• Annals of Hepato-Biliary-Pancreatic Surgery
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• v.28 no.3
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• pp.291-301
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• 2024

This systematic review and meta-analysis aimed to evaluate the impact of prospective payment systems (PPSs) on cholecystectomy. A comprehensive literature review was conducted, examining studies published until December 2023. The review process focused on identifying research across major databases that reported critical outcomes such as length of stay (LOS), mortality, complications, admissions, readmissions, and costs following PPS for cholecystectomy. The studies were specifically selected for their relevance to the impact of PPS or the transition from fee-for-service (FFS) to PPS. The study analyzed six papers, with three eligible for meta-analysis, to assess the impact of the shift from FFS to PPS in laparoscopic and open cholecystectomy procedures. Our findings indicated no significant changes in LOS and mortality rates following the transition from FFS to PPS. Complication rates varied and were influenced by the diagnosis-related group categorization and surgeon cost profiles under episode-based payment. There was a slight increase in admissions and readmissions, and mixed effects on hospital costs and financial margins, suggesting varied responses to PPS for cholecystectomy procedures. The impact of PPS on cholecystectomy is nuanced and varies across different aspects of healthcare delivery. Our findings indicate a need for adaptable, patient-centered PPS models that balance economic efficiency with high-quality patient care. The study emphasizes the importance of considering specific surgical procedures and patient demographics in healthcare payment reforms.

• Journal of Korea Society of Digital Industry and Information Management
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• v.12 no.3
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• pp.1-9
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• 2016

The current meaning of "Big Data" refers to all the techniques for value eduction and actionable analytics as well management tools. Particularly, with the advances of wireless sensor networks, they yield diverse patterns of digital records. The records are mostly semi-structured and unstructured data which are usually beyond of capabilities of the management tools. Such data are rapidly growing due to their complex data structures. The complex type effectively supports data exchangeability and heterogeneity and that is the main reason their volumes are getting bigger in the sensor networks. However, there are many errors and problems in applications because the managing solutions for the complex data model are rarely presented in current big data environments. To solve such problems and show our differentiation, we aim to provide the solution of actionable analytics and semantic reusability in the sensor web based streaming big data with new data structure, and to empower the competitiveness.

• Knowledge Management Research
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• v.14 no.5
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• pp.137-149
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• 2013

Although business Intelligence system is introduced to many companies over the past decade, The result of business benefits from BI investment are not so significant than expected. But still successful BI system can provide the ability to analyse business information in order to support and improve management decision making across a broad range of business activities. In recently, Business Analytics System(BA) is emerging as advanced alternative of outdated and inefficient BI System. This study is focus on constructing procedure of BA system in KB card company, which is major credit card company in South Korea. In practice there were just few works that mentioned well-designed environment of KMS system, and other contribution of this study is to make a platform which invoke revelation of collective intelligence in data analytic professional users group.

• Annual Conference on Human and Language Technology
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• 2012.10a
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• pp.215-218
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• 2012

본 논문에서는 한국어 대화 처리를 위한 통계기반 음성언어이해 시스템에 대해 기술한다. 음성언어이해시스템은 대화처리에서 음성 인식된 문장으로부터 사용자의 의도를 인식하여 의미표현으로 표현하는 기능을 담당한다. 한국어의 특성을 반영한 실용적인 음성언어이해 시스템을 위해서 강건성과 적용성, 확장성 등이 요구된다. 이를 위해 본 시스템은 음성언어의 특성상 구조분석을 하지 않고, 마이닝 기법을 이용하여 사용자 의도 표현을 생성하는 방식을 취하고 있다. 또한 한국어에서 나타나는 특징들에 대한 처리를 위해 자질 추가 및 점규화 처리 등을 수행하였다. 정보서비스용 대화처리 시스템을 대상으로 개발되고 있고, 차량 정보서비스용 학습 코퍼스를 대상으로 실험을 하여 문장단위 정확률로 약 89%의 성능을 보이고 있다.

• Asia Pacific Journal of Business Review
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• v.1 no.2
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• pp.63-88
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• 2017

With the advent of new frontiers in robotics, the spectrum of robot research area has widened in many fields and applications. Other than conventional robot research, many technologies such as smart devices, drones, healthcare robots, and soft robots are emerging as promising applications. Due to the research complexity of this topic, this research requires international collaboration and should be fertilized by R&D policies. This paper aims to propose a method to perform a cross-national analysis of robot research with unstructured data such as papers in the proceedings of an international conference. Text analytics are applied to extract research issues and applications in an automatic manner.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.4
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• pp.366-374
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• 2014

This study assessed and analysed a result of a product design experiment through Product Data Analytics (PDA), to find reasons for failure of some projects in the experiment. PDA is a computer-based data analysis that uses Product Data Management (PDM) databases as its operational databases. The study examines 20 product design projects in the experiment, which are prepared to follow same product development process by using an identical PDM system. The design result in the PDM database is assessed and analysed by On-Line Analytical Processing (OLAP) and data mining tools in PDA. The assesment and analysis reveals the lateness in creation of 3D CAD models as the main reason of the failure.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.261-266
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• 2021

The spread of cloud computing, digital computing, and the popular social media platforms have led to increased growth of data. That growth of data results in what is known as big data (BD), which seen as one of the most strategic resources. The analysis of these BD has allowed generating value from massive raw data that helps in making effective decisions and providing quality of service. With Vision 2030, Saudi Arabia seeks to invest in BD technologies, but many challenges and barriers have led to delays in adopting BD. This research paper aims to search in the state of Big Data Analytics (BDA) in Saudi higher education sector, identify the barriers by reviewing the literature, and then to apply the design-reality gap model to assess these barriers that prevent effective use of big data and highlights priority areas for action to accelerate the application of BD to comply with Vision 2030.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.1996-2011
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• 2021

To understand a trend is to explore the intricate process of how something or a particular situation is constantly changing or developing in a certain direction. This exploration is about observing and describing an unknown field of knowledge, not testing theories or models with a preconceived hypothesis. The purpose is to gain knowledge we did not expect and to recognize the associations among the elements that were suspected or not. This generally requires examining a massive amount of data to find information that could be transformed into meaningful knowledge. That is, looking through the lens of big-data analytics with an inductive reasoning approach will help expand our understanding of the complex nature of a trend. The current study explored the trend of well-being in South Korea using big-data analytic techniques to discover hidden search patterns, associative rules, and keyword signals. Thereafter, a theory was developed based on inductive reasoning - namely the hook, upward push, and downward pull to elucidate a holistic picture of how big-data implications alongside social phenomena may have influenced the well-being trend.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.193-197
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• 2021

Customers are increasingly attracted towards different e-commerce websites and applications for the purchase of products significantly. This is the reason the sellers are moving to different internet based services to sell their products online. The growth of customers in this sector has resulted in the use of big data analytics to understand customers' behavior in predicting the demand of items. It uses a complex process of examining large amount of data to uncover hidden patterns in the information. It is established on the basis of finding correlation between various parameters that are recorded, understanding purchase patterns and applying statistical measures on collected data. This paper is a document of the bottom-up strategy used to manage the selling price of a first-time product for maximizing profit while selling it online. It summarizes how existing customers' expectations can be used to increase the sale of product and attract the attention of the new customer for buying the new product.

• Journal of applied mathematics & informatics
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• v.40 no.3_4
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• pp.563-575
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• 2022

Many organizations seek statistical modelling facilitated by data analytics technologies for determining the prediction models associated with M&A (Merger and Acquisition). By combining these data analytics tool alongside with data collection approaches aids organizations towards M&A decision making, followed by achieving profitable insights as well. It promotes for better visibility, overall improvements and effective negotiation strategies for post-M&A integration. This paper explores on the impact of pre and post integration of M&A in a standard organizational setting via devising a suitable statistical model via employing techniques such as Naïve Bayes, K-nearest neighbour (KNN), and Decision Tree & Support Vector Machine (SVM).