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居民對路跑賽事衝擊認知與態度之研究-以集集鎮為例;Residents' Perceptions Toward Road Race Event Impact and Attitude -The case of Jiji Town
No full text[[abstract]]本研究旨在探討居民對路跑賽事衝擊認知與態度之間的關係，以立意抽樣法抽取南投縣集集鎮行政轄區內全部六所學校(集集國小、和平國小、隘寮國小、永昌國小、富山國小與集集國中) 的486位教師與學生家長為樣本。以單因子變異數分析探討不同背景變項居民在路跑賽事衝擊認知與態度的差異情形，並以多元逐步迴歸分析居民的路跑賽事衝擊認知與路跑賽事態度的關係。研究結果顯示：一、居民在社會文化正面衝擊 (M=3.44)感受度較高；對經濟負面衝擊 (M=2.96)感受度較低。對路跑的態度中，居民中度支持在集集鎮舉辦路跑(M=3.66)，但並未特別關心在集集鎮舉辦路跑的相關訊息(M=3.28)。二、學生、未婚、有穩定收入、常接觸跑者和遊客的居民對路跑賽事衝擊認知較有正面感受。年輕族群、未婚、學歷較高、不從事觀光相關行業對路跑賽事衝擊認知較有負面感受。未婚的居民對路跑賽事有較支持的態度。三、經濟正面衝擊、社會文化正面衝擊、實質環境正面衝擊與實質環境負面衝擊可有效預測居民的路跑賽事態度。 經濟正面衝擊、社會文化正面衝擊與實質環境正面衝擊對支持路跑有正向的預測能力，為了提升居民對舉辦路跑的支持程度，建議主辦單位在規劃路跑時，可與居民、店家或者當地政府合作，規劃結合在地文化特色的路跑，吸引觀光人潮；同時可舉辦在地的產業展售會，帶動經濟效益。
The purpose of this study was to examine the relationship between the residents' perception and attitudes towards the impact of running events. A total of 486 residents who were teachers or parents of the six schools (Ji-Ji Elementary School, He-Ping Elementary School, Ai-Liao Elementary School, Yu-Ying Elementary School, Fu-Shan Elementary School and Ji-Ji Junior High School) in Jiji town finished the questionnaire. ANOVA and t-test were used to examine the difference of residents’ perception of impacts and attitudes by demographics, and stepwise regression was used to examine the relationship between impacts and attitudes. The results indicated that:As to the impacts of road race events, residents perceived higher positive sociocultural impacts, and lower negative environmental impacts. For the attitude, residents did not pay extra attention to the events, but fairly support hosting road race events in Jiji town.Residents who were students, unmarried, with stable income, and had more interactions with runners tended to perceive higher positive impacts. Residents who were younger, unmarried, higher educated, with non-tourism-related job tended to perceive higher negative impacts. Unmarried residents tended to have more supporting attitudes to the road race events. Resident’s perceptions of the positive economic impact, positive sociocultural impact, positive environmental impact, and negative environmental impact could predict their attitudes to the road race events. Due to the effects of the positive economic, sociocultural, and environmental impacts on the attitude, future road race event organizers could work with local residents and government to increase the residents’ support by combining some sociocultural characteristics to the event or hosting some exhibitions or festivals to promote tourism and local economy
工作壓力源對職家衝突與壓力效應之影響-從即時通訊軟體應用角度之探討;The Impact of Work Stress on Conflict and Pressure on Work and Family Professionals - A Discussion from the Application of Instant Messaging Software
No full text[[abstract]]工作和家庭生活對現今職家男女而言是最重要的生活領域，個人均需滿足或履行工作或家庭角色之責任。但因資訊科技的發展與普及，造成了工作與家庭角色及時間的分野有愈來愈糢糊的趨勢，科技雖然可以創造利益(降低成本及溝通時效)，但同時也形成壓力。像是行動裝置、即時通訊等這些科技都具有無所不在的特性，再加上網路系統，更是可以不分時間及地點傳輸訊息、溝通聯繫，增加工作者額外的工作負荷及工作家庭衝突。本研究將探討工作壓力、職家衝突及壓力效應之間的影響，更延伸探討使用智慧型行動裝置之即時通訊軟體做業務溝通之行為，會與工作壓力及工作家庭衝突造成何效應。本研究採用網路問卷收集樣本資料，並透過社群網站、即時通訊工具以及電子郵件發送問卷。本研究預定以壓力理論以及職家衝突模式之構念，加上科技壓力做相關因素探討。資料分析方法採結構方程模式，並以SPSS及 SmartPLS 作為主要統計分析工具，驗證研究模式中各變數的因果關係。研究結果如下：(一)工作壓力會「正向」影響工作家庭衝突(二)工作負荷與工作家庭衝突間無正相關 (三)科技侵犯會「正向」影響工作家庭衝突(四)科技超載與工作家庭衝突間無正相關(五)工作家庭衝突會「負向」影響工作滿意度 (六)工作家庭衝突會「負向」影響家庭滿意度 (七)工作家庭衝突會「負向」影響工作身心健康。
Work and family life are the most important areas of life for men and women in work today, and individuals are required to meet or fulfill the responsibilities of work or family roles. However, the development and popularization of information technology can create benefits (reduce costs and communication time), and also create pressure. Resulting the boundaries in the work and family roles (time) of the increasingly blurrend trend, Such as mobile devices, instant messaging and other technologies are all ubiquitous and features, pervasive coupled with the network system, but also regardless of time and place to transmit messages, communication links, increase the work-loading of workers and work-family conflict.This study will examine the impact of work-related stress, work-family conflict, and further explore the effect of communication with the instant messaging software using intelligent mobile devices to work with work-related stress and work-family conflict. This study uses a web-based questionnaire to collect sample data and send a questionnaire through community sites, instant messaging tools, and e-mail. This study is intended to use pressure theory and work-family conflict model, coupled with the technical pressure to do the relevant factors. The data analysis method was used to model the structural equation, and SPSS and Smart PLS were used as the main statistical analysis tools to verify the causal relationship between the variables in the research model. The results are as follows:(1)The work-related stress will positively affect the work-family conflict.(2)There is no positive correlation between work-loading and work-family conflict.(3)Scientific and technological violations will positively affect the work-family conflict.(4)There is no positive correlation between the overloading of scientific technology and Work-family conflict.(5)Work-family conflict can negativly affects job satisfaction.(6)Work-family conflict can negativly affects family satisfaction.(7)Work-family conflict can negativly affect the physical and mental health
診斷重度憂鬱症之患者後續更改診斷為雙極性疾患之預測因子分析：一個全國性的回溯型研究;Predictors for Switch From Unipolar Major Depressive Disorder to Bipolar Disorder: A Nationwide Population-based Retrospective Cohort Study
No full text[[abstract]]重度憂鬱症及雙極性疾患為主要的兩種情緒疾患，這兩種疾病有不同的治療策略及預後，然而，雙極性疾患可能以鬱症發作作為初次發病的表現，因而被診斷為重度憂鬱症，進一步造成治療失敗，先前的研究也指出有一定比例被診斷為重度憂鬱症的患者，在追蹤一段時間後，會被診斷成雙極性疾患，這類「隱藏」的雙極性疾患患者可能是導致治療失敗的原因之一。 本研究為使用台灣健康保險資料庫所做的全國性研究，從台灣健康保險資料庫中找出診斷為重度憂鬱症的個案，並追蹤後續更改診斷為雙極性疾患的比率，同時利用診斷重度憂鬱症前的臨床資料，找出與診斷轉換相關的預測因子，本研究使用關聯規則來發掘與診斷轉換相關的臨床特徵，並使用分類與迴歸樹法來發展診斷轉換的風險分類模組。 本研究一共納入2820位重度憂鬱症個案，其中61%為女性個案，在追蹤期間，536位個案被診斷為雙極性疾患 (19%)。在關聯規則的結果顯示，2月、3月、4月、5月、8月、9月、10月、11月的平均精神科門診次數較多、年齡介於20歲至39歲間、每年平均住院次數較多、每年平均使用的鎮靜劑種類較多等變數，和診斷轉換較為相關。此外，分類與迴歸樹法發現利用總精神科門診次數、3月平均門診次數、2月平均精神科門診次數、8月平均精神科門診次數、秋季平均門診次數、以及平均使用鎮靜劑種類等六個變數，能將重度憂鬱症個案後續是否會更改診斷為雙極性疾患的風險分為高、中、低三組，此風險分類模組可被便捷地在臨床情境中使用，並協助醫師早期正確地診斷出雙極性疾患患者，以安排適當地治療。
Unipolar major depressive disorder (MDD) and bipolar disorder are two major mood disorders. The two disorders have different treatment strategies and prognoses. However, bipolar disorder may begin with depression and could be diagnosed as MDD at the initial stage which may contribute to treatment failure. Previous studies indicated that a significant proportion of patients who were diagnosed with MDD will over time develop bipolar disorder. This kind of hidden bipolar disorder may contribute to the treatment resistance observed in MDD patients. In this population-based study, our aim is to investigate the rate and risk factors for a diagnostic change from unipolar MDD to bipolar disorder during a 10-year follow up using Taiwan National Health Insurance Research Database (NHIRD). Association rule mining (ARM) was used to discover the associations of bipolar conversion and clinical characteristics before enrollment. Furthermore, a risk stratification model was also developed for MDD to bipolar conversion by using the classification and regression trees (CART) method. There are 2820 MDD patients enrolled in our study, among whom 60.1% were women. During follow-up period, 536 patients was diagnosed with bipolar disorder (19.0%). The results of ARM showed that variables including mean psychiatric outpatient visits of February, March, April, May, August, September, October, and November, age between 20 and 39 years, mean annual hospitalizations, and mean annual use of benzodiazepine, composed association rules discovered in our work. Furthermore, the CART method identified 6 variables (total psychiatric outpatient visits, mean outpatient visits of March, mean psychiatric outpatient visits of fall, February, and August, and mean annual use of benzodiazepines) as significant predictors of risk of bipolar conversion. Using these variables, we could group patients into low, intermediate, or high risk for bipolar conversion. The risk stratification model can be easily applied in clinical practice and help to identify patients with bipolar disorder early and to arrange appropriate treatment for these patients
就醫民眾對個人資料保護不同態度之影響因素;Factors Influencing Patient's Attitude towards Self Protection on Personal Data
No full text[[abstract]]隨著資訊科技快速發展，越來越多產業領域重視資料累積、分析或預測的運用。相對也面對越來越多資料透明化、易取得，甚至濫用的情況發生。在面對提供資料得到便利的同時，又面臨著個人資料外洩的風險。本研究旨在探討就醫民眾在使用過資訊系統的掛號服務、手機應用程式以及健康存摺後，對於個人資料保護態度之影響因素。我們以隱私計算理論(Privacy Calculus Theory)為基礎，結合科技威脅迴避理論(Technology Threat Avoidance Theory)之相關變數，以歸納出使用者的影響因素。為避免網路問卷的母體偏差與不完整性，本研究採用實地在北中南三區醫院的實體發放調查的方式，共計回收448份，有效問卷為423份，有效樣本為94.4%。資料分析結果顯示，感知易用性、法律規範、隱私顧慮以及資料敏感性對個人資料保護有正向顯著影響；獎勵、感知有用性、威脅嚴重性以及威脅易感性則對個人資料保護無顯著影響。最後，本研究針對實務面與學術面提出具體建議與未來研究方向。
With the rapid development of information and communication technologies (ICT), manyorganizationsengage in extensiveICT applicationsand therefore a vast amount of personal data is collected, processed and used. Consequently, the personal dataisfrequentlyabused. In the face of being convenient at the same time, it is also facing the risks of personal data abused.The aim of this study is to explore factors influencing patient's attitude towards self protection on personal data. Based on the privacy calculus theory and technology threat avoidance theory,a research model is developed and several factors influencing patient's attitude towards self protection on personal data are examined.Asurvey of several hospitals from northern, central and southern Taiwan is conducted. The results of 423 usable responses were collected and the usable response ratewas 94.4%. The analytical results indicated that perceived ease of use, legal assurance, privacy concerns and health information sensitivity positively affect patient's attitude towards self protection on personal data. Finally, this study concludes with useful recommendations for academics and practitioners
結合深度學習與主動形狀模型於心臟左心室超音波影像之追蹤;Combining Deep Learning and Active Shape Model for Automated Segmentation of the Left Ventricle of the Heart from Ultrasound Data
No full text[[abstract]]本研究提出一種創新的應用，結合更快的區域卷積神經網絡(Faster Region with Convolution Neural Network, Faster R-CNN)與主動形狀模型(Active Shape Model, ASM)應用於心臟左心室超音波影像之自動分割。由於左心室形狀與外觀變化甚大，但藉由深度學習，我們僅須透過小的訓練集就可以適應不同的心臟相位和案例。比起其他傳統的分割方法，ASM是一種基於模型的分割方法，其中包含訓練與統計分析。CNN在目標識別方面表現優異，成為許多目標識別挑戰中的首選算法。而Faster R-CNN使用CNN提取影像特徵，改進區域提案方法，與Fast R-CNN檢測網路共享卷積特徵，使得目標檢測和識別近乎同時。本研究詳細描述了Faster R-CNN與ASM算法，並用於心臟左心室超音波影像。我們測試了醫生提供之臨床數據，結果證實我們的方法在完全自動化挑戰中達到了一定的準確率，且具備非常有競爭力的執行時間。
We introduce an innovative application that combines Faster Region with Convolution Neural Network (Faster R-CNN) and Active Shape Model (ASM) for automated segmentation of the left ventricle of the heart from ultrasound data. This combination is relevant for segmentation problems, where the left ventricle presents large shape and appearance variations, but we can use only small annotated training set to adapt different heart phase and case by deep learning. Compare to other tradition segmentation methods, ASM is a model-based segmentation methodology which incorporates training and statistical analysis. CNN is excellent in target recognition and it became the preferred algorithm in many target recognition challenge. Faster R-CNN uses the CNN to extract the image features, improves the region proposal method, shares the convolution feature with the Fast R-CNN, and makes the target detection and recognition almost instantaneous.This study describes the Faster R-CNN and ASM algorithms in detail and used on left ventricular ultrasound images. We test our methodology on the data from clinicians, and our approach achieves the equivalent accuracy in the state-of-the-art results for the fully automated challenge, while having very competitive execution time
影響Facebook藝人粉絲專頁持續使用之因素研究;A Study on the Factors Affecting the Continuous Usage of Facebook Artist Fan Pages
No full text[[abstract]]Facebook近年來快速的發展讓許多企業或是公眾人物想要透過Facebook粉絲專頁來進行銷或是宣傳，而藝人同樣地也可以靠Facebook粉絲專頁來做自我行銷。藝人不僅可以透過Facebook粉絲專頁來更新自己的即時動態，也可以透過Facebook粉絲專頁來與粉絲作互動。 本研究主要是以「資訊系統持續使用模式」(A Post-Acceptance Model of IS Continuance)作為主架構，並且整合「沉浸經驗」(flow experience)、「親密度」(intimacy)、「熟悉度」(familiarity)、「互動性」(interaction)這四個因素，藉以探討使用者在持續使用Facebook藝人粉絲專頁之影響因素。 而本研究的問卷是依據研究目的和架構所設計的，並且是採用結構化的設計，而調查對象是針對使用Facebook並加入藝人粉絲專頁的使用者，而調查方式是採用網路問卷的方式來進行調查。而最後本研究的問卷總共收到了520份，而在刪除掉不要的問卷之後得到了367份的有效問卷，然後用統計軟體來進行問卷資料的分析。其結果顯示「確認」會正向影響「知覺有用性」、「滿意度」、「沉浸經驗」、「親密度」、「熟悉度」，「知覺有用性」與「沉浸經驗」會正向影響「滿意度」，「滿意度」、「親密度」、「熟悉度」會正向影響「持續使用意圖」，「熟悉度」會正向影響「親密度」，「互動性」會正向影響「滿意度」。
In recent years, the rapid rise of Facebook have caused many companies or public to conduct marketing or publicity through Facebook fan pages, and the artists also can do marketing for themselves through Facebook fan pages. The artists not only can update their real-time dynamic information through Facebook fan pages, but also can interact with the fans through Facebook fan pages. This research adopts A Post-Acceptance Model of IS Continuance as the main structure, and integrates the four factors, including flow experience, intimacy, familiarity, and interaction, to explore the affecting factors of continuance intention of using Facebook artist fan pages for the users. The questionnaire of this research is designed on the basis of the research purpose and conceptual framework, and the structured design is adopted. Respondents of the questionnaire are the users that have used Facebook and joined Facebook artist fan pages, and the survey method is that the web questionnaire is adopted to investigate. At the end there are a total of 520 questionnaires that are received in this research, and after deleting the invalid questionnaires, there are 367 valid questionnaires that are obtained, and then valid questionnaires are analyzed by the statistical software. The results show that confirmation positively influences perceived usefulness, satisfaction, flow experience, intimacy, and familiarity. And perceived usefulness and flow experience positively influence satisfaction. And satisfaction, intimacy, and familiarity positively influence continuance intention. And familiarity positively influences intimacy. And interaction positively influences satisfaction
抗變異自適性調壓迴路控制設計;Variation-Resilient Adaptive Voltage Scaling Control Loop Design
No full text[[abstract]]欲推估參數下自適性電壓調控系統的結果傳統上須透過長時模擬才能得到一組參數的之預期結果，預先錯誤自適性電壓調控系統提出使用馬可夫模型讓參數對於自適性電壓調控系統的影響可透過簡易數學式完成推估，只需要一次性不同電壓下的延遲分布模擬，因此能夠快速的分析不同組參數對於自適性電壓調控系統的影響。本篇論文實作一個90nm晶片驗證預先錯誤自適性電壓調控系統，並使用馬可夫模型搭配10萬筆運算數量以及每20mV為一單位的不同電壓下之延遲分布，其推估之結果與實際之自適性電壓調控結果之電壓分布誤差為34.86%，但延遲分布使用1千萬筆pattern數量時其推估的結果與實際之電壓分布誤差為2.78%，此原因為模擬的延遲分布尾端資訊不足使得推估電壓機率有誤差。提出使用1千筆運算數量之延遲分布配合近似之高斯分布模型取代模擬10萬筆運算數量之延遲分布解決上述延遲分布尾端資訊不足之原因，其推估的結果與實際自適性電壓調控系統結果之電壓分布誤差為5.68%，分析參數 (N,、nlimit↓、nlimit↑)，其分析結果為N> nlimit↓> nlimit↑，透過控制參數調整錯誤率時，建議將nlimit↓設成1後且nlimit↑設成N*2%，透過調整N達到預期的效果，並以晶片驗證平均錯誤率落在10-2 到10-5 範圍。
Traditionally, to estimate one of AVS control parameters of AVS results takes large simulation time, pre-error AVS system have been proposed to simplify the procedure of estimate AVS result, which just simulated delay distributions at different voltage for once with Markov chain to estimate all set of AVS control parameters of AVS result. In this paper, we evaluate pre-error AVS system in 90nm CMOS, using delay distributions of 100,000 patterns and 20mV step voltage and Markov chain to estimate the AVS result which the error of RMSE of voltage scatter with AVS in 90nm CMOS is 34.86%, but the error of RMSE of voltage scatter with AVS in 90nm CMOS is 2.78% while increasing the patterns to 10,000,000 patterns, the reason of this difference is the tail information of delay distribution is not enough (i.e. limit precision). We proposed using delay distributions of 1,000 patterns with Interpolate Gaussian mathematic model to replace delay distributions of 100,000 patterns to estimate AVS results which the error of RMSE of voltage scatter with AVS in 90nm CMOS is 5.68%. We also analysis the sensitivity of AVS control parameters (N, nlimit↓, nlimit↑) which the result is N> nlimit↓> nlimit↑, therefore, fixed n_lb=1, n_ub=N*2% and adjust N to get the difference error rate from control parameters, and evaluate the adjust method in 90nm CMOS to get the error rate from 10-2 to 10-5
在訊息傳遞中估計邊的影響力;Estimating Edge Influence of Message Spread
No full text[[abstract]]社會網路分析在現今中是個重要工具，人們在社交網路平台上貼文可以開啟一個訊息傳遞。我們想要找出兩朋友之間的邊對訊息傳遞的影響，並且找出影響因素。另外，我們也提出一個快速且正確的方法來估計邊的重要程度。
Nowadays, social network analysis is important in our life. A post on social media like facebook can start spreading a message. We study the factor between message spread and ties of friendship. Also, we propose a method that can quickly and accurately estimate the importance of ties in a graph
在密集的多天線無線區域網路中基於流量相關性來開啟無線接取點之策略;Traffic-Correlated Green AP Activation Strategies for Multi-Antenna Dense WLANs
No full text[[abstract]]由於Wireless Local Area Networks (WLANs)具有穩定以及容易布建及價格低廉等優點，而隨著行動設備的普及，具有上網需求的使用者也越來越多，因而出現了Dense WLANs這項技術，意即access point (AP)部署的十分密集。使用如此高密度的是希望能應付尖峰時刻，眾多使用者上網所產生的需求，但由於使用者的生活習慣，導致流量需求變化量極大，在低流量時期，許多AP仍還是維持著開啟的狀態，此時過多的AP所消耗的電量就等於是一種浪費。而在過去對於減少耗電量的方式大多是使用Resource-on-Demand (RoD)的策略，可以根據使用者的需求，來決定需要開啟多少數量的AP。但隨著現今通訊設備的推陳出新，同時也帶給了省電策略新的可能，我們可以將硬體的新能力結合省電策略，達到更大的省電量。在本論文中，我們利用AP Multi User Multi-Input Multi-Output (MU-MIMO)的通訊技術，不僅可依照當前環境的狀態調整AP的開啟與關閉，還可更進一步調整其天線的組態；同時我們也提出了一個新概念：將其他AP當作衡量自己要否需要開啟的感測器，並使用AP的歷史流量資料去計算AP間的Pearson correlation coefficient (皮爾森相關係數) ，取得它們的流量相關性，以決定出下一個需要打開的AP。在我們的實驗模擬中，與其他現有省電方法進行耗電量的評比，結果顯示我們所提出的方法所消耗的電量是少於其他方法的。
Due to cheapness, stability and easy to deploy, Wireless Local Area Networks (WLANs) are becoming the most popular solution. With the popularity of mobile devices, there are more and more users with Internet demand. In order to cope with such high traffic, the term of Dense WLANs is born. It is such dense that there are thousands of APs per square kilometer. We use such high density AP in order to handle the traffic generated in peak hours. However, heavy traffic does not exist all the time. Due to behaviors and habits of users, the traffic demand changes a lots. During low traffic period, all APs still active, the power consumption of additional APs is wasted. The early researches leveraged Resource-on-Demand (RoD) strategy to reduce power consumption, it can turn on different number of AP according to user demand. Due to the progress of the hardware nowadays, it brings the new opportunity to power saving strategy. We can combine novel abilities of hardware into power saving strategy to assist us to obtain more power-saving. In our paper, we leverage Multi-User Multiple-Input Multiple-Output (MU-MIMO) technology of AP, not only can turn on/off of AP but also can furthermore adjust its antenna configuration. We also come up with a concept that other APs serve as their own sensors. In order to decide the next active AP, we use historical traffic data of each AP to calculate Pearson correlation coefficient between APs to acquire their traffic correlation. In our simulation, we compare our RoD strategies with other method in power consumption. The result show that the power consumption of our proposed solution is less than other method
引導式中醫辨證系統;A Guided Syndrome Differentiation System for Traditional Chinese Medicine
No full text[[abstract]]中醫臨床診斷時，中醫師會先透過望、聞、問、切四診蒐集患者的症狀，再根據患者的症狀辨識患者的證候。由於中醫辨證體系龐大複雜，中醫師通常不易完整記憶辨證體系，臨床診斷也不易充分推理。本研究團隊先前利用電腦的龐大記憶能力及迅速的推理能力，開發一個中醫虛證辨證系統，做為中醫師臨床診斷的輔助工具。該系統提供近1000個標準化的症狀，將患者的標準症狀輸入，系統可以迅速地計算該患者罹患各虛證的歸屬度。 本論文將在該系統的基礎上，提供更有效的臨床診斷輔助功能。第一，引導式的症狀輸入功能。為免除中醫師記憶標準症狀的困擾，新系統允許中醫師輸入任意的症狀描述，系統會自動將其轉換成標準症狀，或自動提供可能的標準症狀，引導中醫師選擇適當的標準症狀。第二，引導式的症狀建議功能。新系統計算完患者罹患各虛證的歸屬度後，會建議中醫師可以繼續詢問歸屬度最高證候的症狀。此功能不僅提供即時的辨證結果，也提供即時的症狀建議，避免患者或中醫師因遺漏症狀而影響辨證的正確性。此功能也協助系統蒐集完整的症狀，以利後續的病歷分析及研究。
In clinical diagnosis for traditional Chinese medicine, practitioners first collect symptoms and signs of patients through four examinations: inspection, smelling and listening, inquiry and palpation. Practitioners then differentiate syndromes of patients according to the collected symptoms and signs. Because of the huge complexity of the syndrome differentiation system, it is hard for practitioners to remember the entire syndrome differentiation system and to perform sufficient deduction on syndrome differentiation. Our research team had previously utilized the huge memory capability and fast deduction capability of computers to develop a deficiency syndrome differentiation system to aid clinical diagnosis. This system provided nearly 1,000 standardized symptoms. Given the symptoms of a patient, this system could quickly calculate the attribution degree of suffering each deficiency syndrome for the patient.Based on the previous system, this thesis would further provide two effective clinical diagnostic functions. First, the new system provides a guided symptom input function. To avoid memorizing a large amount of standardized symptoms, the system allows practitioners enter any symptom descriptions. The system would automatically convert them into standardized symptoms, or automatically hint proper standardized symptoms. Second, the new system provides a guided symptom suggestion function. After the system calculated the attribution degree of suffering each deficiency syndrome for the patient, the system will suggest practitioners to further collect the symptoms for the syndrome with the highest attribution degree. This function promptly provides the results of syndrome differentiation and the symptoms suggested to further collect. This function aids to ensure the correctness of syndrome differentiation by avoiding unintentionally missing symptoms by patients or practitioners. This function also facilitates the collection of the complete symptoms of each syndrome to promote the future analysis and research on medical records