請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/74088
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 賴飛羆(Fei-Pei Lai) | |
dc.contributor.author | Yi-Chun Wu | en |
dc.contributor.author | 吳翊群 | zh_TW |
dc.date.accessioned | 2021-06-17T08:19:28Z | - |
dc.date.available | 2024-08-18 | |
dc.date.copyright | 2019-08-18 | |
dc.date.issued | 2019 | |
dc.date.submitted | 2019-08-14 | |
dc.identifier.citation | [1] Harris I., Murray S.A. Can palliative care reduce futile treatment? A systematic review.
[2] Temel JS, Greer JA, Muzikansky A, et al. Early palliative care for patients with metastatic non-small-cell lung cancer. [3] Garrido MM, Balboni TA, Maciejewski PK, Bao Y, Prigerson HG. Quality of Life and Cost of Care at the End of Life: The Role of Advance Directives. [4] Maetens A, Beernaert K, De Schreye R, et al Impact of palliative home care support on the quality and costs of care at the end of life: a population-level matched cohort study. [5] 溫純芳, 林文德(2014) 癌末死亡病人簽署不施行心肺復甦時點與醫療利用之相關性研究 [6] P.K. Fu, Y.C. Tung, C.Y. Wang, S.F. Hwang, S.P. Lin, C.Y. Hsu, et al. Early and late do-not-resuscitate (DNR) decisions in patients with terminal COPD: a retrospective study in the last year of life. [7] 王曉婷 徐明儀(2017) 血液腫瘤住院病人血流感染危險因子之相關性探討 [8] Button E, Chan RJ, Chambers S, Butler J, Yates P. A systematic review of prognostic factors at the end of life for people with a hematological malignancy. [9] Glare PA, Sinclair CT: Palliative medicine review: Prognostication. [10] LeBlanc TW, El-Jawahri A. When and why should patients with hematologic malignancies see a palliative care specialist? [11] Ohno, E., Abe, M., Sasaki, H., & Okuhiro, K. (2017). Validation of 2 Prognostic Models in Hospitalized Patients With Advanced Hematological Malignancies in Japan. [12] Auret, K, Bulsara, C, Joske, D. Australasian haematologist referral patterns to palliative care: lack of consensus on when and why. [13] Baek SK, Chang HJ, Byun JM, Han JJ, Heo DS. The association between end-of-life care and the time interval between provision of a do-not-resuscitate consent and death in cancer patients in Korea. [14] A. Awad, M. Bader-El-Den, J. McNicholas, J. Briggs, Early hospital mortality prediction of intensive care unit patients using an ensemble learning approach. [15] Choi E, Bahadori MT, Schuetz A, Stewart WF, Sun J. 2015Doctor AI: predicting clinical events via recurrent neural networks. [16] Pham T, Tran T, Phung D, Venkatesh S. 2016DeepCare: a deep dynamic memory model for predictive medicine. [17] Razavian N, Marcus J, Sontag D. 2016 Multi-task prediction of disease onsets from longitudinal lab tests. [18] Peters SG, Buntrock JD. Big data and the electronic health record. [19] Miotto, R., Wang, F., Wang, S., Jiang, X., and Dudley, J. T. (2017). Deep learning for healthcare: review, opportunities and challenges. [20] Kinzbrunner BM, Weinreb NJ, Policzer JS, End of life Care. [21] Avati, A., Jung, K., Harman, S., Downing, L., Ng, A., and Shah, N. H. (2017). Improving palliative care with deep learning. [22] González G, Ash SY, Vegas-Sánchez-Ferrero G, et al. Disease Staging and Prognosis in Smokers Using Deep Learning in Chest Computed Tomography. [23] White N, Reid F, Harris A, Harries P, Stone P. A Systematic Review of Predictions of Survival in Palliative Care: How Accurate Are Clinicians and Who Are the Experts?. [24] Cheng Q, Tang Y, Yang Q, Wang E, Liu J, Li X. The prognostic factors for patients with hematological malignancies admitted to the intensive care unit. [25] Sathyabama K., Saruladha K., Hemalatha M. (2019) Design of LSTM-Based RNN for Prognosis Prediction of High-Risk Diseases from Patient Diagnostic Histories. [26] Yao Zhu and Xiaoliang Fan and Jinzhun Wu and Xiao Liu and Jia Shi and Cheng Wang, Predicting ICU mortality by supervised bidirectional LSTM networks. [27] C. Esteban, O. Staeck, Y. Yang, V. Tresp, 'Predicting clinical events by combining static and dynamic information using recurrent neural networks' [28] Morita T, Tsunoda J, Inoue S, Chihara S (1999). The Palliative Prognostic Index: a scoring system for survival prediction of terminally ill cancer patients. [29] D. A. Karnofsky, W. H. Abelmann, L. F. Craver, and J. H. Burchenal, “The use of the nitrogen mustards in the palliative treatment of carcinoma. With particular reference to bronchogenic carcinoma,” [30] F. Lau, G. M. Downing, M. Lesperance, J. Shaw, and C. Kuziemsky, “Use of Palliative Performance Scale in End-of-Life Prognostication,” [31] Niscola P, Tendas A, Scaramucci L, et al. Pain in malignant hematology. [32] McCarberg BH, Nicholson BD, Todd KH, Palmer T, Penles L. The impact of pain on quality of life and the unmet needs of pain management: results from pain sufferers and physicians participating in an Internet survey. [33] Jurlander J. (2011) Hematological Malignancies, Leukemias and Lymphomas. [34] Niscola P. Effective pain management in hematological malignancies. [35] Kamel PI, Nagy PG. Patient-Centered Radiology with FHIR: an Introduction to the Use of FHIR to Offer Radiology a Clinically Integrated Platform. [36] Hui D, Nooruddin Z, Didwaniya N, et al. Concepts and definitions for 'actively dying,' 'end of life,' 'terminally ill,' 'terminal care,' and 'transition of care': a systematic review. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/74088 | - |
dc.description.abstract | 近年來,台灣致力於發展安寧緩和醫療並得到相當的成效。然而在臨床
上我們發現血液腫瘤病患簽署DNR 的時間過晚,超過 50%的病人在死亡前一週以內才簽署DNR ,其主要原因是目前臨床上沒有準確的血液腫瘤預後資訊。然而,過晚簽署DNR 可能導致受到無效醫療的機會增加,醫療花費也會跟著增加,且血液腫瘤病情起伏較大,導致臨床上血液腫瘤病患常面臨到轉介安寧的時間已經過晚的問題。如果能夠有一個準確的臨終預測系統,能夠在病人接近臨終階段時發出警示,及早轉介安寧,便可改善以上這些狀況。 現今,越來越多醫學研究運用深度學習結合電子病歷,且得到相當好的結果。本研究提出一個基於長短期記憶網路 (LSTM) 模型,將血液腫瘤病患之基本資料、生命徵象、檢驗值、身體評估、嗎啡類藥物紀錄,組合在一起輸入模型,輸出為一個風險值,若風險值高於某門檻值,則發出警訊。我們的模型經過評估能有不錯的表現。此外,我們也支援 FHIR 輸入的服務,讓模型更容易被應用及具彈性。 | zh_TW |
dc.description.abstract | In recent years, Taiwan has been committed to the development of palliative care and has achieved considerable results. However, in clinical practice, we found that patients with hematological malignancies signed DNR late. More than 50% of patients signed DNR within one week before death. The main reason is that there is no accurate prognosis of hematological malignancies in the clinic. Signing DNR late may lead to increased chances of receiving medical futility and also increased medical costs. What’s more, the illness trajectory of hematological malignancies is fluctuating, leading to late referral to palliative care. If we have an accurate end-of-life prediction system that can alert us when the patient is nearing the end of life, and refer to palliative care as early as possible, these conditions can be improved.
Today, more and more medical research uses deep learning combined with electronic medical records, and has achieved fairly good results. In our research, we propose a long short term memory network (LSTM) model that input the combination of patients’ demographics, vital signs, laboratory tests, physical examination, illness categories, and opioids analgesics usage of patients with hematological malignancies and output a risk score. If the risk score is higher than the threshold, a warning will be sent. After our evaluations, the model can perform well. Moreover, our model support FHIR input services to make the model more flexible and applicable. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T08:19:28Z (GMT). No. of bitstreams: 1 ntu-108-R05922121-1.pdf: 2577340 bytes, checksum: 07d88dfca0f18c0c8424dc1961113576 (MD5) Previous issue date: 2019 | en |
dc.description.tableofcontents | 口委員審定書 i
誌謝 ii 中文摘要 iii ABSTRACT iv CONTENTS vi LIST OF FIGURES ix LIST OF TABLES x Chapter 1 Introduction 1 1.1 Background 1 1.2 Motivation 3 1.3 Objective 4 Chapter 2 Related Work 6 2.1 Prognostic Tool 6 2.2 Deep Learning 7 Chapter 3 Data Description 9 3.1 Data Introduction 9 3.2 Data Description 10 3.2.1 Patient Demographics 10 3.2.2 Vital Sign 11 3.2.3 Laboratory Test 12 3.2.4 Physical Examination (PE) 14 3.2.5 Illness Category 16 3.2.6 Opioids analgesics 17 3.3 Fast Healthcare Interoperability Resources (FHIR) 19 Chapter 4 Methodology 20 4.1 Workflow 20 4.2 Data Preprocessing 21 4.2.1 Patient Demographics 22 4.2.2 Vital Signs 22 4.2.3 Laboratory Test 22 4.2.4 Physical Examination (PE) 24 4.2.5 Illness Category 24 4.2.6 Opioids Analgesics Usage 24 4.2.7 Merging data 24 4.2.8 Define Labels 25 4.3 Building Model 25 4.4 Evaluation Metric 28 Chapter 5 Results and discussion 29 5.1 0/1 label and our proposed label 30 5.2 Performance of Warning 31 Chapter 6 Conclusion 33 REFERENCE 34 | |
dc.language.iso | zh-TW | |
dc.title | 應用深度學習於血液腫瘤病患臨終警示系統 | zh_TW |
dc.title | End-of-Life Warning System on Hematological Malignancy Patients Using Deep Learning | en |
dc.type | Thesis | |
dc.date.schoolyear | 107-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 胡文郁(Wen-yu Hu),莊仁輝(Jen-Hui Chuang),汪大暉(Ta-Hui Wang),成佳憲(Chia-Hsien Cheng) | |
dc.subject.keyword | 血液腫瘤,長短期記憶網路,安寧緩和照護,臨終預測,深度學習, | zh_TW |
dc.subject.keyword | Hematological malignancies,End-of-life prediction,Palliative care,Deep learning,LSTM, | en |
dc.relation.page | 39 | |
dc.identifier.doi | 10.6342/NTU201903295 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2019-08-14 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
顯示於系所單位: | 資訊工程學系 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-108-1.pdf 目前未授權公開取用 | 2.52 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。