• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.21-41
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• 2019

Thanks to the rapid development of information technologies, the data available on Internet have grown rapidly. In this era of big data, many studies have attempted to offer insights and express the effects of data analysis. In the tourism and hospitality industry, many firms and studies in the era of big data have paid attention to online reviews on social media because of their large influence over customers. As tourism is an information-intensive industry, the effect of these information networks on social media platforms is more remarkable compared to any other types of media. However, there are some limitations to the improvements in service quality that can be made based on opinions on social media platforms. Users on social media platforms represent their opinions as text, images, and so on. Raw data sets from these reviews are unstructured. Moreover, these data sets are too big to extract new information and hidden knowledge by human competences. To use them for business intelligence and analytics applications, proper big data techniques like Natural Language Processing and data mining techniques are needed. This study suggests an analytical approach to directly yield insights from these reviews to improve the service quality of hotels. Our proposed approach consists of topic mining to extract topics contained in the reviews and the decision tree modeling to explain the relationship between topics and ratings. Topic mining refers to a method for finding a group of words from a collection of documents that represents a document. Among several topic mining methods, we adopted the Latent Dirichlet Allocation algorithm, which is considered as the most universal algorithm. However, LDA is not enough to find insights that can improve service quality because it cannot find the relationship between topics and ratings. To overcome this limitation, we also use the Classification and Regression Tree method, which is a kind of decision tree technique. Through the CART method, we can find what topics are related to positive or negative ratings of a hotel and visualize the results. Therefore, this study aims to investigate the representation of an analytical approach for the improvement of hotel service quality from unstructured review data sets. Through experiments for four hotels in Hong Kong, we can find the strengths and weaknesses of services for each hotel and suggest improvements to aid in customer satisfaction. Especially from positive reviews, we find what these hotels should maintain for service quality. For example, compared with the other hotels, a hotel has a good location and room condition which are extracted from positive reviews for it. In contrast, we also find what they should modify in their services from negative reviews. For example, a hotel should improve room condition related to soundproof. These results mean that our approach is useful in finding some insights for the service quality of hotels. That is, from the enormous size of review data, our approach can provide practical suggestions for hotel managers to improve their service quality. In the past, studies for improving service quality relied on surveys or interviews of customers. However, these methods are often costly and time consuming and the results may be biased by biased sampling or untrustworthy answers. The proposed approach directly obtains honest feedback from customers' online reviews and draws some insights through a type of big data analysis. So it will be a more useful tool to overcome the limitations of surveys or interviews. Moreover, our approach easily obtains the service quality information of other hotels or services in the tourism industry because it needs only open online reviews and ratings as input data. Furthermore, the performance of our approach will be better if other structured and unstructured data sources are added.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.141-154
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• 2019

Rapid growth of internet technology and social media is progressing. Data mining technology has evolved to enable unstructured document representations in a variety of applications. Sentiment analysis is an important technology that can distinguish poor or high-quality content through text data of products, and it has proliferated during text mining. Sentiment analysis mainly analyzes people's opinions in text data by assigning predefined data categories as positive and negative. This has been studied in various directions in terms of accuracy from simple rule-based to dictionary-based approaches using predefined labels. In fact, sentiment analysis is one of the most active researches in natural language processing and is widely studied in text mining. When real online reviews aren't available for others, it's not only easy to openly collect information, but it also affects your business. In marketing, real-world information from customers is gathered on websites, not surveys. Depending on whether the website's posts are positive or negative, the customer response is reflected in the sales and tries to identify the information. However, many reviews on a website are not always good, and difficult to identify. The earlier studies in this research area used the reviews data of the Amazon.com shopping mal, but the research data used in the recent studies uses the data for stock market trends, blogs, news articles, weather forecasts, IMDB, and facebook etc. However, the lack of accuracy is recognized because sentiment calculations are changed according to the subject, paragraph, sentiment lexicon direction, and sentence strength. This study aims to classify the polarity analysis of sentiment analysis into positive and negative categories and increase the prediction accuracy of the polarity analysis using the pretrained IMDB review data set. First, the text classification algorithm related to sentiment analysis adopts the popular machine learning algorithms such as NB (naive bayes), SVM (support vector machines), XGboost, RF (random forests), and Gradient Boost as comparative models. Second, deep learning has demonstrated discriminative features that can extract complex features of data. Representative algorithms are CNN (convolution neural networks), RNN (recurrent neural networks), LSTM (long-short term memory). CNN can be used similarly to BoW when processing a sentence in vector format, but does not consider sequential data attributes. RNN can handle well in order because it takes into account the time information of the data, but there is a long-term dependency on memory. To solve the problem of long-term dependence, LSTM is used. For the comparison, CNN and LSTM were chosen as simple deep learning models. In addition to classical machine learning algorithms, CNN, LSTM, and the integrated models were analyzed. Although there are many parameters for the algorithms, we examined the relationship between numerical value and precision to find the optimal combination. And, we tried to figure out how the models work well for sentiment analysis and how these models work. This study proposes integrated CNN and LSTM algorithms to extract the positive and negative features of text analysis. The reasons for mixing these two algorithms are as follows. CNN can extract features for the classification automatically by applying convolution layer and massively parallel processing. LSTM is not capable of highly parallel processing. Like faucets, the LSTM has input, output, and forget gates that can be moved and controlled at a desired time. These gates have the advantage of placing memory blocks on hidden nodes. The memory block of the LSTM may not store all the data, but it can solve the CNN's long-term dependency problem. Furthermore, when LSTM is used in CNN's pooling layer, it has an end-to-end structure, so that spatial and temporal features can be designed simultaneously. In combination with CNN-LSTM, 90.33% accuracy was measured. This is slower than CNN, but faster than LSTM. The presented model was more accurate than other models. In addition, each word embedding layer can be improved when training the kernel step by step. CNN-LSTM can improve the weakness of each model, and there is an advantage of improving the learning by layer using the end-to-end structure of LSTM. Based on these reasons, this study tries to enhance the classification accuracy of movie reviews using the integrated CNN-LSTM model.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.

• Journal of Korean Home Economics Education Association
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• v.26 no.4
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• pp.129-146
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• 2014

This study is a fundamental research in the field of home economics education to enforce vocational competencies. It was carried out in the purpose of examining the recent economical and social environmental changes and its management system related to the vocational training in the field of home economics education. It seeks change in direction in relation to the National Competency Standard(NCS) based on revisions in the educational system. The method of study was mostly through reference and data analysis, professional advisory and public hearing. The main research results are as follows. First, the main environmental change factors in relation to vocational training have been integrated to the changes in; population structure, gender related economic activities, generation composition, communications technology, and innovation of living technique. These change factors are forecasting innovations in related industries, lifestyle changes, demand for manpower and changes in capabilities required for each specific profession. Second, according to the analysis of current home economics education training, vocational home educations high school accounts for 9.4% of the total number of specialized high schools, where 8 standard departments are specialized in and characterized into 137 different department names. Despite differences among departments, overall employment rate of graduates were measured 44.7%, which rates above the entrance rate of 41.9%. These numbers show great change since 2010(overall employment rate 16.9%, entrance rate 75.2%), a meaningful outcome resulting from changes in policy from the previous employment-centered education system. Third, NCS based on high school vocational home economics education system revision and investigations in change of direction in vocational home economics, this study attempts to provide background for revision from the development of NCS. It also provides proposals for restructuring division of current classification and departments of home economics education, and propositions for further future research.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.71-89
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• 2016

Sentiment analysis is used for identifying emotions or sentiments embedded in the user generated data such as customer reviews from blogs, social network services, and so on. Various research fields such as computer science and business management can take advantage of this feature to analyze customer-generated opinions. In previous studies, the star rating of a review is regarded as the same as sentiment embedded in the text. However, it does not always correspond to the sentiment polarity. Due to this supposition, previous studies have some limitations in their accuracy. To solve this issue, the present study uses a supervised sentiment classification model to measure a more accurate sentiment polarity. This study aims to propose an advanced sentiment classifier and to discover the correlation between movie reviews and box-office success. The advanced sentiment classifier is based on two supervised machine learning techniques, the Support Vector Machines (SVM) and Feedforward Neural Network (FNN). The sentiment scores of the movie reviews are measured by the sentiment classifier and are analyzed by statistical correlations between movie reviews and box-office success. Movie reviews are collected along with a star-rate. The dataset used in this study consists of 1,258,538 reviews from 175 films gathered from Naver Movie website (movie.naver.com). The results show that the proposed sentiment classifier outperforms Naive Bayes (NB) classifier as its accuracy is about 6% higher than NB. Furthermore, the results indicate that there are positive correlations between the star-rate and the number of audiences, which can be regarded as the box-office success of a movie. The study also shows that there is the mild, positive correlation between the sentiment scores estimated by the classifier and the number of audiences. To verify the applicability of the sentiment scores, an independent sample t-test was conducted. For this, the movies were divided into two groups using the average of sentiment scores. The two groups are significantly different in terms of the star-rated scores.

• Journal of Preventive Medicine and Public Health
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• v.11 no.1
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• pp.123-127
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• 1978

The major objective of this research is to identify those hospital characteristics that best explain cost variation among hospitals and to formulate linear models that can predict hospital costs. Specific emphasis is placed on hospital output, that is, the identification of diagnosis related patient groups (DRGs) which are medically meaningful and demonstrate similar patterns of hospital resource consumption. A casemix index is developed based on the DRGs identified. Considering the common problems encountered in previous hospital cost research, the following study requirements are estab-lished for fulfilling the objectives of this research: 1. Selection of hospitals that exercise similar medical and fiscal practices. 2. Identification of an appropriate data collection mechanism in which demographic and medical characteristics of individual patients as well as accurate and comparable cost information can be derived. 3. Development of a patient classification system in which all the patients treated in hospitals are able to be split into mutually exclusive categories with consistent and stable patterns of resource consumption. 4. Development of a cost finding mechanism through which patient groups' costs can be made comparable across hospitals. A data set of Medicare patients prepared by the Social Security Administration was selected for the study analysis. The data set contained 27,229 record abstracts of Medicare patients discharged from all but one short-term general hospital in Connecticut during the period from January 1, 1971, to December 31, 1972. Each record abstract contained demographic and diagnostic information, as well as charges for specific medical services received. The 'AUT-OGRP System' was used to generate 198 DRGs in which the entire range of Medicare patients were split into mutually exclusive categories, each of which shows a consistent and stable pattern of resource consumption. The 'Departmental Method' was used to generate cost information for the groups of Medicare patients that would be comparable across hospitals. To fulfill the study objectives, an extensive analysis was conducted in the following areas: 1. Analysis of DRGs: in which the level of resource use of each DRG was determined, the length of stay or death rate of each DRG in relation to resource use was characterized, and underlying patterns of the relationships among DRG costs were explained. 2. Exploration of resource use profiles of hospitals; in which the magnitude of differences in the resource uses or death rates incurred in the treatment of Medicare patients among the study hospitals was explored. 3. Casemix analysis; in which four types of casemix-related indices were generated, and the significance of these indices in the explanation of hospital costs was examined. 4. Formulation of linear models to predict hospital costs of Medicare patients; in which nine independent variables (i. e., casemix index, hospital size, complexity of service, teaching activity, location, casemix-adjusted death. rate index, occupancy rate, and casemix-adjusted length of stay index) were used for determining factors in hospital costs. Results from the study analysis indicated that: 1. The system of 198 DRGs for Medicare patient classification was demonstrated not only as a strong tool for determining the pattern of hospital resource utilization of Medicare patients, but also for categorizing patients by their severity of illness. 2. The wei틴fed mean total case cost (TOTC) of the study hospitals for Medicare patients during the study years was $11,27.02 with a standard deviation of $117.20. The hospital with the highest average TOTC ($1538.15) was 2.08 times more expensive than the hospital with the lowest average TOTC ($743.45). The weighted mean per diem total cost (DTOC) of the study hospitals for Medicare patients during the sutdy years was $107.98 with a standard deviation of $15.18. The hospital with the highest average DTOC ($147.23) was 1.87 times more expensive than the hospital with the lowest average DTOC ($78.49). 3. The linear models for each of the six types of hospital costs were formulated using the casemix index and the eight other hospital variables as the determinants. These models explained variance to the extent of 68.7 percent of total case cost (TOTC), 63.5 percent of room and board cost (RMC), 66.2 percent of total ancillary service cost (TANC), 66.3 percent of per diem total cost (DTOC), 56.9 percent of per diem room and board cost (DRMC), and 65.5 percent of per diem ancillary service cost (DTANC). The casemix index alone explained approximately one half of interhospital cost variation: 59.1 percent for TOTC and 44.3 percent for DTOC. Thsee results demonstrate that the casemix index is the most importand determinant of interhospital cost variation Future research and policy implications in regard to the results of this study is envisioned in the following three areas: 1. Utilization of casemix related indices in the Medicare data systems. 2. Refinement of data for hospital cost evaluation. 3. Development of a system for reimbursement and cost control in hospitals.

• Journal of the Korean Society of Costume
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• v.2
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• pp.73-88
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• 1978

This article is an attempt to explain, at least in part, the contemporary Korean women's consciousness of Western Dreasses. As time changes, the role of clothing undergoes varisous transitions, while values and ways of life are constantly in change. It is, therefore, proper and appropriate to recognize as among the major aspects of social psychology such phenomenon as interests, understanding of clothing, the choice of a dress, and attitudes toward clothing, etc. The purpose of this study is to discover problems concerning and their clothing and their solutions, by means of a surveying approach. The method of research used is based upon questionares distributed to parents of first-year pupils in elementary schools and to female clerks working in offices, covering the period from August through October, 1976. The number of the questionares distrubuted totalled 600, and 526 were returned to the research to be utilized for analysis. The contents of the survey included such things as values concerning clothing, kinds of clothing and their practical use, the selection of clothing and the method of purchase, fashions, etc. The classification of aquisition are self-made clothing, clothing made to order and ready-made materials. It is composed of 25 items, including affirmative reasons as well as negative ones. The processing of the material returned was made by using the computer, and based upon classifications such as ages, monthly income, occupations; thus diagraming the result in percentages. The conclusion made and the improvements proposed are as follows: 1. The values of clothing were placed on the expression of the wearer's personality (32.7) and on eauty(28. 6%). The lower age group places is stress upon the expression of personality, while the higher age group stresses beauty. About 50% of wearers are contented with their clothing, their clothing, the rest of whom them indicating their dissatisfaction with what they wear. As to designs at the time of selection, about 46% indicated their preference of personal expression, 31.8% on usefulness. In selecting material, practicality is emphasized; in selecting patterns, single color is preferred. In short, personal expression and esthetic values are primary, with consideration of practicality in mind. 2. The classification of clothing according to their uses indicates the highest numbers in normal wear (home wears) and clothings to be worn outside home. As to evening dresses, (party dress) only one or two articles were checked by many, and no such article was clamed to be possessed by most. The highest ratio of wearing was shown in the case of home wear (47.3%) and clothing to be worn outside the home, which is 55.8%. The budget for one article of clothing was greatest in the case of home wear, and clothing worn outside the home. Many used both kinds of articles for the same purpose. It is desirable, therefore, that the kinds of clothing should be varied according to the purpose for which they are worn, and that clothing appropriate for that purpose should be worn. 3. The motivation for purchasing clothing was highly chosen in the item of seasonal change, which was 55.7%; Clothing deliberately made was indicated by 45.2%. In the mothods of purchasing clothing, clothing made to order and ready-made was indicated by 44.4%, which is the highest; Clothing made to order was 25.4%, and self-sewing was 1.1%, which is the lowest. (1) In the case of self-sewing, "I like it but it is very hard," was checked by 43.6%; "It is so difficult that I cannot wear such clothing" was checked by 13.3%. From these, we can conclude that the questionees are willing to make clothing by themselves, but techniques involved in sewing and at her problems involved in the skill are complicated but when those problems are eliminated there is a possibility for practice. The response checked by questionees concerning the self-sewing was, "It's economical", which is a clear indication that many questionees are positive for self-sewing. It is generally believed that ready-made clothing is cheaper, but it is not necessarily so. In consideration of the quality of clothing, self-sewing is a necessity, and it is desirable that it should be encouraged. (3) Problems involved in ready-made clothing, such as designs, skills, size (fitting) should be eliminated. When these problems are scientifically gotten rid of, it is possible that affirmative returns will be expected. Affirmative responses such as "Ready-made clothing is economical," "You can select there on the spot," are good signs that many women expect to wear ready-made clothing. It is in this sense that the prospect for ready-made clothing is brighter when much development for ready-made clothing is on the way. 4. Much concern for fashion are checked in such item of questions as "Fashionable clothing in the show window," "Clothes worn by women." The first item was checked by 50.1 %, and the second was checked by 48.6%. The reason for following fashion is "Because many people wear them," which was indicated by 30.4%. The reason for not following fashion is "It is too expensive," which was checked by 29.6%. The 26.2% of the answers indicated that "Fashionable clothing is devoid of personality," The influences of fashion over the development of fashion over the development of clothing are two-fold: Esthetic and active. It is not to be deniable that people follow fashion more or less. 1978.9>

• Journal of Korean society of Dental Hygiene
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• v.10 no.2
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• pp.375-392
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• 2010

Objectives : Dental hygienist's work satisfaction and stress affect the overall quality of work life(QWL). Therefore, this research is intended to suggest fundamental data to improve QWL by finding out characteristics of each work satisfaction and stress element. To this end, a total of 327 dental hygienists working at general hospitals, university hospitals, dental hospitals and dental clinics across Seoul, Gyeonggi and Incheon were surveyed. Results of survey are as follows. Methods : The collected data were analyzed by using an SPSS 12.0 statistical program, obtaining the following results. The collected data conducted a questionnaire survey for 327 dental hygienists who work at the hospitals, university hospitals, dental hospitals, and dental clinics located at Seoul, Gyeonggi-do, and Incheon district from January until March, 2009, and drew the conclusions as follows. Result : 1. Demographic characteristics, income from 1.5 to 1.99 million were the whole lot, more than 2 million to less than 1.5 million was similar. Marital status Married Unmarried higher than the atheist religion, Christianity, Catholicism, Buddhism, and other, respectively. Classification by level of education in the college graduate, university graduate, graduate diploma, respectively. 2. Are working in a job-related characteristics of dentistry, dental hospital, general and university hospital, respectively. The making in position, Mount, contractor, responsible, senior, was an intern in the order. The five-day workweek whether working at night and is not going to care whether the conduct was similar. Classification of working hours and 8 hours, 8 hours, 8 hours or less orderly, and total of less than 1-3 years of clinical experience, 5 years, less than one year, less than 3-5 years, respectively. 3. There comes out a significant difference according to age, income, position, gross clinical experience, and whether to put night shift into practice in job stability in terms of the quality subsequent to general characteristics(p<.05). 4. There comes out a significant difference according to marital status, one's place of work, position, whether to put a five-day workweek into practice in work environment and benefits package in terms of the quality subsequent to general characteristics (p<.05). 5. There comes out a significant difference according to age, marital status, income, position, and gross clinical experience in education & training and benefits packages in terms of the quality subsequent to general characteristics(p<.05). 6. There comes out a significant difference according to whether to put night medical treatment into practice in social usefulness in terms of the quality subsequent to general characteristics(p<.05). 7. There comes out a significant difference according to marital status, income, one's place of work, gross clinical experience, work hours, and whether to put a five-day workweek into practice in leisure activity in terms of the quality subsequent to general characteristics(p<.05). 8. There comes out a significant difference according to income, one's place of work, and position in wage level in terms of the quality subsequent to general characteristics(p<.05). 9. There was no significant difference in all items related to human relations and free communication in terms of the quality subsequent to general characteristics(p>.05). Conclusions : It is necessary to analyze factors related to work satisfaction and stress in order to improve dental hygienist's quality of work life. Hospitals must support them systematically and institutionally and related organizations must conduct practical research.

• Journal of the Korean Institute of Landscape Architecture
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• v.40 no.5
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• pp.8-18
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• 2012

Urban nature plays an important role not only in the improvement of the physical environment but also from the perspective of psychological and social function. In particular, schoolyards as well as the green spaces near school surroundings function as a primary space for urban children to experience nature in Korea, as they spend most of their time at school. In this study, the status of urban green spaces near school surroundings was examined. For the analysis, 185 elementary schools in Seoul were selected and the green spaces within a radius of 300m(defined as 'school zone' in this study) were analyzed using the Rapid Eye multispectral satellite image data. The mean green space ratio of school zone accounts to about 21% with a high variation from 74% to 0.7% and more than half of the school zone have a green space ratio of less than 20%. Schools with a high green space ratio in their school zone are mostly located near urban forests, so forest areas particularly contribute to increase the green space ratio. Furthermore, forest vegetation shows relatively higher vitality than other green spaces located in urbanized areas. In contrast, schools with a low green space ratio in their school zone are mostly situated in high-density residential areas and the green spaces show relatively low vegetation vitality. Except for the urban forest, the majority of urban green spaces in urbanized areas are landscape green facilities in apartment districts. The other types of urban open spaces such as environmentally shaped schoolyards or street parks account only for a very small proportion of school surroundings. Therefore, it is needed to establish countermeasures in the context of urban planning; e.g. to promote the school forest projects preferentially by selecting schools with a extremely low green space ratio in their school zone, to foster roof greening in near surroundings, and to connect schoolyards organically with nearby apartment landscape green facilities as an easily accessible urban open space.

• Journal of Hospice and Palliative Care
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• v.21 no.1
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• pp.23-32
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• 2018

Purpose: Little is known regarding the extent to which dying patients with chronic obstructive pulmonary disease (COPD) receive life-sustaining procedures and palliative care in U.S. hospitals. We examine hospital cost trends and the impact of palliative care utilization on the use of life-sustaining procedures in this population. Methods: Retrospective nationwide cohort analysis was performed using National Inpatient Sample (NIS) data from 2005 and 2014. We examined the receipt of both palliative care and intensive medical procedures, defined as systemic procedures, pulmonary procedures, or surgeries using the International Classification of Diseases, 9th revision (ICD-9-CM). Results: We used compound annual growth rates (CAGR) to determine temporal trends and multilevel multivariate regressions to identify factors associated with hospital cost. Among 77,394,755 hospitalizations, 79,314 patients were examined. The CAGR of hospital cost was 5.83% (P<0.001). The CAGRs of systemic procedures and palliative care were 5.98% and 19.89% respectively (each P<0.001). Systemic procedures, pulmonary procedures, and surgeries were associated with increased hospital cost by 59.04%, 72.00%, 55.26%, respectively (each P<0.001). Palliative care was associated with decreased hospital cost by 28.71% (P<0.001). Conclusion: The volume of systemic procedures is the biggest driver of cost increase although there is a cost-saving effect from greater palliative care utilization.