互聯網的發展與近年來數據平台受到公私部門重視，資訊的取得與流通變得便捷，中國房地產文化目前有別於台灣，尚無實價登錄機制且地域面積廣大，傳統估價模型可能無法直接應用，面對房地產背後眾多的影響因素，本研究將預測建模目標放在泡沫化尚不嚴重且較具有潛力的中國新一線城市杭州市，自新浪二手房網爬取杭州市房地產數據，並自國家統計局取得各地區行政支出數據，作為實證分析資料。結合自動程序爬蟲抓取數據、統計分析與機器學習方法，期望對中國房地產建立一混合非監督式與監督式學習之模型。 在分群結果之後建構模型採用之技術為C5.0、三層CHAID、五層CHAID與Neural Network，挑選出最適合的模型為使用混合模型後的C5.0決策樹方法，達到降低變數維度亦提升或達到相當的預測準確率的雙贏目標，模型中行政地區、面積、總樓層為最頻出現的重要變數。 另外透過集群分析於行政支出的應用，發現2016年度杭州市投入的行政支出集中於余杭區、蕭山區、濱江區，成為賣屋及購屋者的第二項決策標準。 / In recent years, with the growth of the Internet and the importance of data platform on public sector and private sector. Getting and sharing information are made easily. The culture of real estate in China is different from Taiwan. For instance, there is no actual house price registration system. Furthermore, traditional estimate model may not be directly applicable to China which has the vast geographical area of the mainland. There are many factors to influence house price model. This study focus on Hangzhou city. Because the burst of real estate bubbles is not serious as first-tier cities and it is one of new first-tier cities in China. The research data were crawler from Sina second-hand housing website and National Bureau of Statistics. By using auto web crawler skill, statistical analysis, and machine learning method to build a real estate model in China, which was combining unsupervised learning method with supervised learning method. After clustering Hangzhou second-hand housing data, this study used C5.0, three layers Chi-Square Automatic Interaction Detector(CHAID), five layers CHAID, and Neural Network(NN). The study goal are both reducing dimension and getting better forecast accuracy. Choosing clustering- C5.0 model as appropriate house price model to achieve win-win situation after comparing final result. Administrative region, area, and total floor are the top three high frequency influential factors. Applying Clustering Analysis to administrative expenses data in Hangzhou, the study found that the government resource focus on Yuhang, Xiaoshan, and Binjiang. It can be the second decision-making criterion for house sellers and house buyers.

本研究主旨是希望提供一個智能金融演算法交易平台，以Django CMS作為網頁框架，區分成研發環境與交易環境，完整的功能包含用戶研發、用戶測試以及使用演算法服務。用戶研發與測試上採用IPython的互動式開發介面，利用JupyterHub進行管理與配置，能夠同時提供多個用戶存取平台，使得平台足以負載大規模用戶的使用；而演算法服務經由Celery包裝成任務，以利交付給後台進行分散式運算。搭上近年來深度學習的熱潮，平台額外擴充Tensorflow套件與GPU建置，支援多核及高速演算法運算。 面對存取大量、複雜且結構化的金融資料，本研究的資料庫採用HAWQ做為解決方案，利用其極大量平行化的架構，改善過往存取大數據所造成的系統複雜性與效能瓶頸，並搭配Ambari達到創建、監視及管理Hadoop分散式集群的功用，讓開發者在部署與維運上都將事半功倍。 由於採用新的資料庫HAWQ，傳統的資料表設計將不利反傷，因此本研究會針對程式端存取資料庫裡的金融資料，量身打造適合的資料表設計，並對其做效能評測，以確保資料能有效且迅速地被程式所取用。 / The purpose of this research is to provide a smartly algorithmic trading platform with financial data. I use Django CMS as a web framework and consisting of Develop environment and Trade environment. The entire functions of the platform include “User Research and Development”,” User Testing” and “Algorithmic Services”. “User Research and Development” and “User Testing” using IPython interactive development interface, with JupyterHub management and configuration, can simultaneously provide multiple user accessing and make the platform enough to support more and more users; “Algorithmic Services” using Celery to package algorithms into tasks can facilitate the delivery to the Server for distributed computing. By means of the growth of Deep Learning in recent years, the platform adds extra Tensorflow and GPU deployment to support multi-core and high-speed algorithm computing. In face of accessing large number of complex and structured financial data, I choose HAWQ as the database in this research. Its extremely massively parallel processing can alleviate the complexity of system and the bottlenecks of efficiency caused by accessing massive number of data. Combing HAWQ with Ambari can achieve the functions of creation, monitoring and management of Hadoop distributed cluster. The developers will do much more easily in deployment and maintenance. The traditional table design may not fit in with the new database HAWQ, so this research will design appropriate table, and evaluate its performance to ensure that data can be accessed effectively and quickly from programs.

作業基礎成本制度（Activity-Based Costing, ABC）為現行管理會計制度中，為較多企業所採用之制度，吳安妮教授在經過多年理論與實務之研究後，將ABC制度IT系統商品化，並與許多不同的制度整合為一體，命名為「作業價值管理系統（Activitiy Value Management System,AVMS）」，藉由作業價值管理能提供管理階層正確、即時、攸關之資訊，並協助其做出較適當之管理決策。 　　大數據（Big Data）被許多產業所使用，藉由歷史資料與未來預測，開創了新市場與新商業模式，而大數據也結合了許多制度，例如工業4.0、物聯網等，但卻沒有與管理會計相結合之研究，本研究藉由作業價值管理與產能管理之結合進行大數據之分析，初步的將管理會計與大數據結合，並同時協助個案公司發現有關產能管理之問題，並改善之。 　　本研究使用個案研究法，個案公司為一民防工程與地下空間設計之公司，藉由該個案公司所處產業之產業資料、未來趨勢、競爭者資料、作業價值管理資料等，分析找出個案公司之產能管理問題，並協助個案公司解決所發現之問題以提升管理之效率。 / Activity-Based Costing (ABC) is a well-known management accounting method and used by many companies. After professor An Wu’s 30-years research, she put Activity-Based Costing into IT system and named it Activity Value Management System (AVMS). This system provides correct and immediate information for company’s manager which can help them making a good decision. 　　Big data Analysis is used by many industry for creating new markets and new business models. Big Data Analysis combined with many systems such as Industry 4.0 and Internet of Things (IOT), but there aren’t any integration with management accounting. In this thesis we will Integrate Activity Value Management and Capacity Management with Big Data Analysis. By doing so, this can help the company reviving and solving the problem of Capacity Management. 　　The thesis is a case study with a China Basement designing company. Using the industry information, future trends, Competitor information and AVM data we can not only figure out the problem of Capacity Management that the company is facing but also help the company solving them.

資料的快速增長與變化以及分析工具日新月異，增加資料分析的挑戰，本研究希望透過一個完整機器學習流程，提供學術或企業在導入大數據分析時的參考藍圖。我們以Spark作為大數據分析的計算框架，利用MLlib的Spark.ml與Spark.mllib兩個套件建構機器學習模型，解決傳統資料分析時可能會遇到的問題。在資料分析過程中會比較Spark不同分析模組的適用性情境，首先使用本地端叢集進行開發，最後提交至Amazon雲端叢集加快建模與分析的效能。大數據資料分析流程將以微博為實驗範例，並使用香港大學新聞與傳媒研究中心提供的2012年大陸微博資料集，我們採用RDD、Spark SQL與GraphX萃取微博使用者貼文資料的特增值，並以隨機森林建構預測模型，來預測使用者是否具有官方認證的二元分類。 / The rapid growth of data volume and advanced data analytics tools dramatically increase the challenge of big data analytics services adoption. This paper presents a big data analytics pipeline referenced blueprint for academic and company when they consider importing the associated services. We propose to use Apache Spark as a big data computing framework, which Spark MLlib contains two packages Spark.ml and Spark.mllib, on building a machine learning model. This resolves the traditional data analytics problem. In this big data analytics pipeline, we address a situation for adopting suitable Spark modules. We first use local cluster to develop our data analytics project following the jobs submitted to AWS EC2 clusters to accelerate analytic performance. We demonstrate the proposed big data analytics blueprint by using 2012 Weibo datasets. Finally, we use Spark SQL and GraphX to extract information features from large amount of the Weibo users’ posts. The official certification prediction model is constructed for Weibo users through Random Forest algorithm.

重大傷病是我國全民健保的主要特色之一，民國105年重大傷病領證人數為95萬6626人（約4%人口），但其醫療費用超過全國四分之一，且盛行率有逐年上升的趨勢（資料來源：衛生福利部中央健康保險署）。其中，癌症又為重大傷病的首位，佔了重大傷病發證數的49%，雖然癌症發生率每年僅些微上升，但因罹癌後死亡率也逐年下降，而且癌症發生率隨年齡而增加，預期癌症盛行率將隨人口老化而快速上升，醫療利用與支出亦會愈趨上升，加重健保財務的負擔。有鑑於癌症盛行率的增加，健保署於兩年前提高癌症病患換新卡的資格，於103年停發約1萬7000張癌症領證數，但追根究底的解決之道仍在於及早發現與治療，不僅可提昇國民健康，更可有效率使用醫療資源。 本文使用全民健康保險資料庫，以探討國人罹癌前後的健康狀況為目標。透過資料庫的就醫資料，包括重大傷病證明明細檔（HV）、重大傷病門診處方及治療明細檔（HV_CD）、承保資料檔（ID）、2005年百萬人抽樣檔之門診處方及治療明細檔（CD），套用大數據的資料分析方法，探討國人罹患癌症的相關特性。首先對癌症病患進行基本資料之分析，接著探討不同準則下在判定癌症發生與罹癌死亡人數之間的估算差異，整合HV與HV_CD兩個資料庫，選擇可信度較高的方式作為估算癌症發生率與罹癌死亡率的基礎。研究發現，以退保資訊判斷癌症患者是否死亡，錯誤率優於先前根據就醫記錄。本文研究希冀可供政府擬定癌症相關的醫療策略，提高癌症病患的就醫意願及治癒率，增進國人健康，並且有效控制健保支出。

桌上遊戲從休閒娛樂逐漸融入到學校教育，運用巧妙設計的遊戲機制引發學生遊玩意願，進而在愉悅中學習。數位桌遊，一個透過結合數位科技的優勢輔助學習與娛樂的概念隨著教育型桌遊而崛起；然而從產業、學習、娛樂等角度來思考，數位桌遊究竟應具何特性？其體驗是否良好？學習是否有效？透過這些問題，本研究旨在（1）瞭解桌遊產業與玩家對數位桌遊的需求，（2）設計一款體驗供需法則的數位桌遊，（3）評估數位桌遊的遊戲性與學習效益。 首先，本研究運用體驗式學習圈與建構主義等學習理論設計出桌遊《寶島建設》，接著透過訪談桌遊產業各利害關係人了解產業對數位桌遊的想像與需求，透過彙整訪談內容建立數位桌遊的設計指標，最後本研究投入研發數位桌遊與數據分析系統，用以分析學習者的學習歷程與經驗。 本研究共有32位參與者，在進行遊戲期間會採集參與者的操作行為和遊戲資料作為分析，遊戲後會填寫含有心流經驗和遊戲接受度的問卷，並接受遊戲性與學習內容相關的訪談。實驗結果顯示，參與者普遍對《寶島建設》感到滿意，從競標的數據上顯示參與者逐漸掌握資源的價格區間；所開發的數據分析系統亦能發現參與者未達表現的原因，進而對學習者提出有效建議。 總結，本研究成果為（1）透過訪談瞭解桌遊產業對數位桌遊的需求與想像。（2）設計出能體驗與學習供需法則的數位桌遊《寶島建設》，並獲得遊戲參與者們對遊戲體驗正向的回饋。（3）數據分析系統能透過歷程分析了解學習者的困難與障礙，從數據分析圖表裡也可發現學習者逐漸掌握價格區間，這顯示透過數位桌遊《寶島建設》的競標機制能有效學習掌握需求與價格的關係。 / Board games in Taiwan has risen from leisure and entertainment towards teachings in schools, by introducing fascinating game mechanism and theme to enhance student motivation makes learning more fun. Digital board games, a concept combining the advantages of digital technologies to enhance learning and entertaining arose with the rise of educational board games; however, from the aspect of industry, learning and entertainment, what characteristic should digital board game have? Does it create good experience? Is learning effective? Through these question, this research aims to (1) Understand the visions and needs of industry towards digital board game, (2) Design a digital board game to learn the law of supply & demand, (3) Evaluate the learning effectiveness and gameplay. First, the research uses the experiential cycle and constructism learning theory to design the board game Formosa Construction Ltd, then interview several industrial stakeholders to understand the needs and visions of digital board game, through the interviews concluded a design guidelines, finally the digital version of Formosa Consturction Ltd was built along with the data analysis program use to evaluate user experience and learning portfolio in game. Experiments was conducted with 32 participants, gameplay data are collected during gameplay, participants was asked to fill in a questionnaire with flow experience and acceptance, an interview session regarding gameplay and learning will be held after the questionnaire. Results indicate that participants are satisfy with the game, and data collected from auction showed that participants were progressively mastering the price range; The data analysis program was able to find reasons for participants that did not perform well, having chance to provide advice to learners. In conclusion, the research results are (1) Understand the needs and visions of digital board game through interviewing The Taiwan Board Game Industry. (2) Design Formosa Construction Ltd and obtain positive feedback. (3) The data analysis program showed the obstacles learners met through portfolio analysis, auction data analysis also showed participants was progressively mastering the price range, showing that Formosa Contruction Ltd is effective in learning the relation between needs and price.

我國每十年進行一次人口普查，以取得國人經常活動地區的資訊，作為中央及地方政府政策規劃的參考。然而，十年一次的人口普查無法即時反映各地區人口特質及其活動，隨著普查完訪率逐年下降、個人資料保護法意識抬頭等趨勢，普查的涵蓋率及其資料品質愈加受到質疑，近年各國思考以其他資料蒐集方式取代傳統普查。我國實施全民健康保險制度已逾20年，民眾納保率超過99%，因此本文以全民健保資料庫為研究素材，透過個人就醫行為探討國人經常活動地區，透過剖析各種疾病的就醫行為，可作為政府評估醫療資源規劃的參考。 本文以全民健保資料庫為依據，探討我國國民選擇醫療地點的特性，作為經常活動地區（或是常住地）的輔助參考。過去研究大多利用上呼吸道感染（俗稱感冒）作為估計常住地的依據，但每年平均只有接近70%國人會因感冒而就醫，其中青壯年、老年人因感冒而就醫的比例明顯較低，以此作為常住地的估計基礎恐有涵蓋率不足之虞。本文依據健保資料庫中的2005年百萬人抽樣檔，包括就醫門診處方及治療明細檔(CD)、承保資料檔(ID)等資料，比較數種常住地判斷的參考準則（包括感冒就醫），分析各方法所觀察到資料的特性及限制，評估以這些準則作為判斷常住地的可行性。 結論：本文提出除了感冒就醫之外的三種常住地推估準則，分別為：因為感冒或是消化就醫、單次健保補助金額較低、基層院所就醫。以樣本涵蓋率量而言，三種準則都能改善感冒就醫涵蓋率的不足，其中以單次金額與基層院所就醫的樣本數增加最多。另外，如果與所有門診資料、普查資料的人口資料比較，發現單次金額與基層院所就醫推估的人口年齡結構最為接近，但單次金額的縣市（地區）結構與普查資料的差異較大。 限制：受限於青壯年人口就醫率較低，本文提出的幾種常住地判斷準則在20歲至44歲的涵蓋率仍然偏低，建議未來研究可經由權數調整修正樣本的年齡等人口結構及比例，或是仰賴就醫以外的紀錄推估，但須考量資料串連及品質等問題。 / Many countries conduct population census every 10 years to acquire the information of population structure and its trend, but the information is not likely to updated since the 10-years period is usually too long. Moreover, the low response rate of questionnaire and the enforcement of Personal Information Protection Act further jeopardize the population census and many question its data quality. Thus, quite a lot of countries are seeking alternatives for collecting the information of de jure population, replacing the regular population census. In this study, we explore the possibility of using the data from National Health Insurance (NHI) Research Database for acquiring the information of de jure population in Taiwan. Taiwan started the NHI in 1995 and more than 99% of Taiwan population are covered. Since the medical accessibility created by the NHI, Taiwan’s people tend to visit medical institutions near to where they live, when they have minor diseases. Past studies showed that the upper respiratory tract infection (or cold) is a popular choice of minor diseases. We will evaluate if the cold is a good candidate and propose alternative criteria for the definition of minor diseases. We found that the proportion of populations with upper respiratory tract infection is about 70% and it is age dependent, with the elderly the lowest. On contrary, the records of smaller amounts and the records of physician clinics (or general practice clinics) can cover more than 90% population, much better than the records of upper respiratory tract infection. The records of digestive system diseases and upper respiratory tract infection can also increase the coverage of elderly population. We recommend using the medical records of smaller amounts to acquire the de jure population.

政府為促進國人健康,並以社會保險的形式分攤弱勢團體的就醫需求,於民國84年開始實施全民健康保險,實施至今超過20年,而且納保率已高達99%。重大傷病證明是全民健保的主要特色之一,持有重大傷病證明卡的病患就醫時可免除部分負擔,減輕罹患重病患者的醫療負擔。截至106年2月約有4%國人領有重大傷病證明卡,但其醫療費用佔健保支出超過 27%,預期這兩個數值會因人口老化而逐年上升,使得重大傷病的相關議題越來越受到重視。 本文以全民健保資料庫中的重大傷病證明明細檔(HV)為基礎,以2005年百萬人抽樣檔之承保紀錄檔(ID)、門診處方及治療明細檔(CD)及住院醫療費用清單明細檔(DD)輔助,探究罹患重大傷病發生及死亡議題,提出判定發生、死亡的準則,並且依此分析各種疾病發生率與死亡率的關係。另外,本文也使用資料庫內容驗證重大傷病患者與非重大傷病患者之間醫療費用的差異,研究也發現新發生的病患就醫率偏低,並以國際疾病分類代碼驗證重大傷病門診處方及治療明細檔(HV_CD)資料抓取的準確性。 / Taiwan started National Health Insurance (NHI) in 1995, for more than 20 years, and more than 99% people are covered in this social insurance plan. It is believed that the NHI has further enhanced the health of Taiwan’s people.Catastrophic illness(CI)card is one of the key features in the NHI and people with this card can enjoy waiver of copayment and other medical benefits which reduce the financial burden of CI patients. For example, about 4% Taiwan’s population were with the CI card and they spend more than 27% of total medical expenditure of NHI. Since the probability with CI increases with age, the population aging and prolonging life are expected to worsen the financial burden of the NHI. Our goal is to explore the medical need and its trend of CI patients, via the data from the NHI Database, including Registry for catastrophic illness patients(HV), Registry for beneficiaries(ID), Inpatient expenditures by admissions(DD)and HV’s Ambulatory care expenditures by visits(HV_CD). Since the medical records do not cover all the required information, we propose several criteria for data analysis, such as the rules of judging whether the patients incur CI and the CI patients passed away. We found that the incidence rates and mortality rates of CI patients decrease with time. Also, there are questions about the data quality regarding the HV_CD database and more than 50% new CI patients do not have medical records of CI diseases.

本研究為第一本運用數位人文學於政治學領域的研究著作，隨著電腦科技及網際網路的快速發展，過去許多僅能以紙本方式加以儲存的政府檔案與歷史文件，在經過電腦科技的輔助下完成數位化工作。數位化後的文檔即可提供研究者進一步研究與分析使用；對此，本研究採取文字探勘(Text-Mining)技術針對數位化後的資料進行論述分析(Discourse Analysis)，由於本研究的資料來源是奠基在數位化文本上透過文字探勘技術所進行的論述分析；因此本研究在此提出一種基於數位人文方法上的論述途徑:數位論述方法(Digital Discourse Analysis, DDA)。以此作為本研究重要的論述與分析基礎，具體的論述分析步驟與策略則採用Norman Fairclough的三段論述分析法，將文本、論述與社會行動加以聯結，並與數位論述分析法加以結合，發展出基於文本的五大分析步驟:總篇數與總字數分析、關鍵詞變化趨勢分析、情緒形容詞使用分析、論述策略使用分析、重大議題聯結分析以及政策論述策略圖像等五項具體分析步驟與架構。有了上述具體的研究步驟後，研究者將研究的對象至於國內四大報(自由時報、中國時報、蘋果日報以及聯合報)，2009-2014年六年間針對ECFA所做的新聞報導內容以及兩大黨(民主進步黨、中國國民黨)，2009-2014年六年間ECFA政策新聞稿及相關內容，進行論述分析。運用上述五個具體步驟，勾勒出臺灣四大報與兩大黨在針對ECFA進行報導與政策論述時的論述策略使用及語言使用模式。 研究結果顯示，四大報中，中國時報與聯合報對於ECFA的新聞報導內容與論述策略較為類似，皆為正向報導、弱監督式以及社會民生議題聯結度低的報導策略；相較之下，自由時報的報導策略則明顯與中國時報及聯合報不同，自由時報常以強監督式的方式進行報導，並且與社會民生議題的聯結程度較高。蘋果日報方面，ECFA新聞議題並非其報導的重要內容，但與另外三大報相比，蘋果日報的ECFA新聞報導與國際議題的聯結程度較高，也較關心到區域經濟整合等相關議題。在兩大黨方面，對於ECFA的政策論述皆以政治類與經貿類關鍵詞為主要論述主軸，國際以及社會民生議題則是因循著不同年度社會、政治經濟脈絡的不同而策略性的出現；另外，兩大黨的ECFA政策論述皆以內銷導向、國際與社會議題工具性出現、論述立場尚屬中立等共同特性，但較不一樣的是民主進步黨的ECFA政策論述具有高監督性、而中國國民黨的ECFA政策論述則具有高針對性與回應性的特質。除了上述的研究發現外，本研究最後研究者也將此次的研究嘗試與社會科學研究趨勢往「語言」、「論述」、「詮釋」轉向進行討論，以及2000年以後政治學所出現的「改造運動」(Perestroika Movement)進行認識論與方法論的討論。讓本研究成果不僅具有實證價值、更擁有與社會科學和政治學研究發展趨勢對話之效。 / This study is the first book applying digital humanities on Political Science research. After digitized document, that can provide research to do more and deeper analysis. This study used digitized document to do discourse analysis. Due to technology development I advocated a brand new analysis framework: Digital Discourse Analysis (DDA). Practical discourse analysis steps, I introduced Norman Fairclough's idea about: three steps of analysis, link text, discourse and social action. Basic on Fairclough's framework, I developed a five steps analysis: the total number of articles and number of words analysis, keywords trend analysis, emotional adjectives use analysis, discourse strategy use analysis, association analysis of major issues and policy discourse strategies image analysis. I found that the China Times and the United Daily News are more similar on ECFA news reports contents and policy discourse strategies. They usually positive reported, low level of supervised and weak linked to livelihood and social issues reported. On the contrary, Liberty Times was more supervised reported and higher degree of link to social and livelihood issues. As for Apple Daily, ECFA issue is not an important part of its news reported issues. Two major parties ECFA policy discourse have some similar characteristics: begin domestic policy discourse-oriented, international and social issues instrumental appear and discourse stand neutrality. On the other hand, Democratic Progressive Party ECFA policy discourse has more high supervisory; Kuomintang ECFA policy discourse has highly-targeted and responsive characteristics. In this study, the researcher will also discuss some new trend of social science research including of language turn, discourse turn and interpretation turn. And discuss Epistemology and Methodology issues after 2000 Perestroika Movement. So that make this research not only has the empirical research value, but also has the value of dialogue on political science and social science research trends.

我國於2015年底通過新修正之個人資料保護法（以下簡稱「個資法」），將病歷納入特種個人資料中保護。目前個資法第六條特種個人資料列舉包含病歷、醫療、基因、性生活、健康檢查及犯罪前科之個人資料。雖然該條文係取法自國際賦予敏感性個人資料特別保護的模式，惟在個人相關健康資料保護部分，我國個資法不若歐盟一般資料保護規則（EU General Data Protection Regulation, GDPR）保護寬廣，納入資料之類型仍較國際立法例狹窄。尤其此次GDPR修法擴大特種個人資料空間，增列基因資料、生物性資料和性傾向，檢視我國特種個人資料列舉類型是否符合現今科技社會需求有其必要性。 過去研究針對健康資料個資法適用問題較少。大數據資料來源來自各處，以一般健康保健物聯網模式為例，自行操作之檢查數據或穿戴式裝置所蒐集之資料，若非須由醫師或其他之醫事人員施以檢查，而可由一般民眾自行測量之行為，該民眾自行測量之結果應不屬於個資法所謂之病歷、醫療或健康檢查個人資料，即非為特種個人資料。 惟大數據分析技術進步之環境下，健康資料亦攸關資料主體生理健康之敏感性，且容易連結並識別個人，考量健康資料敏感性提升，蒐集、處理、利用健康資料易侵犯到個人隱私，因此有加強保護之需求。將來可刪除個資法第六條第一項各種個人資料例示之「醫療」、「病歷」與「健康」資料，並新增「健康」或「與健康相關」之列舉項目。 但解釋「與健康相關」資料之內涵時不能無限上綱，在適用時應考量情境說，依據不同使用情境判斷是否為係作為特種個人資料利用，以排除一般性描述健康的使用情境。 / The change to the regulation of special categories of data (sensitive data) in the Taiwan Personal Information Protection Act (PIPA) in 2015 comes with the inclusion of medical records. The definition of sensitive data in the PIPA Article 6(1) refers to personal information of medical records, medical treatment, genetic information, sexual life, health examination and criminal records. However, the list of sensitive data in PIPA do not contain categories as broad as foreign legislation such as EU General Data Protection Regulation (GDPR). It is important to review the continuing relevance of existing categories of sensitive data in the light of change in social structures and advances in technology. Differ from “medical data” such as medical records, medical treatment and health examination, the collection, process and use of “health data” which is measured from wearable device, is not included in the sensitive data. Concerning the development of big data analysis, the “health data” which sensitivity enhanced is easy to identify an individual. It needs to give a higher level of protection to “health data” under PIPA. Therefore, this thesis suggests that medical records, medical treatment and health examination in PIPA Article 6(1) should be consolidated and amended to health records or data concerning health. However, this is not to say that the processing of all kinds of medical and health data should be regarded as the processing of sensitive data. But data, under certain contexts/circumstances may be treated as the processing of sensitive data.