• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.4
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• pp.153-158
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• 2013

The purpose of this study is to investigate the performance characteristics of the counterflow exhaust heat recovery device for the applied gasoline engines. The EHRS device is installed behind the catalyst. This study investigates the engine warm-up characteristic, the exhaust noise characteristic, the back-pressure characteristic. The engine warm-up characteristics is (load 0%, load 10%, load 20%) in (idle, 1000rpm, 1500rpm, 2000rpm, 2500rpm) conditions by measuring the time it warmed up, coolant temperature ($25^{\circ}C{\sim}80^{\circ}C$) until the performance evaluation is performed. The wide open throttle and the coast down the exhaust noise and the back-pressure characteristic experiment repeated twice. The test conditions is 950rpm~6,050rpm proceed experiment repeated 3-5 times. Load 0% idle conditions except the results improved engine warm-up characteristics. The exhaust noise obtain similar results the BASE+EHRS W/O_FRT_MUFF with BASE and back-pressure to obtain similar results BASE+EHRS W/O_FRT_ MUFF with BASE+EHRS.

• Journal of Digital Convergence
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• v.16 no.1
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• pp.217-229
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• 2018

Electronic Health Record (EHR) systems are widely adopted worldwide in hospitals for generating and exchanging records of patient information. Recent developments are moving towards implementing interoperable EHR systems that enable information to be shared seamlessly across healthcare organizations. In this context, this paper explores the factors that cause medical information privacy concerns, identifies how people react to privacy invasion and what their perceptions are towards the acceptance of the EHR system. Interviews were conducted to draw a grounded theory on medical information privacy concerns in the use of EHRs. Medical information privacy concerns are caused by perceived sensitivity of medical information and the weaknesses in security technologies. Trust in medical professionals, medical institutions and technologies plays an important role in determining people's reaction to privacy invasion and their perceptions on the use of EHRs.

• Journal of Computing Science and Engineering
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• v.2 no.2
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• pp.162-179
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• 2008

We have developed a tool for annotation of electronic health record (EHR) data. Currently we are in the process of manually annotating a corpus of Norwegian general practitioners' EHRs with mainly linguistic information. The purpose of this project is to attain a linguistically annotated corpus of patient histories from general practice. This corpus will be put to future use in medical language processing and information extraction applications. The paper outlines some of our practical experiences from developing such a corpus and, in particular, the effects of semi-automated annotation. We have also done some preliminary experiments with part-of-speech tagging based on our corpus. The results indicated that relevant training data from the clinical domain gives better results for the tagging task in this domain than training the tagger on a corpus form a more general domain. We are planning to expand the corpus annotations with medical information at a later stage.

• Transactions of the Korean Society of Automotive Engineers
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• v.19 no.6
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• pp.53-60
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• 2011

The effect of exhaust heat recovery system can be evaluated by two well known method. First method is to measure the time duration from engine start under cold coolant temperature till coolant get warmed. By this methodology coolant warming duration can be index of warm-up effect. Second method is to analyze heat balance of the engine during warm-up phase under steady engine operation so that wasted energy by losses such as cooling and exhaust can be index of warm-up effect. This study focused on evaluation of warming-up effect by both methodology above mentioned using 2L SI engine under from idle to 2000rpm steady condition. Results, idle operation showed low heat recovery efficiency but under higher engine speed condition, remarkable heat recovery efficiency improvement was observed. In 2000rpm steady condition, warm-up duration of engine is decreased by exhaust heat recovery system.

• 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.

• International Journal of Advanced Culture Technology
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• v.7 no.2
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• pp.28-33
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• 2019

Electronic health records (EHRs) enable us to use and re-use electronic data for various multiple purposes, such as public reporting, quality improvement, and patient outcomes research. Current hospital-acquired pressure injury (HAPI) risk assessment instruments have not been specifically developed for intensive care unit (ICU) patients and showed false positive rates in this specific populations. Previous research studies report a number of risk factors; however, it is still not clear what factors influence ICU HAPI in this population. As part of a larger research study, we performed an exploratory analysis by using a large electronic health record data. The aims of this study were to compare characteristics of patients who developed HAPIs during their ICU stay with those who did not, and to determine whether the two groups were different in the aspects of length of ICU stay, discharge disposition, and discharge destinations. We conducted chi-square test and t-test for group comparison. Association was examined by using bivariate analyses. Pearson correlation coefficients were used to examine correlation between LOS and number of medications. Our findings suggest a number of consistent and potentially modifiable risk factors, such as sedation, feeding tubes, and the number of medications administered. The mortality of the HAPI group was significantly higher than the non-HAPI group in our data. Discharge disposition was significantly different between the groups. 67% of the HAPI group transferred to intermediate or long-term care hospitals whereas 57.7% of the non-HAPI group went home after discharge. Awareness of these risk factors can lead to clinical interventions that can be preventative in the ICU setting.

• Korean Journal of Artificial Intelligence
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• v.11 no.2
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• pp.39-44
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• 2023

The creativity of thesis is that the significance of cyber security challenges in blockchain. The variety of enterprises, including those in the medical market, are the targets of cyberattacks. Hospitals and clinics are only two examples of medical facilities that are easy targets for cybercriminals, along with IoT-based medical devices like pacemakers. Cyberattacks in the medical field not only put patients' lives in danger but also have the potential to expose private and sensitive information. Reviewing and looking at the present and historical flaws and vulnerabilities in the blockchain-based IoT and medical institutions' equipment is crucial as they are sensitive, relevant, and of a medical character. This study aims to investigate recent and current weaknesses in medical equipment, of blockchain-based IoT, and institutions. Medical security systems are becoming increasingly crucial in blockchain-based IoT medical devices and digital adoption more broadly. It is gaining importance as a standalone medical device. Currently the use of software in medical market is growing exponentially and many countries have already set guidelines for quality control. The achievements of the thesis are medical equipment of blockchain-based IoT no longer exist in a vacuum, thanks to technical improvements and the emergence of electronic health records (EHRs). Increased EHR use among providers, as well as the demand for integration and connection technologies to improve clinical workflow, patient care solutions, and overall hospital operations, will fuel significant growth in the blockchain-based IoT market for linked medical devices. The need for blockchain technology and IoT-based medical device to enhance their health IT infrastructure and design and development techniques will only get louder in the future. Blockchain technology will be essential in the future of cybersecurity, because blockchain technology can be significantly improved with the cybersecurity adoption of IoT devices, i.e., via remote monitoring, reducing waiting time for emergency rooms, track assets, etc. This paper sheds the light on the benefits of the blockchain-based IoT market.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.731-736
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• 2023

Recently, the medical field has been applying mandatory Electronic Medical Records (EMRs) and Electronic Health Records (EHRs) systems that computerize and manage medical records, and distributing them throughout the entire medical industry to utilize patients' past medical records for additional medical procedures. However, the conversations between medical professionals and patients that occur during general medical consultations and counseling sessions are not separately recorded or stored, so additional important patient information cannot be efficiently utilized. Therefore, we propose an electronic medical record system that uses speech recognition and natural language processing deep learning to store conversations between medical professionals and patients in text form, automatically extracts and summarizes important medical consultation information, and generates electronic medical records. The system acquires text information through the recognition process of medical professionals and patients' medical consultation content. The acquired text is then divided into multiple sentences, and the importance of multiple keywords included in the generated sentences is calculated. Based on the calculated importance, the system ranks multiple sentences and summarizes them to create the final electronic medical record data. The proposed system's performance is verified to be excellent through quantitative analysis.