• 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.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.