請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80446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 | Shih-Hung Liu | en |
| dc.contributor.author | 劉士宏 | zh_TW |
| dc.date.accessioned | 2022-11-24T03:06:50Z | - |
| dc.date.available | 2022-01-17 | |
| dc.date.available | 2022-11-24T03:06:50Z | - |
| dc.date.copyright | 2022-01-17 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-12-09 | |
| dc.identifier.citation | [1] G. M. H. Swaen, L. G. P. M. van Amelsvoort, U. Bültmann, and I. J. Kant, “Fatigue as a risk factor for being injured in an occupational accident: results from the Maastricht Cohort Study,” Occupational and Environmental Medicine, vol. 60, no. suppl 1, pp. i88–i92, Jun. 2003, doi: 10.1136/oem.60.suppl_1.i88. [2] L. K. Barger et al., “Extended work shifts and the risk of motor vehicle crashes among interns,” N Engl J Med, vol. 352, no. 2, pp. 125–134, Jan. 2005, doi: 10.1056/NEJMoa041401. [3] N. T. Ayas et al., “Extended Work Duration and the Risk of Self-reported Percutaneous Injuries in Interns,” JAMA, vol. 296, no. 9, pp. 1055–1062, Sep. 2006, doi: 10.1001/jama.296.9.1055. [4] L. K. Barger et al., “Impact of Extended-Duration Shifts on Medical Errors, Adverse Events, and Attentional Failures,” PLOS Medicine, vol. 3, no. 12, p. e487, Dec. 2006, doi: 10.1371/journal.pmed.0030487. [5] C. P. West, A. D. Tan, T. M. Habermann, J. A. Sloan, and T. D. Shanafelt, “Association of Resident Fatigue and Distress With Perceived Medical Errors,” JAMA, vol. 302, no. 12, pp. 1294–1300, Sep. 2009, doi: 10.1001/jama.2009.1389. [6] S. M. Keller, P. Berryman, and E. Lukes, “Effects of Extended Work Shifts and Shift Work on Patient Safety, Productivity, and Employee Health,” AAOHN Journal, vol. 57, no. 12, pp. 497–504, Dec. 2009, doi: 10.1177/216507990905701204. [7] T. Åkerstedt and K. P. Wright, “Sleep Loss and Fatigue in Shift Work and Shift Work Disorder,” Sleep Medicine Clinics, vol. 4, no. 2, pp. 257–271, Jun. 2009, doi: 10.1016/j.jsmc.2009.03.001. [8] M. Kivimäki et al., “Long working hours and risk of coronary heart disease and stroke: a systematic review and meta-analysis of published and unpublished data for 603 838 individuals,” The Lancet, vol. 386, no. 10005, pp. 1739–1746, Oct. 2015, doi: 10.1016/S0140-6736(15)60295-1. [9] L.-J. Wang, C.-K. Chen, S.-C. Hsu, S.-Y. Lee, C.-S. Wang, and W.-Y. Yeh, “Active Job, Healthy Job? Occupational Stress and Depression among Hospital Physicians in Taiwan,” Ind Health, vol. 49, no. 2, pp. 173–184, 2011, doi: 10.2486/indhealth.MS1209. [10] T.-C. Lu, C.-M. Fu, M. H.-M. Ma, C.-C. Fang, and A. M. Turner, “Healthcare Applications of Smart Watches,” Appl Clin Inform, vol. 07, no. 3, pp. 850–869, 2016, doi: 10.4338/ACI-2016-03-R-0042. [11] P. Kumar, R. Chauhan, T. Stephan, A. Shankar, and S. Thakur, “A Machine Learning Implementation for Mental Health Care. Application: Smart Watch for Depression Detection,” in 2021 11th International Conference on Cloud Computing, Data Science Engineering (Confluence), Jan. 2021, pp. 568–574. doi: 10.1109/Confluence51648.2021.9377199. [12] E. M. Smets, B. Garssen, B. Bonke, and J. C. De Haes, “The Multidimensional Fatigue Inventory (MFI) psychometric qualities of an instrument to assess fatigue,” J Psychosom Res, vol. 39, no. 3, pp. 315–325, Apr. 1995, doi: 10.1016/0022-3999(94)00125-o. [13] L.-L. Chuang, Y.-F. Chuang, M.-J. Hsu, Y.-Z. Huang, A. M. K. Wong, and Y.-J. Chang, “Validity and reliability of the Traditional Chinese version of the Multidimensional Fatigue Inventory in general population,” PLOS ONE, vol. 13, no. 5, p. e0189850, May 2018, doi: 10.1371/journal.pone.0189850. [14] T. R. Hoens and N. V. Chawla, “Imbalanced Datasets: From Sampling to Classifiers,” in Imbalanced Learning, John Wiley Sons, Ltd, 2013, pp. 43–59. doi: 10.1002/9781118646106.ch3. [15] J.-C. Ho et al., “Work-related fatigue among medical personnel in Taiwan,” Journal of the Formosan Medical Association, vol. 112, no. 10, pp. 608–615, Oct. 2013, doi: 10.1016/j.jfma.2013.05.009. [16] Y. Tran, N. Wijesuriya, M. Tarvainen, P. Karjalainen, and A. Craig, “The Relationship Between Spectral Changes in Heart Rate Variability and Fatigue,” Journal of Psychophysiology, vol. 23, no. 3, pp. 143–151, Jan. 2009, doi: 10.1027/0269-8803.23.3.143. [17] Y. Liu, L.-M. Wu, P.-L. Chou, M.-H. Chen, L.-C. Yang, and H.-T. Hsu, “The Influence of Work-Related Fatigue, Work Conditions, and Personal Characteristics on Intent to Leave Among New Nurses,” Journal of Nursing Scholarship, vol. 48, no. 1, pp. 66–73, 2016, doi: 10.1111/jnu.12181. [18] Q. Li et al., “Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia,” New England Journal of Medicine, vol. 382, no. 13, pp. 1199–1207, Mar. 2020, doi: 10.1056/NEJMoa2001316. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80446 | - |
| dc.description.abstract | "因工作產生的疲勞一直是職場上值得關注的問題之一。此外，醫護人員的工作疲倦，不僅會影響醫護人員的健康，甚至有可能影響患者的安全。過去的研究方式，與過勞相關的研究多是在下班後，以問卷和量表的形式進行，而不是即時監測的方式。隨著科技的進步，穿戴式設備的發明提供了一種可行的解決方案，可以在不影響醫護人員日常工作的情況下，進行即時生理測量。同時，機器學習技術有了巨大的進步，並已應用於各個領域。在這樣的狀況下，我們才能嘗試建構一個可以即時監控過勞發生的警告系統。 此前瞻性觀察型研究於2021年3月10日至6月20日，在台大醫院急診室進行。納入研究的醫護人員會配給一支智慧型手錶 (ASUS VivoWatch SP)。這是一支消費級穿戴裝置，可以檢測心率和氧飽和度等多項生理測量值。此外參與者必須在每次工作前後，各完成一份多軸向疲勞量表。通過這種方式，我們可以找出有工作相關疲勞的醫護人員。接著我們利用量表及機器學習的方式，嘗試構建一個模型，用作即時工作相關疲勞的監控模式。 我們一共收集了1,542份有效的前後問卷。根據多軸向疲勞量表，有85人被判定有與工作相關的疲勞。在參與實驗的醫護人員中，有87.7%的人從事護理師的工作；以上班時間而言，47.7%的醫護人員於試驗期間上小夜班 (15:30~23:30)，44.5%的人員則是白班 (07:30~15:30)。我們使用了幾種目前最突出的模型 (State of the Arts) 的決策樹演算法進行建構。針對全體受試者，通過CatBoost分類器模型，在接收者操作特性曲線 (Receiver Operator Characteristic Curve, ROC) 的曲線下面積 (Area Under the Curve, AUC) 方面得到較好的表現0.838（95% CI：0.742 – 0.918）。而精確召回曲線下面積 (Area Under the Precision-Recall Curve, AUPRC)為0.527（95% CI：0.344 – 0.699）。除此之外，我們還對 35歲以下的護理師進行了次群組分析。在操作特性曲線下面積 (AUC) 得到更好的性能，其結果為0.928（95% CI：0.839 – 0.991），而精確召回曲線下面積 (AUPRC) 為0.781（95% CI：0.617 – 0.0.919）。在這個次群組分析，通過XGBoost得到比CatBoost分類器模型更好的結果，但此模組在回放到整體群組時，並不能得到更好的結果。 從穿戴式裝備萃取出的上百個特徵裡，我們利用了31個選定特徵，成功構建了一個機器學習模型。該模型能夠針對在急診室工作的醫護人員，進行與工作相關的疲勞風險進行分類。未來，我們可以將該工具應用在更多的急診人員上，有助於辨認出有工作相關疲勞風險的醫護人員，進而避免憾事發生。" | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-24T03:06:50Z (GMT). No. of bitstreams: 1 U0001-0912202110391800.pdf: 3403035 bytes, checksum: eaba576b48cc1cbac14a1f252523f213 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 誌謝……………………………………………………………………………..……….i 中文摘要………………………………………………………………………………..ii ABSTRACT…………………………………………………………………………….iv CONTENTS…………………………………………….……………………………...vi LIST OF FIGURES…………………………………………..………………….………vii LIST OF TABLES……..……………….……………………………………………….viii Chapter 1 Introduction……………..……………………………………….………...1 Chapter 2 Materials and method…..………………………………………….………3 2.1 Data collection………………………………………………………………..3 2.2 The machine-learning method………………………………………….…….4 Chapter 3 Results………………..………………………………..…………………..6 Chapter 4 Discussion………………………………...………………………………25 4.1 Comparison with Previous Studies………………………………………….25 4.2 Interpretation of Current Study……………………………………………...26 4.3 Feasible for Clinical Application……………………………………………28 Chapter 5 Limitations…………………………...…………………………………...30 Chapter 6 Conclusion………………………………...……………………………...32 REFERENCE…………………………………………………………………………..33 | |
| dc.language.iso | en | |
| dc.subject | 即時監控 | zh_TW |
| dc.subject | 過勞 | zh_TW |
| dc.subject | 醫護人員 | zh_TW |
| dc.subject | 穿戴式裝置 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | machine learning | en |
| dc.subject | work-related fatigue | en |
| dc.subject | real-time monitoring | en |
| dc.subject | healthcare provider | en |
| dc.subject | wearable device | en |
| dc.title | 以智慧型手錶的生理徵象監測建立急診醫護過勞示警 | zh_TW |
| dc.title | The application of smart watch monitoring to construct an overwork prediction and alarm model for emergency healthcare professionals | en |
| dc.date.schoolyear | 110-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.author-orcid | 0000-0003-4178-1694 | |
| dc.contributor.oralexamcommittee | 呂宗謙(Hsin-Tsai Liu),趙坤茂(Chih-Yang Tseng),簡意玲,許凱平 | |
| dc.subject.keyword | 過勞,醫護人員,穿戴式裝置,機器學習,即時監控, | zh_TW |
| dc.subject.keyword | work-related fatigue,healthcare provider,wearable device,machine learning,real-time monitoring, | en |
| dc.relation.page | 36 | |
| dc.identifier.doi | 10.6342/NTU202104525 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2021-12-10 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 生醫電子與資訊學研究所 | zh_TW |
| 顯示於系所單位： | 生醫電子與資訊學研究所 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| U0001-0912202110391800.pdf 授權僅限NTU校內IP使用（校園外請利用VPN校外連線服務） | 3.32 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。