2018). Feature Engineering for Machine Learning and Data Analytics (First edition). Taylor and Francis.
Donnelly, K. (2006). SNOMED-CT: The advanced terminology and coding system for eHealth. Studies in Health Technology and Informatics, 121, 279–290.
Essa, H., Hill, A. M., & Lip, G. Y. H. (2021). Atrial Fibrillation and Stroke. Cardiac Electrophysiology Clinics, 13(1), 243–255. https://doi.org/10.1016/j.ccep.2020.11.003
European Stroke Organisation (ESO) Executive Committee, & ESO Writing Committee. (2008). Guidelines for Management of Ischaemic Stroke and Transient Ischaemic Attack 2008. Cerebrovascular Diseases, 25(5), 457–507. https://doi.org/10.1159/000131083
Evans, A., & Kalra, L. (2001). Are the Results of Randomized Controlled Trials on Anticoagulation in Patients With Atrial Fibrillation Generalizable to Clinical Practice? Archives of Internal Medicine, 161(11), 1443. https://doi.org/10.1001/archinte.161.11.1443
Feigin, V. L., Brainin, M., Norrving, B., Martins, S., Sacco, R. L., Hacke, W., Fisher, M., Pandian, J., & Lindsay, P. (2022). World Stroke Organization (WSO): Global Stroke Fact Sheet 2022. International Journal of Stroke, 17(1), 18–29. https://doi.org/10.1177/17474930211065917
Feigin, V. L., Forouzanfar, M. H., Krishnamurthi, R., Mensah, G. A., Connor, M., Bennett, D. A., Moran, A. E., Sacco, R. L., Anderson, L., Truelsen, T., O’Donnell, M., Venketasubramanian, N., Barker-Collo, S., Lawes, C. M. M., Wang, W., Shinohara, Y., Witt, E., Ezzati, M., Naghavi, M., & Murray, C. (2014). Global and regional burden of stroke during 1990–2010: Findings from the Global Burden of Disease Study 2010. The Lancet, 383(9913), 245–255. https://doi.org/10.1016/S0140-6736(13)61953-4
Feigin, V. L., Norrving, B., & Mensah, G. A. (2017). Global Burden of Stroke. Circulation Research, 120(3), 439–448. https://doi.org/10.1161/CIRCRESAHA.116.308413
Fix, E., & Hodges, J. L. (1989). Discriminatory Analysis. Nonparametric Discrimination: Consistency Properties. International Statistical Review / Revue Internationale de Statistique, 57(3), 238. https://doi.org/10.2307/1403797
Fodeh, S. J., Zirkle, M., Finch, D., Reeves, R., Erdos, J., & Brandt, C. (2013). MedCat: A Framework for High Level Conceptualization of Medical Notes. 2013 IEEE 13th International Conference on Data Mining Workshops, 274–280. https://doi.org/10.1109/ICDMW.2013.89
Foo, D. H. P., Fong, A. Y. Y., & Almahmeed, W. (2023). Detection of Atrial Fibrillation in Patients Admitted with Ischaemic Stroke: A Non-systematic Review of the Asian Population. Journal of Asian Pacific Society of Cardiology, 2, e04. https://doi.org/10.15420/japsc.2021.32
Frakes, W. B. (Ed.). (1992). Information retrieval: Data structures & algorithms (5. print.). Prentice-Hall.
Fried, L. P., Borhani, N. O., Enright, P., Furberg, C. D., Gardin, J. M., Kronmal, R. A., Kuller, L. H., Manolio, T. A., Mittelmark, M. B., & Newman, A. (1991). The Cardiovascular Health Study: Design and rationale. Annals of Epidemiology, 1(3), 263–276. https://doi.org/10.1016/1047-2797(91)90005-w
Friedlin, J., Overhage, M., Al-Haddad, M. A., Waters, J. A., Aguilar-Saavedra, J. J. R., Kesterson, J., & Schmidt, M. (2010). Comparing methods for identifying pancreatic cancer patients using electronic data sources. AMIA ... Annual Symposium Proceedings. AMIA Symposium, 2010, 237–241.
García, S., Luengo, J., & Herrera, F. (2015). Data preprocessing in data mining (Vol. 72). Springer International Publishing.
Gillum, R. F. (2013). From Papyrus to the Electronic Tablet: A Brief History of the Clinical Medical Record with Lessons for the Digital Age. The American Journal of Medicine, 126(10), 853–857. https://doi.org/10.1016/j.amjmed.2013.03.024
Gilmer, T. P., O’Connor, P. J., Sperl‐Hillen, J. M., Rush, W. A., Johnson, P. E., Amundson, G. H., Asche, S. E., & Ekstrom, H. L. (2012). Cost‐Effectiveness of an Electronic Medical Record Based Clinical Decision Support System. Health Services Research, 47(6), 2137–2158. https://doi.org/10.1111/j.1475-6773.2012.01427.x
Goldberg, Y., & Levy, O. (2014). word2vec Explained: Deriving Mikolov et al.’s negative-sampling word-embedding method. https://doi.org/10.48550/ARXIV.1402.3722
Gonzalez Zelaya, C. V. (2019). Towards Explaining the Effects of Data Preprocessing on Machine Learning. 2019 IEEE 35th International Conference on Data Engineering (ICDE), 2086–2090. https://doi.org/10.1109/ICDE.2019.00245
Goudis, C., Daios, S., Dimitriadis, F., & Liu, T. (2023). CHARGE-AF: A Useful Score For Atrial Fibrillation Prediction? Current Cardiology Reviews, 19(2), e010922208402. https://doi.org/10.2174/1573403X18666220901102557
Granger, C. B., Alexander, J. H., McMurray, J. J. V., Lopes, R. D., Hylek, E. M., Hanna, M., Al-Khalidi, H. R., Ansell, J., Atar, D., Avezum, A., Bahit, M. C., Diaz, R., Easton, J. D., Ezekowitz, J. A., Flaker, G., Garcia, D., Geraldes, M., Gersh, B. J., Golitsyn, S., … Wallentin, L. (2011). Apixaban versus Warfarin in Patients with Atrial Fibrillation. New England Journal of Medicine, 365(11), 981–992. https://doi.org/10.1056/NEJMoa1107039
Granger (Ed. ), Granger, S., & Lefer, M.-A. (2020). Translating and comparing languages: Corpus-based insights: Selected proceedings of the Fifth Using Corpora in Contrastive and Translation Studies Conference. Presses Universitaires.
Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. Q. (2017). On Calibration of Modern Neural Networks. https://doi.org/10.48550/ARXIV.1706.04599
Hajar, R. (2016). Framingham contribution to cardiovascular disease. Heart Views, 17(2), 78. https://doi.org/10.4103/1995-705X.185130
Hankey, G. J., Jamrozik, K., Broadhurst, R. J., Forbes, S., & Anderson, C. S. (2002). Long-Term Disability After First-Ever Stroke and Related Prognostic Factors in the Perth Community Stroke Study, 1989–1990. Stroke, 33(4), 1034–1040. https://doi.org/10.1161/01.STR.0000012515.66889.24
Harris, T. B., Launer, L. J., Eiriksdottir, G., Kjartansson, O., Jonsson, P. V., Sigurdsson, G., Thorgeirsson, G., Aspelund, T., Garcia, M. E., Cotch, M. F., Hoffman, H. J., & Gudnason, V. (2007). Age, Gene/Environment Susceptibility-Reykjavik Study: Multidisciplinary applied phenomics. American Journal of Epidemiology, 165(9), 1076–1087. https://doi.org/10.1093/aje/kwk115
Healey, J. S., & Wong, J. A. (2019). Pre-Screening for Atrial Fibrillation Using the Electronic Health Record. JACC: Clinical Electrophysiology, 5(11), 1342–1343. https://doi.org/10.1016/j.jacep.2019.08.019
Heeringa, J., van der Kuip, D. A. M., Hofman, A., Kors, J. A., van Herpen, G., Stricker, B. H. C., Stijnen, T., Lip, G. Y. H., & Witteman, J. C. M. (2006). Prevalence, incidence and lifetime risk of atrial fibrillation: The Rotterdam study. European Heart Journal, 27(8), 949–953. https://doi.org/10.1093/eurheartj/ehi825
Hennings, E., Coslovsky, M., Paladini, R. E., Aeschbacher, S., Knecht, S., Schlageter, V., Krisai, P., Badertscher, P., Sticherling, C., Osswald, S., Kühne, M., & Zuern, C. S. (2023). Assessment of the atrial fibrillation burden in Holter electrocardiogram recordings using artificial intelligence. Cardiovascular Digital Health Journal, 4(2), 41–47. https://doi.org/10.1016/j.cvdhj.2023.01.003
Hsieh, C.-Y., Kao, H.-M., Sung, K.-L., Sposato, L. A., Sung, S.-F., & Lin, S.-J. (2022). Validation of Risk Scores for Predicting Atrial Fibrillation Detected After Stroke Based on an Electronic Medical Record Algorithm: A Registry-Claims-Electronic Medical Record Linked Data Study. Frontiers in Cardiovascular Medicine, 9, 888240. https://doi.org/10.3389/fcvm.2022.888240
Hsieh, C.-Y., Lee, C.-H., & Sung, S.-F. (2020). Development of a novel score to predict newly diagnosed atrial fibrillation after ischemic stroke: The CHASE-LESS score. Atherosclerosis, 295, 1–7. https://doi.org/10.1016/j.atherosclerosis.2020.01.003
Hsieh, C.-Y., Wu, D. P., & Sung, S.-F. (2017). Trends in vascular risk factors, stroke performance measures, and outcomes in patients with first-ever ischemic stroke in Taiwan between 2000 and 2012. Journal of the Neurological Sciences, 378, 80–84. https://doi.org/10.1016/j.jns.2017.05.002
Hsieh, F.-I., & Chiou, H.-Y. (2014). Stroke: Morbidity, Risk Factors, and Care in Taiwan. Journal of Stroke, 16(2), 59. https://doi.org/10.5853/jos.2014.16.2.59
Hsieh, F.-I., Lien, L.-M., Chen, S.-T., Bai, C.-H., Sun, M.-C., Tseng, H.-P., Chen, Y.-W., Chen, C.-H., Jeng, J.-S., Tsai, S.-Y., Lin, H.-J., Liu, C.-H., Lo, Y.-K., Chen, H.-J., Chiu, H.-C., Lai, M.-L., Lin, R.-T., Sun, M.-H., Yip, B.-S., … the Taiwan Stroke Registry Investigators. (2010). Get With The Guidelines-Stroke Performance Indicators: Surveillance of Stroke Care in the Taiwan Stroke Registry: Get With The Guidelines-Stroke in Taiwan. Circulation, 122(11), 1116–1123. https://doi.org/10.1161/CIRCULATIONAHA.110.936526
Huang, C.-K., Wang, J.-C., Chung, C.-H., Chen, S.-J., Liao, W.-I., & Chien, W.-C. (2022). The risk and timing of acute ischemic stroke after electrical cardioversion for atrial fibrillation in Taiwan: A nationwide population-based cohort study. International Journal of Cardiology, 351, 55–60. https://doi.org/10.1016/j.ijcard.2021.12.035
Hulme, O. L., Khurshid, S., Weng, L.-C., Anderson, C. D., Wang, E. Y., Ashburner, J. M., Ko, D., McManus, D. D., Benjamin, E. J., Ellinor, P. T., Trinquart, L., & Lubitz, S. A. (2019). Development and Validation of a Prediction Model for Atrial Fibrillation Using Electronic Health Records. JACC: Clinical Electrophysiology, 5(11), 1331–1341. https://doi.org/10.1016/j.jacep.2019.07.016
Incitti, F., Urli, F., & Snidaro, L. (2023). Beyond word embeddings: A survey. Information Fusion, 89, 418–436. https://doi.org/10.1016/j.inffus.2022.08.024
Ishaq, A., Sadiq, S., Umer, M., Ullah, S., Mirjalili, S., Rupapara, V., & Nappi, M. (2021). Improving the Prediction of Heart Failure Patients’ Survival Using SMOTE and Effective Data Mining Techniques. IEEE Access, 9, 39707–39716. https://doi.org/10.1109/ACCESS.2021.3064084
Jahan, M. S., Mansourvar, M., Puthusserypady, S., Wiil, U. K., & Peimankar, A. (2022). Short-term atrial fibrillation detection using electrocardiograms: A comparison of machine learning approaches. International Journal of Medical Informatics, 163, 104790. https://doi.org/10.1016/j.ijmedinf.2022.104790
Jones, N. R., Taylor, C. J., Hobbs, F. D. R., Bowman, L., & Casadei, B. (2020). Screening for atrial fibrillation: A call for evidence. European Heart Journal, 41(10), 1075–1085. https://doi.org/10.1093/eurheartj/ehz834
Kaab, S., Darbar, D., Van Noord, C., Dupuis, J., Pfeufer, A., Newton-Cheh, C., Schnabel, R., Makino, S., Sinner, M. F., Kannankeril, P. J., Beckmann, B. M., Choudry, S., Donahue, B. S., Heeringa, J., Perz, S., Lunetta, K. L., Larson, M. G., Levy, D., MacRae, C. A., … Ellinor, P. T. (2008). Large scale replication and meta-analysis of variants on chromosome 4q25 associated with atrial fibrillation. European Heart Journal, 30(7), 813–819. https://doi.org/10.1093/eurheartj/ehn578
Karnik, S., Sin Lam Tan, Berg, B., Glurich, I., Jinfeng Zhang, Vidaillet, H. J., Page, C. D., & Chowdhary, R. (2012). Predicting atrial fibrillation and flutter using Electronic Health Records. 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 5562–5565. https://doi.org/10.1109/EMBC.2012.6347254
Kelly, A., & Johnson, M. A. (2021). Investigating the Statistical Assumptions of Naïve Bayes Classifiers. 2021 55th Annual Conference on Information Sciences and Systems (CISS), 1–6. https://doi.org/10.1109/CISS50987.2021.9400215
Kraljevic, Z., Bean, D., Mascio, A., Roguski, L., Folarin, A., Roberts, A., Bendayan, R., & Dobson, R. (2019). MedCAT -- Medical Concept Annotation Tool (arXiv:1912.10166). arXiv. http://arxiv.org/abs/1912.10166
Kraljevic, Z., Searle, T., Shek, A., Roguski, L., Noor, K., Bean, D., Mascio, A., Zhu, L., Folarin, A. A., Roberts, A., Bendayan, R., Richardson, M. P., Stewart, R., Shah, A. D., Wong, W. K., Ibrahim, Z., Teo, J. T., & Dobson, R. J. (2021). Multi-domain Clinical Natural Language Processing with MedCAT: The Medical Concept Annotation Toolkit (arXiv:2010.01165). arXiv. http://arxiv.org/abs/2010.01165
Kreimeyer, K., Foster, M., Pandey, A., Arya, N., Halford, G., Jones, S. F., Forshee, R., Walderhaug, M., & Botsis, T. (2017). Natural language processing systems for capturing and standardizing unstructured clinical information: A systematic review. Journal of Biomedical Informatics, 73, 14–29. https://doi.org/10.1016/j.jbi.2017.07.012
Lainay, C., Benzenine, E., Durier, J., Daubail, B., Giroud, M., Quantin, C., & Béjot, Y. (2015). Hospitalization Within the First Year After Stroke: The Dijon Stroke Registry. Stroke, 46(1), 190–196. https://doi.org/10.1161/STROKEAHA.114.007429
Lane, D. A., & Lip, G. Y. (2010). Dabigatran in atrial fibrillation: Balancing secondary stroke prevention against bleeding risk. The Lancet Neurology, 9(12), 1140–1142. https://doi.org/10.1016/S1474-4422(10)70275-1
Lane, D. A., & Lip, G. Y. H. (2012). Use of the CHA 2 DS 2 -VASc and HAS-BLED Scores to Aid Decision Making for Thromboprophylaxis in Nonvalvular Atrial Fibrillation. Circulation, 126(7), 860–865. https://doi.org/10.1161/CIRCULATIONAHA.111.060061
Larsen, T. B., Rasmussen, L. H., Gorst-Rasmussen, A., Skjøth, F., Lane, D. A., & Lip, G. Y. H. (2014). Dabigatran and Warfarin for Secondary Prevention of Stroke in Atrial Fibrillation Patients: A Nationwide Cohort Study. The American Journal of Medicine, 127(12), 1172-1178.e5. https://doi.org/10.1016/j.amjmed.2014.07.023
Le, Q. V., & Mikolov, T. (2014). Distributed Representations of Sentences and Documents. https://doi.org/10.48550/ARXIV.1405.4053
Lee, M., Wu, Y.-L., & Ovbiagele, B. (2016). Trends in Incident and Recurrent Rates of First-Ever Ischemic Stroke in Taiwan between 2000 and 2011. Journal of Stroke, 18(1), 60–65. https://doi.org/10.5853/jos.2015.01326
Li, L., Chase, H. S., Patel, C. O., Friedman, C., & Weng, C. (2008). Comparing ICD9-encoded diagnoses and NLP-processed discharge summaries for clinical trials pre-screening: A case study. AMIA ... Annual Symposium Proceedings. AMIA Symposium, 2008, 404–408.
Li, Y.-C. (Jack), Yen, J.-C., Chiu, W.-T., Jian, W.-S., Syed-Abdul, S., & Hsu, M.-H. (2015). Building a National Electronic Medical Record Exchange System – Experiences in Taiwan. Computer Methods and Programs in Biomedicine, 121(1), 14–20. https://doi.org/10.1016/j.cmpb.2015.04.013
Li, Y.-G., Pastori, D., Farcomeni, A., Yang, P.-S., Jang, E., Joung, B., Wang, Y.-T., Guo, Y.-T., & Lip, G. Y. H. (2019). A Simple Clinical Risk Score (C2HEST) for Predicting Incident Atrial Fibrillation in Asian Subjects: Derivation in 471,446 Chinese Subjects, With Internal Validation and External Application in 451,199 Korean Subjects. Chest, 155(3), 510–518. https://doi.org/10.1016/j.chest.2018.09.011
Lip, G. Y. H., Hunter, T. D., Quiroz, M. E., Ziegler, P. D., & Turakhia, M. P. (2017). Atrial Fibrillation Diagnosis Timing, Ambulatory ECG Monitoring Utilization, and Risk of Recurrent Stroke. Circulation: Cardiovascular Quality and Outcomes, 10(1), e002864. https://doi.org/10.1161/CIRCOUTCOMES.116.002864
Lip, G. Y. H., Nieuwlaat, R., Pisters, R., Lane, D. A., & Crijns, H. J. G. M. (2010). Refining clinical risk stratification for predicting stroke and thromboembolism in atrial fibrillation using a novel risk factor-based approach: The euro heart survey on atrial fibrillation. Chest, 137(2), 263–272. https://doi.org/10.1378/chest.09-1584
Lip, G. Y., & Tse, H.-F. (2007). Management of atrial fibrillation. The Lancet, 370(9587), 604–618. https://doi.org/10.1016/S0140-6736(07)61300-2
Lobato Casado, P., Jamilena López, Á., Segundo Rodríguez, J. C., Pachón Iglesias, M. I., Morín Martín, M. D. M., & Arias Palomares, M. Á. (2023). Use of the insertable Holter with remote detection in the etiological diagnosis of cryptogenic stroke: Analysis of 73 patients. Medicina Clínica (English Edition), S2387020623002486. https://doi.org/10.1016/j.medcle.2023.03.010
Luxburg, U. von, Guyon, I., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S. V. N., Garnett, R., & Neural Information Processing Systems Foundation (Eds.). (2018). Advances in neural information processing systems 30: 31st Annual Conference on Neural Information Processing Systems (NIPS 2017): Long Beach, California, USA, 4-9 December 2017. NIPS, Red Hook, NY. Curran Associates, Inc.
Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to information retrieval. Cambridge University Press.
Marco Moia & Pier Mannuccio Mannucci. (2009). Dabigatran versus warfarin in patients with atrial fibrillation. 361(27), 2672. https://www.researchgate.net/profile/Jorgen-Jespersen-2/publication/40849245_Dabigatran_versus_Warfarin_in_Patients_with_Atrial_Fibrillation/links/57beb22108ae2f5eb32e1b6d/Dabigatran-versus-Warfarin-in-Patients-with-Atrial-Fibrillation.pdf
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. https://doi.org/10.48550/ARXIV.1301.3781
Mosteiro, P., Rijcken, E., Zervanou, K., Kaymak, U., Scheepers, F., & Spruit, M. (2020). Making Sense of Violence Risk Predictions Using Clinical Notes. In Z. Huang, S. Siuly, H. Wang, R. Zhou, & Y. Zhang (Eds.), Health Information Science (Vol. 12435, pp. 3–14). Springer International Publishing. https://doi.org/10.1007/978-3-030-61951-0_1
Mtwesi, V., & Amit, G. (2019). Stroke Prevention in Atrial Fibrillation. Medical Clinics of North America, 103(5), 847–862. https://doi.org/10.1016/j.mcna.2019.05.006
Mujtaba, G., Shuib, L., Idris, N., Hoo, W. L., Raj, R. G., Khowaja, K., Shaikh, K., & Nweke, H. F. (2019). Clinical text classification research trends: Systematic literature review and open issues. Expert Systems with Applications, 116, 494–520. https://doi.org/10.1016/j.eswa.2018.09.034
Naeini, M. P., Cooper, G. F., & Hauskrecht, M. (2015). Obtaining Well Calibrated Probabilities Using Bayesian Binning. Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence, 2015, 2901–2907.
Patel, M. R., Mahaffey, K. W., Garg, J., Pan, G., Singer, D. E., Hacke, W., Breithardt, G., Halperin, J. L., Hankey, G. J., Piccini, J. P., Becker, R. C., Nessel, C. C., Paolini, J. F., Berkowitz, S. D., Fox, K. A. A., Califf, R. M., & the ROCKET AF Steering Committee. (2011). Rivaroxaban versus Warfarin in Nonvalvular Atrial Fibrillation. New England Journal of Medicine, 365(10), 883–891. https://doi.org/10.1056/NEJMoa1009638
Pennlert, J., Eriksson, M., Carlberg, B., & Wiklund, P.-G. (2014). Long-Term Risk and Predictors of Recurrent Stroke Beyond the Acute Phase. Stroke, 45(6), 1839–1841. https://doi.org/10.1161/STROKEAHA.114.005060
Pisters, R., Lane, D. A., Nieuwlaat, R., De Vos, C. B., Crijns, H. J. G. M., & Lip, G. Y. H. (2010). A Novel User-Friendly Score (HAS-BLED) To Assess 1-Year Risk of Major Bleeding in Patients With Atrial Fibrillation. Chest, 138(5), 1093–1100. https://doi.org/10.1378/chest.10-0134
Proietti, M., Lane, D. A., Boriani, G., & Lip, G. Y. H. (2019). Stroke Prevention, Evaluation of Bleeding Risk, and Anticoagulant Treatment Management in Atrial Fibrillation Contemporary International Guidelines. Canadian Journal of Cardiology, 35(5), 619–633. https://doi.org/10.1016/j.cjca.2019.02.009
Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2017). CatBoost: Unbiased boosting with categorical features (Version 5). arXiv. https://doi.org/10.48550/ARXIV.1706.09516
Psaty, B. M., O’Donnell, C. J., Gudnason, V., Lunetta, K. L., Folsom, A. R., Rotter, J. I., Uitterlinden, A. G., Harris, T. B., Witteman, J. C. M., Boerwinkle, E., & CHARGE Consortium. (2009). Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium: Design of prospective meta-analyses of genome-wide association studies from 5 cohorts. Circulation. Cardiovascular Genetics, 2(1), 73–80. https://doi.org/10.1161/CIRCGENETICS.108.829747
Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), 81–106. https://doi.org/10.1007/BF00116251
Resnick, M. P., LeHouillier, F., Brown, S. H., Campbell, K. E., Montella, D., & Elkin, P. L. (2021). Automated Modeling of Clinical Narrative with High Definition Natural Language Processing Using Solor and Analysis Normal Form. In J. Delgado, A. Benis, P. De Toledo, P. Gallos, M. Giacomini, A. Martínez-García, & D. Salvi (Eds.), Studies in Health Technology and Informatics. IOS Press. https://doi.org/10.3233/SHTI210822
Rosenberg, D. (2014). Stop, Words. Representations, 127(1), 83–92. https://doi.org/10.1525/rep.2014.127.1.83
Salton, G., Wong, A., & Yang, C. S. (1975). A vector space model for automatic indexing. Communications of the ACM, 18(11), 613–620. https://doi.org/10.1145/361219.361220
Salzberg, S. L. (1994). C4.5: Programs for Machine Learning by J. Ross Quinlan. Morgan Kaufmann Publishers, Inc., 1993. Machine Learning, 16(3), 235–240. https://doi.org/10.1007/BF00993309
Singer, D. E., Atlas, S. J., Go, A. S., Lopes, R. D., Lubitz, S. A., McManus, D. D., Revkin, J. H., Mills, D., Crosson, L. A., Lenane, J. C., & Aronson, R. S. (2022). ReducinG stroke by screening for UndiAgnosed atRial fibrillation in elderly inDividuals (GUARD-AF): Rationale and design of the GUARD-AF randomized trial of screening for atrial fibrillation with a 14-day patch-based continuous ECG monitor. American Heart Journal, 249, 76–85. https://doi.org/10.1016/j.ahj.2022.04.005
Själander, S., Sjögren, V., Renlund, H., Norrving, B., & Själander, A. (2018). Dabigatran, rivaroxaban and apixaban vs. High TTR warfarin in atrial fibrillation. Thrombosis Research, 167, 113–118. https://doi.org/10.1016/j.thromres.2018.05.022
Sposato, L. A., Cerasuolo, J. O., Cipriano, L. E., Fang, J., Fridman, S., Paquet, M., Saposnik, G., & On behalf of the PARADISE Study Group. (2018). Atrial fibrillation detected after stroke is related to a low risk of ischemic stroke recurrence. Neurology, 90(11), e924–e931. https://doi.org/10.1212/WNL.0000000000005126
Sposato, L. A., Chaturvedi, S., Hsieh, C.-Y., Morillo, C. A., & Kamel, H. (2022). Atrial Fibrillation Detected After Stroke and Transient Ischemic Attack: A Novel Clinical Concept Challenging Current Views. Stroke, 53(3). https://doi.org/10.1161/STROKEAHA.121.034777
Sposato, L. A., Cipriano, L. E., Saposnik, G., Vargas, E. R., Riccio, P. M., & Hachinski, V. (2015). Diagnosis of atrial fibrillation after stroke and transient ischaemic attack: A systematic review and meta-analysis. The Lancet Neurology, 14(4), 377–387. https://doi.org/10.1016/S1474-4422(15)70027-X
Sun, W., Cai, Z., Li, Y., Liu, F., Fang, S., & Wang, G. (2018). Data Processing and Text Mining Technologies on Electronic Medical Records: A Review. Journal of Healthcare Engineering, 2018, 1–9. https://doi.org/10.1155/2018/4302425
Sung, S.-F., Chen, Y.-W., Tseng, M.-C., Ong, C.-T., & Lin, H.-J. (2013). Atrial fibrillation predicts good functional outcome following intravenous tissue plasminogen activator in patients with severe stroke. Clinical Neurology and Neurosurgery, 115(7), 892–895. https://doi.org/10.1016/j.clineuro.2012.08.034
Sung, S.-F., Lin, C.-Y., & Hu, Y.-H. (2020). EMR-Based Phenotyping of Ischemic Stroke Using Supervised Machine Learning and Text Mining Techniques. IEEE Journal of Biomedical and Health Informatics, 24(10), 2922–2931. https://doi.org/10.1109/JBHI.2020.2976931
Tangri, N. (2011). A Predictive Model for Progression of Chronic Kidney Disease to Kidney Failure. JAMA, 305(15), 1553. https://doi.org/10.1001/jama.2011.451
Taylor, W. L. (1953). “Cloze Procedure”: A New Tool for Measuring Readability. Journalism Quarterly, 30(4), 415–433. https://doi.org/10.1177/107769905303000401
Thayabaranathan, T., Kim, J., Cadilhac, D. A., Thrift, A. G., Donnan, G. A., Howard, G., Howard, V. J., Rothwell, P. M., Feigin, V., Norrving, B., Owolabi, M., Pandian, J., Liu, L., & Olaiya, M. T. (2022). Global stroke statistics 2022. International Journal of Stroke, 17(9), 946–956. https://doi.org/10.1177/17474930221123175
Uphaus, T., Weber-Krüger, M., Grond, M., Toenges, G., Jahn-Eimermacher, A., Jauss, M., Kirchhof, P., Wachter, R., & Gröschel, K. (2019). Development and validation of a score to detect paroxysmal atrial fibrillation after stroke. Neurology, 92(2), e115–e124. https://doi.org/10.1212/WNL.0000000000006727
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention Is All You Need. https://doi.org/10.48550/ARXIV.1706.03762
Vrieze, S. I., Docherty, A., Thuras, P., Arbisi, P., Iacono, W. G., Sponheim, S., Erbes, C. R., Siegel, W., & Leskela, J. (2013). Best Practices: The Electronic Medical Record Is an Invaluable Clinical Tool: Let’s Start Using It. Psychiatric Services, 64(10), 946–949. https://doi.org/10.1176/appi.ps.201300272
Wang, B., Wang, A., Chen, F., Wang, Y., & Kuo, C.-C. J. (2019). Evaluating word embedding models: Methods and experimental results. APSIPA Transactions on Signal and Information Processing, 8(1). https://doi.org/10.1017/ATSIP.2019.12
Wei, Y., Wang, L., Lin, C., Xie, Y., Bao, Y., Luo, Q., & Zhang, N. (2021). Association between the rs2106261 polymorphism in the zinc finger homeobox 3 gene and risk of atrial fibrillation: Evidence from a PRISMA-compliant meta-analysis. Medicine, 100(49), e27749. https://doi.org/10.1097/MD.0000000000027749
Yang, L., Brooks, M. M., Glynn, N. W., Zhang, Y., Saba, S., & Hernandez, I. (2020). Real-World Direct Comparison of the Effectiveness and Safety of Apixaban, Dabigatran, Rivaroxaban, and Warfarin in Medicare Beneficiaries With Atrial Fibrillation. The American Journal of Cardiology, 126, 29–36. https://doi.org/10.1016/j.amjcard.2020.03.034
Yang, X.-M., Rao, Z.-Z., Gu, H.-Q., Zhao, X.-Q., Wang, C.-J., Liu, L.-P., Liu, C., Wang, Y.-L., Li, Z.-X., Xiao, R.-P., Wang, Y.-J., & on behalf of the China National Stroke Registry II Investigators. (2019). Atrial Fibrillation Known Before or Detected After Stroke Share Similar Risk of Ischemic Stroke Recurrence and Death. Stroke, 50(5), 1124–1129. https://doi.org/10.1161/STROKEAHA.118.024176
Yu, X., Hu, W., Lu, S., Sun, X., & Yuan, Z. (2019). BioBERT Based Named Entity Recognition in Electronic Medical Record. 2019 10th International Conference on Information Technology in Medicine and Education (ITME), 49–52. https://doi.org/10.1109/ITME.2019.00022
指導教授 胡雅涵(Ya-Han Hu) 審核日期 2024-7-19
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