• Journal of Environmental Science International
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• v.4 no.2
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• pp.223-232
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• 1995

New two macrocyclic compounds using as carriers of liquid emulsion menbrame, have been synthesized. These reuslts provide evidance for the usefulness of the theory in designing the systems. The efficiency of selective transport for heavy metal ions have been discussed from the membrane systems that make use of $SCN^-$,<>,$I^-$,CN- and $Cl^-$ ion as co-anions in source phase and make use of $S_2O_3^{2-}$ and $P_2O_7^{4-}$ ion as receiving phase, respectively. The transport rate of M(II) was highest when a maximum amount of the M(II) in the source phase was present as$Cd(SCN)_2$$(P[SCN^-]= 0.40M)$, $Hg(SCN)_2([SCN^-]=0.40M)$ and Pd(CN)$([CN^-]= 0.40M)$. The Cd(II) and Pb(II) over each competitive cations were well transprted with 0.3M-S2032- and 0.3M-P2O74-, respectively in the receiving phase. Results of this study indicate that two criteria must be met in order to have effective macrocycle-mediated transport in these emulsion system. First one must effective extraction of the $M^{n+}$ into the toluene systems. The effectiveness of this extraction is the greatest if locK for $M^{n+}$macrocycle interaction is large and if the macrocycle is very insoluble in the aqueous phase. Second, the ratio of the locK values (or Mn+-receiving phase ($S_2O_3^{2-}$- or $P_2O_7^{4-}$) to $M^{n+}$-macrocycle (($L_1$이나 $L_2$) interaction must be large enough to ensure quantitative stripping of Mn+(($Cd^{2+}$,$Pb^{2+}$)at the toluene receiving Phase interface. $L_1$(3.5-benzo-10,13,18,21-tetraoxa-1,7,diazabicyclo(8,5,5) eicosan) forms a stable ($Cd^{2+}$ and >,$Pb^{2+}$ complexes and $L_1$ is very insoluble in water and its $Cd^{2+}$ and >,$Pb^{2+}$ complex is considerably less stable than $Cd^{2+}$-(S2O3)22- and $Pd^{2+}-P_2O_7^{4-}$ complexes. On the other hand, the stability of the $Hg^{2+}$)+-$L_1$( complex exceed that of the $Hg^{2+}$- (S2O3)22- and Hg2+-P2O74-, and the distribution coefficient of $L_2$(5,8,15,18,23,26-hexaoxa-1,12- diazabicyclo-(10,8,8) octacosane) is much smaller than that of $L_1$. Therefore, the partitioning of Lr is favored by the aqueous receiving Phase, and little heavy metal ions transport is seen despite the large logK for $Hg^{2+}$+-$L_1$ and $Mn^+$($Cd^{2+}$+, $Pb^{2+}$+ and $Hg^{2+}$)-$L_2$ interactions. Key Words : macrocycles, transport, heavy metal, co-anion, source phase, receiveing, complex separation, interaction, destribution coefficient.

• The Journal of Korean Society for Radiation Therapy
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• v.27 no.2
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• pp.131-143
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• 2015

Purpose : By comparing a CT fusion plan based on MRI with a plan based on only MRI without CT, we intended to study usefulness of a plan based on only MRI. And furthermore, we intended to realize a realtime MR-IGRT by MRI image without CT scan during the course of simulation, treatment planning, and radiation treatment. Materials and Methods : BBB CT (Brilliance Big Bore CT, 16slice, Philips), Viewray MRIdian system (Viewray, USA) were used for CT & MR simulation and Treatment plan of 11 patients (1 Head and Neck, 5 Breast, 1 Lung, 3 Liver, 1 Prostate). When scanning for treatment, Free Breathing was enacted for Head&Neck, Breast, Prostate and Inhalation Breathing Holding for Lung and Liver. Considering the difference of size between CT and Viewray, the patient's position and devices were in the same condition. Using Viewray MRIdian system, two treatment plans were established. The one was CT fusion treatment plan based on MR image. Another was MR treatment plan including electron density that [ICRU 46] recommend for Lung, Air and Bone. For Head&Neck, Breast and Prostate, IMRT was established and for Lung and Liver, Gating treatment plan was established. PTV's Homogeneity Index(HI) and Conformity Index(CI) were use to estimate the treatment plan. And DVH and dose difference of each PTV and OAR were compared to estimate the treatment plan. Results : Between the two treatment plan, each difference of PTV's HI value is 0.089% (Head&Neck), 0.26% (Breast), 0.67% (Lung), 0.2% (Liver), 0.4% (Prostate) and in case of CI, 0.043% (Head&Neck), 0.84% (Breast), 0.68% (Lung), 0.46% (Liver), 0.3% (Prostate). As showed above, it is on Head&Neck that HI and CI's difference value is smallest. Each difference of average dose on PTV is 0.07 Gy (Head&Neck), 0.29 Gy (Breast), 0.18 Gy (Lung), 0.3 Gy (Liver), 0.18 Gy (Prostate). And by percentage, it is 0.06% (Head&Neck), 0.7% (Breast), 0.29% (Lung), 0.69% (Liver), 0.44% (Prostate). Likewise, All is under 1%. In Head&Neck, average dose difference of each OAR is 0.01~0.12 Gy, 0.04~0.06 Gy in Breast, 0.01~0.21 Gy in Lung, 0.06~0.27 Gy in Liver and 0.02~0.23 Gy in Prostate. Conclusion : PTV's HI, CI dose difference on the Treatment plan using MR image is under 1% and OAR's dose difference is maximum 0.89 Gy as heterogeneous tissue increases when comparing with that fused CT image. Besides, It characterizes excellent contrast in soft tissue. So, radiation therapy using only MR image without CT scan is useful in the part like Head&Neck, partial breast and prostate cancer which has a little difference of heterogeneity.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.183-203
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• 2018

News articles are the most suitable medium for examining the events occurring at home and abroad. Especially, as the development of information and communication technology has brought various kinds of online news media, the news about the events occurring in society has increased greatly. So automatically summarizing key events from massive amounts of news data will help users to look at many of the events at a glance. In addition, if we build and provide an event network based on the relevance of events, it will be able to greatly help the reader in understanding the current events. In this study, we propose a method for extracting event networks from large news text data. To this end, we first collected Korean political and social articles from March 2016 to March 2017, and integrated the synonyms by leaving only meaningful words through preprocessing using NPMI and Word2Vec. Latent Dirichlet allocation (LDA) topic modeling was used to calculate the subject distribution by date and to find the peak of the subject distribution and to detect the event. A total of 32 topics were extracted from the topic modeling, and the point of occurrence of the event was deduced by looking at the point at which each subject distribution surged. As a result, a total of 85 events were detected, but the final 16 events were filtered and presented using the Gaussian smoothing technique. We also calculated the relevance score between events detected to construct the event network. Using the cosine coefficient between the co-occurred events, we calculated the relevance between the events and connected the events to construct the event network. Finally, we set up the event network by setting each event to each vertex and the relevance score between events to the vertices connecting the vertices. The event network constructed in our methods helped us to sort out major events in the political and social fields in Korea that occurred in the last one year in chronological order and at the same time identify which events are related to certain events. Our approach differs from existing event detection methods in that LDA topic modeling makes it possible to easily analyze large amounts of data and to identify the relevance of events that were difficult to detect in existing event detection. We applied various text mining techniques and Word2vec technique in the text preprocessing to improve the accuracy of the extraction of proper nouns and synthetic nouns, which have been difficult in analyzing existing Korean texts, can be found. In this study, the detection and network configuration techniques of the event have the following advantages in practical application. First, LDA topic modeling, which is unsupervised learning, can easily analyze subject and topic words and distribution from huge amount of data. Also, by using the date information of the collected news articles, it is possible to express the distribution by topic in a time series. Second, we can find out the connection of events in the form of present and summarized form by calculating relevance score and constructing event network by using simultaneous occurrence of topics that are difficult to grasp in existing event detection. It can be seen from the fact that the inter-event relevance-based event network proposed in this study was actually constructed in order of occurrence time. It is also possible to identify what happened as a starting point for a series of events through the event network. The limitation of this study is that the characteristics of LDA topic modeling have different results according to the initial parameters and the number of subjects, and the subject and event name of the analysis result should be given by the subjective judgment of the researcher. Also, since each topic is assumed to be exclusive and independent, it does not take into account the relevance between themes. Subsequent studies need to calculate the relevance between events that are not covered in this study or those that belong to the same subject.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.59-83
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• 2018

With the increasing importance of sentiment analysis to grasp the needs of customers and the public, various types of deep learning models have been actively applied to English texts. In the sentiment analysis of English texts by deep learning, natural language sentences included in training and test datasets are usually converted into sequences of word vectors before being entered into the deep learning models. In this case, word vectors generally refer to vector representations of words obtained through splitting a sentence by space characters. There are several ways to derive word vectors, one of which is Word2Vec used for producing the 300 dimensional Google word vectors from about 100 billion words of Google News data. They have been widely used in the studies of sentiment analysis of reviews from various fields such as restaurants, movies, laptops, cameras, etc. Unlike English, morpheme plays an essential role in sentiment analysis and sentence structure analysis in Korean, which is a typical agglutinative language with developed postpositions and endings. A morpheme can be defined as the smallest meaningful unit of a language, and a word consists of one or more morphemes. For example, for a word '예쁘고', the morphemes are '예쁘(= adjective)' and '고(=connective ending)'. Reflecting the significance of Korean morphemes, it seems reasonable to adopt the morphemes as a basic unit in Korean sentiment analysis. Therefore, in this study, we use 'morpheme vector' as an input to a deep learning model rather than 'word vector' which is mainly used in English text. The morpheme vector refers to a vector representation for the morpheme and can be derived by applying an existent word vector derivation mechanism to the sentences divided into constituent morphemes. By the way, here come some questions as follows. What is the desirable range of POS(Part-Of-Speech) tags when deriving morpheme vectors for improving the classification accuracy of a deep learning model? Is it proper to apply a typical word vector model which primarily relies on the form of words to Korean with a high homonym ratio? Will the text preprocessing such as correcting spelling or spacing errors affect the classification accuracy, especially when drawing morpheme vectors from Korean product reviews with a lot of grammatical mistakes and variations? We seek to find empirical answers to these fundamental issues, which may be encountered first when applying various deep learning models to Korean texts. As a starting point, we summarized these issues as three central research questions as follows. First, which is better effective, to use morpheme vectors from grammatically correct texts of other domain than the analysis target, or to use morpheme vectors from considerably ungrammatical texts of the same domain, as the initial input of a deep learning model? Second, what is an appropriate morpheme vector derivation method for Korean regarding the range of POS tags, homonym, text preprocessing, minimum frequency? Third, can we get a satisfactory level of classification accuracy when applying deep learning to Korean sentiment analysis? As an approach to these research questions, we generate various types of morpheme vectors reflecting the research questions and then compare the classification accuracy through a non-static CNN(Convolutional Neural Network) model taking in the morpheme vectors. As for training and test datasets, Naver Shopping's 17,260 cosmetics product reviews are used. To derive morpheme vectors, we use data from the same domain as the target one and data from other domain; Naver shopping's about 2 million cosmetics product reviews and 520,000 Naver News data arguably corresponding to Google's News data. The six primary sets of morpheme vectors constructed in this study differ in terms of the following three criteria. First, they come from two types of data source; Naver news of high grammatical correctness and Naver shopping's cosmetics product reviews of low grammatical correctness. Second, they are distinguished in the degree of data preprocessing, namely, only splitting sentences or up to additional spelling and spacing corrections after sentence separation. Third, they vary concerning the form of input fed into a word vector model; whether the morphemes themselves are entered into a word vector model or with their POS tags attached. The morpheme vectors further vary depending on the consideration range of POS tags, the minimum frequency of morphemes included, and the random initialization range. All morpheme vectors are derived through CBOW(Continuous Bag-Of-Words) model with the context window 5 and the vector dimension 300. It seems that utilizing the same domain text even with a lower degree of grammatical correctness, performing spelling and spacing corrections as well as sentence splitting, and incorporating morphemes of any POS tags including incomprehensible category lead to the better classification accuracy. The POS tag attachment, which is devised for the high proportion of homonyms in Korean, and the minimum frequency standard for the morpheme to be included seem not to have any definite influence on the classification accuracy.

• Radiation Oncology Journal
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• v.26 no.2
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• pp.83-90
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• 2008

Purpose: For the first time, a nationwide survey of the Patterns of Care Study(PCS) for the various radiotherapy treatments of esophageal cancer was carried out in South Korea. In order to observe the different parameters, as well as offer a solid cooperative system, we compared the Korean results with those observed in the United States(US) and Japan. Materials and Methods: Two hundreds forty-six esophageal cancer patients from 21 institutions were enrolled in the South Korean study. The patients received radiation theraphy(RT) from 1998 to 1999. In order to compare these results with those from the United States, a published study by Suntharalingam, which included 414 patients[treated by Radiotherapy(RT)] from 59 institutions between 1996 and 1999 was chosen. In order to compare the South Korean with the Japanese data, we choose two different studies. The results published by Gomi were selected as the surgery group, in which 220 esophageal cancer patients were analyzed from 76 facilities. The patients underwent surgery and received RT with or without chemotherapy between 1998 and 2001. The non-surgery group originated from a study by Murakami, in which 385 patients were treated either by RT alone or RT with chemotherapy, but no surgery, between 1999 and 2001. Results: The median age of enrolled patients was highest in the Japanese non-surgery group(71 years old). The gender ratio was approximately 9:1(male:female) in both the Korean and Japanese studies, whereas females made up 23.1% of the study population in the US study. Adenocarcinoma outnumbered squamous cell carcinoma in the US study, whereas squamous cell carcinoma was more prevalent both the Korean and Japanese studies(Korea 96.3%, Japan 98%). An esophagogram, endoscopy, and chest CT scan were the main modalities of diagnostic evaluation used in all three countries. The US and Japan used the abdominal CT scan more frequently than the abdominal ultrasonography. Radiotherapy alone treatment was most rarely used in the US study(9.5%), compared to the Korean(23.2%) and Japanese(39%) studies. The combination of the three modalities(Surgery+RT+Chemotherapy) was performed least often in Korea(11.8%) compared to the Japanese(49.5%) and US(32.8%) studies. Chemotherapy(89%) and chemotherapy with concurrent chemoradiotherapy(97%) was most frequently used in the US study. Fluorouracil(5-FU) and Cisplatin were the most preferred drug treatments used in all three countries. The median radiation dose was 50.4 Gy in the US study, as compared to 55.8 Gy in the Korean study regardless of whether an operation was performed. However, in Japan, different median doses were delivered for the surgery(48 Gy) and non-surgery groups(60 Gy). Conclusion: Although some aspects of the evaluation of esophageal cancer and its various treatment modalities were heterogeneous among the three countries surveyed, we found no remarkable differences in the RT dose or technique, which includes the number of portals and energy beams.

• Radiation Oncology Journal
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• v.23 no.1
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• pp.22-31
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• 2005

Purpose : To conduct a nationwide survey on the principals in radiotherapy for rectal cancer, and produce a database of Korean Patterns of Care Study. Materials and Methods : We developed web-based Patterns of Care Study system and a national survey was conducted using random sampling based on power allocation methods. Eligible patients were who had postoperative radiotherapy for rectal cancer without gross residual tumor after surgical resection and without previous history of other cancer and radiotherapy to pelvis. Data of patients were Inputted to the web based PCS system by each investigators in 19 institutions. Results : Informations on 309 patients with rectal cancer who received radiotherapy between 1998 and 1999 were collected. Male to female ratio was 59 : 41, and the most common location of tumor was lower rectum ($46\%$). Preoperative CEA was checked in $79\%$ of cases and its value was higher than 6 ng/ml in $32\%$. Pathologic stage were I in $1.5\%$, II in $32\%$, III in $53\%$, and IV in $1.6\%$. Low anterior resection was the most common type of surgery and complete resection was peformed in $95\%$ of cases. Distal resection margin was less than 2 cm in $30\%$, and number of lymph node dissected was less than 12 in $31\%$. Chemotherapy was peformed in $91\%$ and most common regimen was 5-FU and leucovorine ($59\%$). The most common type of field arrangement used for the initial pelvic field was the four field box (Posterior-Right-Left) technique ($65.0\%$), and there was no AP-PA parallel opposing field used. Patient position was prone in $81.2\%$, and the boost field was used in $61.8\%$. To displace bowel outward, pressure modulating devices or bladder filling was used in $40.1\%$. Radiation dose was prescribed to isocenter in $45.3\%$ and to isodose line in 123 cases ($39.8\%$). Percent delivered dose over $90\%$ was achieved in $92.9\%$. Conclusion : We could find the Patterns of Care for the radiotherapy in Korean rectal cancer patients was similar to that of US national survey. The type of surgery and the regimen of chemotherapy were variable according to institutions and the variations of radiation dose and field arrangement were within acceptable range.

• Korean journal of food and cookery science
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• v.23 no.5
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• pp.589-600
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• 2007

Recently, with the rapid expansion of the franchise restaurants, ensuring food safety has become essential for restaurant growth. Consequently, the need for food safety training and related material is in increasing demand. In this study, we identified potentially hazardous risk factors for ensuring food safety in restaurants through a food safety monitoring tool, and developed training materials for restaurant employees based on the results. The surveyed restaurants, consisting of 6 Korean restaurants and 1 Japanese restaurant were located in Seoul. Their average check was 15,500 won, ranging from 9,000 to 23,000 won. The range of their total space was 297.5 to $1322.4m^2$, and the amount of kitchen space per total area ranged from 4.4 to 30 percent. The mean score for food safety management performance was 57 out of 100 points, with a range of 51 to 73 points. For risk factor analysis, the most frequently cited sanitary violations involved the handwashing methods/handwashing facilities supplies (7.5%), receiving activities (7.5%), checking and recording of frozen/refrigerated foods temperature (0%), holding foods off the floor (0%), washing of fruits and vegetables (42%), planning and supervising facility cleaning and maintaining programs of facilities (50%), pest control (13%), and toilet equipped/cleaned (13%). Base on these results, the main points that were addressed in the hygiene training of restaurant employees included 4 principles and 8 concepts. The four principles consisted of personal hygiene, prevention of food contamination, time/temperature control, and refrigerator storage. The eight concepts included: (1) personal hygiene and cleanliness with proper handwashing, (2) approved food source and receiving management (3) refrigerator and freezer control, (4) storage management, (5) labeling, (6) prevention of food contamination, (7) cooking and reheating control, and (8) cleaning, sanitation, and plumbing control. Finally, a hygiene training manual and poster leaflets were developed as a food safety training materials for restaurants employees.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.185-202
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• 2012

.Since the value of information has been realized in the information society, the usage and collection of information has become important. A facial expression that contains thousands of information as an artistic painting can be described in thousands of words. Followed by the idea, there has recently been a number of attempts to provide customers and companies with an intelligent service, which enables the perception of human emotions through one's facial expressions. For example, MIT Media Lab, the leading organization in this research area, has developed the human emotion prediction model, and has applied their studies to the commercial business. In the academic area, a number of the conventional methods such as Multiple Regression Analysis (MRA) or Artificial Neural Networks (ANN) have been applied to predict human emotion in prior studies. However, MRA is generally criticized because of its low prediction accuracy. This is inevitable since MRA can only explain the linear relationship between the dependent variables and the independent variable. To mitigate the limitations of MRA, some studies like Jung and Kim (2012) have used ANN as the alternative, and they reported that ANN generated more accurate prediction than the statistical methods like MRA. However, it has also been criticized due to over fitting and the difficulty of the network design (e.g. setting the number of the layers and the number of the nodes in the hidden layers). Under this background, we propose a novel model using Support Vector Regression (SVR) in order to increase the prediction accuracy. SVR is an extensive version of Support Vector Machine (SVM) designated to solve the regression problems. The model produced by SVR only depends on a subset of the training data, because the cost function for building the model ignores any training data that is close (within a threshold ${\varepsilon}$) to the model prediction. Using SVR, we tried to build a model that can measure the level of arousal and valence from the facial features. To validate the usefulness of the proposed model, we collected the data of facial reactions when providing appropriate visual stimulating contents, and extracted the features from the data. Next, the steps of the preprocessing were taken to choose statistically significant variables. In total, 297 cases were used for the experiment. As the comparative models, we also applied MRA and ANN to the same data set. For SVR, we adopted '${\varepsilon}$-insensitive loss function', and 'grid search' technique to find the optimal values of the parameters like C, d, ${\sigma}^2$, and ${\varepsilon}$. In the case of ANN, we adopted a standard three-layer backpropagation network, which has a single hidden layer. The learning rate and momentum rate of ANN were set to 10%, and we used sigmoid function as the transfer function of hidden and output nodes. We performed the experiments repeatedly by varying the number of nodes in the hidden layer to n/2, n, 3n/2, and 2n, where n is the number of the input variables. The stopping condition for ANN was set to 50,000 learning events. And, we used MAE (Mean Absolute Error) as the measure for performance comparison. From the experiment, we found that SVR achieved the highest prediction accuracy for the hold-out data set compared to MRA and ANN. Regardless of the target variables (the level of arousal, or the level of positive / negative valence), SVR showed the best performance for the hold-out data set. ANN also outperformed MRA, however, it showed the considerably lower prediction accuracy than SVR for both target variables. The findings of our research are expected to be useful to the researchers or practitioners who are willing to build the models for recognizing human emotions.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.39-53
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• 2013

The emergence of the internet technology and SNS has increased the information flow and has changed the way people to communicate from one-way to two-way communication. Users not only consume and share the information, they also can create and share it among their friends across the social network service. It also changes the Social Media behavior to become one of the most important communication tools which also includes Social TV. Social TV is a form which people can watch a TV program and at the same share any information or its content with friends through Social media. Social News is getting popular and also known as a Participatory Social Media. It creates influences on user interest through Internet to represent society issues and creates news credibility based on user's reputation. However, the conventional platforms in news services only focus on the news recommendation domain. Recent development in SNS has changed this landscape to allow user to share and disseminate the news. Conventional platform does not provide any special way for news to be share. Currently, Social News Service only allows user to access the entire news. Nonetheless, they cannot access partial of the contents which related to users interest. For example user only have interested to a partial of the news and share the content, it is still hard for them to do so. In worst cases users might understand the news in different context. To solve this, Social News Service must provide a method to provide additional information. For example, Yovisto known as an academic video searching service provided time dependent metadata from the video. User can search and watch partial of video content according to time dependent metadata. They also can share content with a friend in social media. Yovisto applies a method to divide or synchronize a video based whenever the slides presentation is changed to another page. However, we are not able to employs this method on news video since the news video is not incorporating with any power point slides presentation. Segmentation method is required to separate the news video and to creating time dependent metadata. In this work, In this paper, a time dependent metadata-based framework is proposed to segment news contents and to provide time dependent metadata so that user can use context information to communicate with their friends. The transcript of the news is divided by using the proposed story segmentation method. We provide a tag to represent the entire content of the news. And provide the sub tag to indicate the segmented news which includes the starting time of the news. The time dependent metadata helps user to track the news information. It also allows them to leave a comment on each segment of the news. User also may share the news based on time metadata as segmented news or as a whole. Therefore, it helps the user to understand the shared news. To demonstrate the performance, we evaluate the story segmentation accuracy and also the tag generation. For this purpose, we measured accuracy of the story segmentation through semantic similarity and compared to the benchmark algorithm. Experimental results show that the proposed method outperforms benchmark algorithms in terms of the accuracy of story segmentation. It is important to note that sub tag accuracy is the most important as a part of the proposed framework to share the specific news context with others. To extract a more accurate sub tags, we have created stop word list that is not related to the content of the news such as name of the anchor or reporter. And we applied to framework. We have analyzed the accuracy of tags and sub tags which represent the context of news. From the analysis, it seems that proposed framework is helpful to users for sharing their opinions with context information in Social media and Social news.

• Journal of agricultural medicine and community health
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• v.26 no.1
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• pp.19-35
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• 2001

This study was conducted to investigate the internal changes and the countermeasure for performance improvement by Separation of Prescribing and Dispensing Practice (SPDP) in Health Center. Data were collected from two sources: Performance report before and after SPDP of 25 Health Centers in Kyongsangbuk-do and 6 Health Centers in Daegu-City and self-administerd questionnaire survey of 221 officials at health center. The results of this study were summarized as follows: Twenty-four health centers(77.4%) of 31 health centers took convenience measures for medical treatment of citizens and convenience measures were getting map of pharmacy, improvement of health center interior, introduction of order communication system in order. After the SPDP in health centers, 19.4% of health centers increased doctors and 25.8% decreased pharmacists. 58.1% of health centers showed that number of medical treatments were decreased. 96.4%, 80.6% 80.6% 96.7% of health centers showed that number of prescriptions, total medical treatment expenses, amounts paid by the insureds and the expenses to purchase drugs, respectively, were decreased. More than fifty percent(54.2%) of health centers responded that the relative importance of health works increased compared to medical treatments after the SPDP, and number of patients decreased compared to those in before the SPDP. And there was a drastic reduction in number of prescriptions, total medical treatment expenses, amounts paid by insureds, the expenses to purchase drugs after the SPDP. Above fifty percent(57.6%) of officers at health center responded that the function of medical treatment should be reduced after the SPDP. Fields requested improvement in health centers were 'development of heath works contents'(62.4%), 'rearrangement of health center personnel'(51.6%), 'priority setting for health works'(48.4%), 'restructuring the organization'(36.2%), 'quality impro­vement for medical services'(32.1%), 'replaning the budgets'(23.1%) in order. And to better the image of health centers, health center officers replied that 'health information management'(60.7%), 'public relations for health center'(15.8%), 'kindness of health center officers'(15.3%) were necessary in order. Health center officers suggested that 'vaccination program', 'health promotion', 'maternal and children health', 'communicable disease management', 'community health planning' were relatively important works, in order, performed by health center after SPDP. In the future, medical services in health centers should be cut down with a momentum of the SPDP so that health centers might reestablish their functions and roles as public health organizations, but quality of medical services must be improved. Also health centers should pay attention to residents for improving health through 'vaccination program', 'health promotion', 'mother-children health', 'acute and chronic communicable disease management', 'community health planning', 'oral health', 'chronic degenerative disease management', etc. And there should be a differentiation of relative importance between health promotion services and medical treatment services by character of areas(metropolitan, city, county).