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書中字有黃金屋 股票這個價位要不要買論文書籍站 otolaryngologist中文

另外網站Otolaryngology - Crossroads Providers也說明：An otolaryngologist or ENT doctor specializes in the medical and surgical care of the ear, nose and throat. Our board-certified ENT specialist can assist ...

長庚大學 資訊管理學系 陳春賢、曾意儒所指導 王奕晟的 利用機器學習技術與唾液蛋白標記建置口腔鱗狀細胞癌風險預測模型 （2020），提出otolaryngologist中文關鍵因素是什麼，來自於口腔鱗狀細胞癌、自體抗體、機器學習、極限梯度提升、模型堆疊。

而第二篇論文國立陽明交通大學 臨床醫學研究所 李光申所指導 胡皓淳的 運用深度學習辨識病理嗓音作為聲帶疾患診斷工具 （2020），提出因為有 的重點而找出了 otolaryngologist中文的解答。

最後網站Ear, Nose and Throat (Pediatric Otolaryngology) - CS Mott ...則補充：The Otolaryngology team at C.S. Mott Children's Hospital provides world class care for children with conditions affecting the ears, nose and throat.

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Nasal Fractures - Dr. Hung Che Wai [email protected]
Source: https://www.finddoc.com/en

Background: Mr. Ko's face was hit by a basketball while he was playing basketball with his friends. Mr. Ko started having nosebleed and feeling pain shortly after the hit. He thought he could recover after some rest; however, after two days, Mr. Ko was having persistent nasal congestion. He also noticed that his nasal bridge was slightly out of position.

(1) How can one distinguish between a minor nasal injury from nasal fractures? How can the doctor diagnose? 0:25

(2) What sequelae could develop from nasal fracture? 2:26

(3) What treatments are available for nasal fracture? 3:45

(4) If Ken’s nose is hit again after recovery, will it complicate his recovery? 5:03

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http://youtu.be/HnnhAnS2PKI

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利用機器學習技術與唾液蛋白標記建置口腔鱗狀細胞癌風險預測模型

為了解決otolaryngologist中文的問題，作者王奕晟 這樣論述：

Table of Contents指導教授推薦書口試委員會審定書中文摘要 iiiAbstract ivList of Figures viiList of Tables viiiChapter 1 Introduction 11.1. Background 11.1.1. Stage and treatment in OSCC 21.1.2. Risk factors in habitual behaviors 31.1.3. Premalignant lesions before OSCC 31.1.4. Autoantibodies

as protein biomarkers 41.1.5. Machine learning techniques 51.2. Motivation 51.3. Objective 6Chapter 2 Literature review 72.1. Malignant transformation in OPMD 72.2. Autoantibodies application in cancers 82.3. Machine learning application in clinical practice 9Chapter 3 Materi

als and methods 123.1. Study participants and design 123.2. Saliva processing and autoantibodies detection 133.3. Development of prediction models 143.4. Evaluation of predictive models 193.5. Statistical analysis 20Chapter 4 Results 224.1. Demographic characteristics and the le

vels of salivary IgA autoantibodies 224.2. Comparing the performance of predictive models 224.3. Variable importance in predictive models 29Chapter 5 Discussion 315.1. Salivary autoantibodies in OSCC risk prediction 315.2. Habitual behaviors are still important factors for predicting

OSCC 325.3. Machine learning has good performance for predicting high-risk cases of OSCC 325.4. Binary data format fits the ensemble model 345.5. Limitations 34Chapter 6 Conclusion 36References 37List of FiguresFigure 1. The flow chart of screening for OSCC. 13Figure 2. The supp

ort vectors, marked with grey squares, define the margin of the largest division between the two classes (Cortes and Vapnik 1995). 16Figure 3. A simple DNN architecture (Topol 2019). 18Figure 4. The flow chart of continuous variables processing. 19Figure 5. The flow chart of the predictive

models’ development and evaluation. 21Figure 6. Performance of six different data processing strategies for distinguishing the high-risk from the low-risk population by AUC. 24Figure 7. Variable importance plot in XGBoost 30List of TablesTable 1. All tuning parameters of the six models.

15Table 2. Baseline demographic characteristics and the levels of salivary autoantibodies in all participants. 23Table 3. The AUCs of the original MFI value and different age formats. 25Table 4. The AUCs of the binary MFI format and different age formats. 26Table 5. Holm-Bonferroni method p

ost hoc test (p-value) in converting age (years) and autoantibodies (MFI value) to binary format. 27Table 6. The AUCs of the logarithmic MFI format and different age formats. 28Table 7. The AUCs of the standardized MFI format and different age formats. 29

運用深度學習辨識病理嗓音作為聲帶疾患診斷工具

為了解決otolaryngologist中文的問題，作者胡皓淳 這樣論述：

前言:聲音沙啞會顯著的影響生活品質，且須經由喉部內視鏡檢查才能做出正確的診斷。然而此項檢查為侵入性且儀器設備昂貴導致並非所有基層診所都有此項設備。而且喉部內視鏡的判讀也需要有經驗的喉科醫師才能做出正確的診斷。本研究希望能夠利用人工智慧將聲音沙啞患者的嗓音音訊檔案進行深度學習，以期以沙啞嗓音辨識即可做出相關聲帶疾患的診斷，增加醫療之可近性。方法:我們共收集了正常嗓音、聲帶萎縮、單側聲帶麻痹、器質性聲帶病變、內收肌痙攣性發聲障礙五種不同類型的聲音，聲音內容為母音“阿”加上標準國語短文的朗讀。我們將各組音檔的對應的時頻圖藉由卷積式神經網路進行深度學習，並將訓練成果與人類專家做比較。結果:本研究共進

行兩次試驗。第一次試驗收集了29筆正常嗓音的音檔以及527筆病理嗓音的音檔，病理嗓音音檔包含了聲帶萎縮210筆、單側聲帶麻痹43筆、器質性聲帶病變244筆以及內收肌痙攣性發聲障礙30筆。深度學習模型在五分類（正常嗓音、聲帶萎縮、單側聲帶麻痹、器質性聲帶病變、內收肌痙攣性發聲障礙）的狀況下可以達到敏感度0.70、特異度0.90、準確度65.5%，與人類專家相比較，兩位喉科醫師的準確度分別為58.6%及49.1%，兩位一般耳鼻喉科醫師的準確度分別為38.8%及34.5%。第二次試驗收集了189筆正常嗓音的音檔以及552筆病理嗓音的音檔，病理嗓音音檔包含了聲帶萎縮224筆、單側聲帶麻痹50筆、器質性

聲帶病變248筆以及內收肌痙攣性發聲障礙30筆。深度學習模型在五分類的狀況下可以達到敏感度0.66、特異度0.91、準確度66.9%，與人類專家相比較，兩位喉科醫師的準確度分別為60.1%及56.1%，兩位一般耳鼻喉科醫師的準確度分別為51.4%及43.2%。結論:本研究顯示藉由深度學習辨識病理嗓音可以達到初步辨識聲帶疾患的目的。由於嗓音辨識非侵入性的檢查特色，可運用在廣泛性的初步篩檢或是即時性的初步判讀，像是在需要遠端醫療或是沒有喉部內視鏡的醫療不便區域，可以幫助第一線的基層醫師初步篩選出需要後送進行侵入性檢查的病人。且本次研究也成功建立了華語病理嗓音資料庫，可以做為未來聲音沙啞相關研究的資

料來源。

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