問題的答案無所不包論文書籍站
國立政治大學 亞太研究英語博士學位學程(IDAS) 官大偉、卜道所指導 丁莉庭的 英屬錫蘭種姓制度中因汽車造成之社會流動: 地景價值變化與僧伽羅社會變遷 （2021），提出sarasa st-1 ptt關鍵因素是什麼，來自於英屬錫蘭、19 世紀末至 20 世紀中葉、機動車輛、主位研究法、歷史人、類學、僧伽羅 各種姓、社會轉型、景觀感知、橫向與縱向流動。
而第二篇論文國立臺北護理健康大學 國際健康科技碩士學位學程 Chien-Yeh Hsu所指導 賈馬瑞的 A MACHINE LEARNING MODEL FOR DYNAMIC PREDICTION OF CHRONIC KIDNEY DISEASE RISK USING LABORATORY DATA, NON‐LABORATORY DATA, AND NOVEL METABOLIC INDICES （2021），提出因為有 Chronic kidney disease、Glomerular filtration rate、Creatinine、Novel metabolic indices、Machine learning、Risk prediction的重點而找出了 sarasa st-1 ptt的解答。
英屬錫蘭種姓制度中因汽車造成之社會流動: 地景價值變化與僧伽羅社會變遷
為了解決sarasa st-1 ptt 的問題，作者丁莉庭 這樣論述：
摘要本研究是對英屬錫蘭僧伽羅人各種姓群體中社會流動性之調查。基於英國人為追求其殖民經濟 事業而引入錫蘭種種所帶來之機會，本研究側重於與種姓制度平行之社會組織制度之發展。僧 伽羅種姓制度是基於對傳統種姓職業之遵守，而英國引進之技術使某些低種姓得以填補新職位， 獲得成功並採用英國之新技術成為自己之技能。本論文之重點是僧伽羅各種姓對英國引進之機動車輛之採用。本人提出，居住在沿海低 地之僧伽羅低種姓，與歐洲殖民企業有更多之接觸，更有可能將機動車輛用作個人交通工具， 並且主要負責機動車輛之進口。另一方面，居住地遠離海岸、享受傳統政治影響之高種姓發現 其狀況滯後，因此也將機動車輛用於自己之種姓環境。本論
文是對錫蘭之機動車輛首次進行全 面研究。其方法要求分析發生橫向與縱向流動之僧伽羅人採用和使用機動車輛之相關現象。本 人在研究中利用歷史及人類學之觀點。本研究就英國殖民時期之人員、交通、所有權和個人使 用機動車輛情況進行了訪談來收集數據。本人用主位法來解釋僧伽羅社會結構轉型之過程及歷史景觀概念化之變化。本人從主位 法之角度提供了關於僧伽羅種姓結構變化之洞見。本研究有助於提供從 19 世紀末到 20 世紀中 葉島上新社會轉型之資訊。
A MACHINE LEARNING MODEL FOR DYNAMIC PREDICTION OF CHRONIC KIDNEY DISEASE RISK USING LABORATORY DATA, NON‐LABORATORY DATA, AND NOVEL METABOLIC INDICES
為了解決sarasa st-1 ptt 的問題，作者賈馬瑞 這樣論述：
Chronic kidney disease (CKD) is a major public health challenge with high prevalence, rising incidence, and serious adverse consequences. Developing effective risk prediction models is a cost-effective approach to predict and prevent complications of chronic kidney disease (CKD). This study aimed t
o develop an accurate machine learning model that can dynamically identify individuals at risk of CKD using various kinds of diagnostic data, with or without laboratory data, at different follow-up points. Creatinine is a key component used to predict CKD. These models will enable affordable and eff
ective screening for CKD even with incomplete patient data, such as the absence of creatinine testing. This retrospective cohort study included data on 19,429 adults provided by a private research institute and screening laboratory in Taiwan, gathered between 2001 and 2015. Univariate Cox proportion
al hazard regression analyses were performed to determine the variables with high prognostic value for predicting CKD. We then identified interacting variables and grouped them according to diagnostic data categories. Our models used three types of data gathered at three points in time: non-laborato
ry, laboratory, and novel metabolic indices data. Next, we used subgroups of variables within each category to train two machine learning models (Random Forest and XGBoost). Our machine learning models can dynamically discriminate individuals at risk for developing CKD. All the models performed well
using all three kinds of data, with or without laboratory data. Using only non-laboratory-based data (such as age, sex, BMI, and waist circumference), both models predict chronic kidney disease as accurately as models using laboratory and metabolic indices data. Our machine learning models have dem
onstrated the use of different categories of diagnostic data for CKD prediction, with or without laboratory data. The ML models are simple to use and flexible, because they work even with incomplete data, and can be applied in any clinical setting, including settings where laboratory data is difficu
lt to obtain.