• The Korean Journal of Archival Studies
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• no.79
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• pp.305-341
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• 2024

This study explored the possibilities of a new approach to developing the provenance concept to electronic records in the data-driven digital environments by reviewing and adopting data provenance concepts and models. It then conducted basic literature review to develop a ground for a model representing the provenance of data-driven electronic records. In particular, it proposed to embrace to the concepts of retrospective and prospective provenance, and to develop a different model for representing provenance from records management metadata. If the model can be developed that can represent provenance independently while maintaining a dynamic relationship with records, it can be ensure the fluidity of records and even support to secure the record's attributes and play the roles of provenance. Eventually, it proposed the direction to develop the provenance model which can support the fixity of records, the reproducibility of activities, and the trustworthiness of representations. It is expected to be a fit provenance model in the data-driven digital environment.

• The Korean Journal of Archival Studies
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• no.80
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• pp.5-41
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• 2024

This study was conducted to develop a provenance representation model for data-type electronic records. It supports the distinction between provenance and context for the creation and management of data-type electronic records. To express both, it aims to design an extensible provenance model. For this purpose, W3C PROV is utilized as a basic model, with P-Plan and ProvONE for designing prospective provenance area. Afterward, the provenance model was extended by mapping the record management requirements. The provenance model proposed in this study is designed to represent and connect both retrospective and prospective provenance of data-type electronic records. Based on this study, it is expected to discussing the concept of provenance in the records management and archival studies area and to extending the model in the future.

• Smart Media Journal
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• v.11 no.11
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• pp.63-74
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• 2022

Nowadays, lithium-ion battery has become more popular around the world. Knowing when batteries reach their end of life (EOL) is crucial. Accurately predicting the remaining useful life (RUL) of lithium-ion batteries is needed for battery health management systems and to avoid unexpected accidents. It gives information about the battery status and when we should replace the battery. With the rapid growth of machine learning and deep learning, data-driven approaches are proposed to address this problem. Extracting aging information from battery charge/discharge records, including voltage, current, and temperature, can determine the battery state and predict battery RUL. In this work, we first outlined the charging and discharging processes of lithium-ion batteries. We then summarize the proposed techniques and achievements in all published data-driven RUL prediction studies. From that, we give a discussion about the accomplishments and remaining works with the corresponding challenges in order to provide a direction for further research in this area.

• Journal of Korean Academy of Nursing
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• v.49 no.5
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• pp.575-585
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• 2019

Purpose: The purpose of this study was to develop predictive models for pressure ulcer incidence using electronic health record (EHR) data and to compare their predictive validity performance indicators with that of the Braden Scale used in the study hospital. Methods: A retrospective case-control study was conducted in a tertiary teaching hospital in Korea. Data of 202 pressure ulcer patients and 14,705 non-pressure ulcer patients admitted between January 2015 and May 2016 were extracted from the EHRs. Three predictive models for pressure ulcer incidence were developed using logistic regression, Cox proportional hazards regression, and decision tree modeling. The predictive validity performance indicators of the three models were compared with those of the Braden Scale. Results: The logistic regression model was most efficient with a high area under the receiver operating characteristics curve (AUC) estimate of 0.97, followed by the decision tree model (AUC 0.95), Cox proportional hazards regression model (AUC 0.95), and the Braden Scale (AUC 0.82). Decreased mobility was the most significant factor in the logistic regression and Cox proportional hazards models, and the endotracheal tube was the most important factor in the decision tree model. Conclusion: Predictive validity performance indicators of the Braden Scale were lower than those of the logistic regression, Cox proportional hazards regression, and decision tree models. The models developed in this study can be used to develop a clinical decision support system that automatically assesses risk for pressure ulcers to aid nurses.

• The Korean Journal of Archival Studies
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• no.79
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• pp.5-32
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• 2024

The Korean government ranked 3rd among 193 UN member countries in the UN's 2022 e-Government Development Index. Korea, which has consistently been evaluated as a top country, can clearly be said to be a leading country in the world of e-government. The lubricant of e-government is data. Data itself is neither information nor a record, but it is a source of information and records and a resource of knowledge. Since administrative actions through electronic systems have become widespread, the production and technology of data-based records have naturally expanded and evolved. Technology may seem value-neutral, but in fact, technology itself reflects a specific worldview. The digital order of new technologies, armed with hyper-connectivity and super-intelligence, not only has a profound influence on traditional power structures, but also has an a similar influence on existing information and knowledge transmission media. Moreover, new technologies and media, including data-based generative artificial intelligence, are by far the hot topic. It can be seen that the all-round growth and spread of digital technology has led to the augmentation of human capabilities and the outsourcing of thinking. This also involves a variety of problems, ranging from deep fakes and other fake images, auto profiling, AI lies hallucination that creates them as if they were real, and copyright infringement of machine learning data. Moreover, radical connectivity capabilities enable the instantaneous sharing of vast amounts of data and rely on the technological unconscious to generate actions without awareness. Another irony of the digital world and online network, which is based on immaterial distribution and logical existence, is that access and contact can only be made through physical tools. Digital information is a logical object, but digital resources cannot be read or utilized without some type of device to relay it. In that respect, machines in today's technological society have gone beyond the level of simple assistance, and there are points at which it is difficult to say that the entry of machines into human society is a natural change pattern due to advanced technological development. This is because perspectives on machines will change over time. Important is the social and cultural implications of changes in the way records are produced as a result of communication and actions through machines. Even in the archive field, what problems will a data-based archive society face due to technological changes toward a hyper-intelligence and hyper-connected society, and who will prove the continuous activity of records and data and what will be the main drivers of media change? It is time to research whether this will happen. This study began with the need to recognize that archives are not only records that are the result of actions, but also data as strategic assets. Through this, author considered how to expand traditional boundaries and achieves reterritorialization in a data-driven society.

• Journal of Trauma and Injury
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• v.25 no.4
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• pp.188-195
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• 2012

Purpose: An increase in the demand for specialized Trauma Centers led to a government-driven campaign, that began in 2009. Our hospital was selected as one of the Trauma Centers, and we reviewed data on trauma patients in order to correlate the mortality at a regional Trauma Center with its contributing factors, such as the severity of the injury, the means of arrival, and the time duration before arrival at our center. Methods: Data on the patients who visited our Trauma Center from January 2010 to November 2011 were retrospectively reviewed using electronic medical records. The patients who had revised trauma scores (RTSs) less than 7 or injury severity scores (ISSs) greater than 15 were included. The patients were categorized as survivors and non-survivors, and the means of arrival as transferred or visited directly. Time durations before arrival of less than one hour were also taken intoconsideration. Results: Two hundred(200) patients were enrolled, and the mortality rate was 36.5%. The most common cause of the accident was an automobile accident, and the most common cause of death was brain injury. The RTSs and the ISSs were significantly different in the non-survivor and the survivor groups. The mortality rate of the patients who were transferred was not statistically different from that of patients who visited directly. However, a time duration before arrival of less than one hour was statistically meaningful. Conclusion: The prognosis of the trauma patients were correlated with the severity of the trauma as can be expected, but the time between the incidence of accident and the arrival at hospital and whether the presence of transfer to trauma center were not statistically significant to the prognosis.