Pohang University of Science and Technology · Business, Management and Accounting
Professor Minseok Song's research lab specializes in data-driven decision support systems, health information technology, and intelligent systems for sustainable infrastructure. The lab focuses on leveraging advanced analytics, blockchain technology, and deep learning to address challenges in healthcare operations, hydrogen energy supply chains, and sports performance analysis. Key research directions include secure health information exchange, optimization of clean energy infrastructure, and the development of AI-powered tools for real-world applications.
Figures are computed from collected data and may differ slightly.
Accurate understanding of the factors associating with the LOS and progressive improvements in processing and monitoring may allow more efficient management of the LOS of inpatients.
Health information exchange (HIE) refers to the integrated management and secure sharing of health information among healthcare entities. HIE improves healthcare quality and streamline healthcare administrative work. These advantages have propelled health-care stakeholders to implement HIE. However, challenged by issues such as security, privacy, and costs, HIE is not widespread. Recent studies have suggested blockchain-based HIE for solving security and privacy issues. Unfortunately, existing b
This study presents a novel web-based decision support system (DSS) that optimizes the locations of hydrogen refueling stations (HRSs) and hydrogen supply chains (HSCs). The system is developed with a design science approach that identifies key design requirements and features through interviews and literature reviews. Based on the findings, a system architecture and data model were designed, incorporating scenario management, optimization model, visualization, and data management components. Th
The aim of this research was to analyze the player’s pass style with enhanced accuracy using the deep learning technique. We proposed Pass2vec, a passing style descriptor that can characterize each player’s passing style by combining detailed information on passes. Pass data was extracted from the ball event data from five European football leagues in the 2017–2018 season, which was divided into training and test set. The information on location, length, and direction of passes was combined usin
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