• Neonatal Medicine
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• v.17 no.2
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• pp.193-200
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• 2010

Purpose: Feeding desaturation is a common problem among preterm infants which can result in prolonged hospital stays, longterm feeding difficulties and growth delay. The purpose of this study was to identify the characteristics of premature infants with feeding desaturation and to examine the effect of orocutaneous stimulation on oral feeding. Methods: During the first phase of this study, 125 extremely low birth weight infants were reviewed retrospectively. Characteristics between infants with feeding desaturation (n=34) and those without feeding desaturation (n=91) were examined. During the second phase, 29 infants recruited from March, 2009 to May, 2010 were subjected to orocutaneous stimulation. The results of orocutaneous stimulation were compared to a control group (n=81). Results: The first phase of the study revealed that extremely low birth weight infants with feeding desaturation were significantly lower in gestational ages at birth, and had lower 5 minute apgar scores, more gastroesophageal refluxes and bronchopulmonary dysplasia. Infants without feeding desaturation reached full enteral feeding significantly earlier and showed shorter duration of hospital stay. At the second phase, infants in the intervention group showed shorter days to achieve initiation of bottle feeding, shorter days in achievement of full bottle feeding, last episodes of feeding desaturation and length of hospital stay compared to the control group of similar characteristics. Conclusion: Orocutaneous stimulation among extremely low birth weight infants results in earlier achievement of full bottle feedings without episodes of feeding desaturation hence shortens the length of hospital stay.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.16
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• pp.7117-7121
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• 2015

Background: The effects of home nursing intervention on the quality of life in patients with nasopharyngeal carcinoma (NPC) after radiotherapy and chemotherapy are unclear. According to the characteristics of nursing home patients with nasopharyngeal carcinoma, we should continuously improve the nursing plan and improve the quality of life of patients at home. Materials and Methods: We selected 180 patients at home with NPC after radiotherapy and chemotherapy. The patients were randomly divided into experimental and control groups (90 patients each). The experimental group featured intervention with an NPC home nursing plan, while the control group was given routine discharge and outpatient review. Nursing intervention for patients was mainly achieved by regular telephone follow-up and home visits. We use the quality of life scale (QOL-C30), anxiety scale (SAS) and depression scale (SDS) to evaluate these patients before intervention, and during follow-up at 1 month and 3 months after the intervention. Results: Overall health and quality of life were significantly different between the groups (p<0.05), Emotional function score was significantly higher after intervention (p<0.05), as were cognitive function and social function scores after 3 months of intervention (p<0.05). Scores of fatigue, nausea and vomiting, pain, appetite and constipation were also significantly different between the two groups (p<0.05). Rates of anxiety and depression after 3 months of intervention were 11.1%, 22.2% and 34.4%, 53.3%, the differences being significant (p<0.05). Conclusions: NPC home nursing plan could effectively improve overall quality of life, cognitive function, social function (after 3 months) of patients, but improvement regarding body function is not suggested. Fatigue, nausea and vomiting, pain, appetite, constipation were clearly improved. We should further pursue a personalized, comprehensive measurements for nursing interventions and try to improve the quality of life of NPC patients at home.

• Journal of the korean academy of Pediatric Dentistry
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• v.47 no.3
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• pp.344-351
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• 2020

Amelogenesis imperfecta (AI) occurs either in isolation or in association with other dental abnormalities and systemic disorder. A rare syndrome associating AI with nephrocalcinosis was named as Enamel Renal Syndrome (ERS; OMIM #204690). This syndrome is characterized by severe enamel hypoplasia, failed tooth eruption, intra pulpal calcifications, enlarged gingiva, and nephrocalcinosis. Nephrocalcinosis is a condition where calcium salts are deposited in renal tissue, and this may lead to critical kidney complications. This rare syndrome shows pathognomonic oral characteristics that are easily detectable at an early age, which proceeds the onset of renal involvement. Pediatric dentists are the first oral health practitioners whom ERS patients will meet at early age. The role of pediatric dentists is critically important for early diagnosis and referral of patients to both nephrologists for renal assessment and geneticists for identification of causative mutation and diagnosis. Early detection of renal involvement may provide chances to prevent further undesired renal complications.

• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.789-795
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• 2020

Many deaths have been reported due to the worldwide pandemic of COVID-19. In order to prevent the further spread of COVID-19, it is necessary to quickly and accurately read images of suspected patients and take appropriate measures. To this end, this paper introduces a deep learning-based COVID-19 chest X-ray reading technique that can assist in image reading by providing medical staff whether a patient is infected. First of all, in order to learn the reading model, a sufficient dataset must be secured, but the currently provided COVID-19 open dataset does not have enough image data to ensure the accuracy of learning. Therefore, we solved the image data number imbalance problem that degrades AI learning performance by using a Stacked Generative Adversarial Network(StackGAN++). Next, the DenseNet-based classification model was trained using the augmented data set to develop the reading model. This classification model is a model for binary classification of normal chest X-ray and COVID-19 chest X-ray, and the performance of the model was evaluated using part of the actual image data as test data. Finally, the reliability of the model was secured by presenting the basis for judging the presence or absence of disease in the input image using Grad-CAM, one of the explainable artificial intelligence called XAI.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.57-65
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• 2021

A hyper-connected intelligence information society is emerging that creates new value by converging IoT, AI, and Bigdata, which are new technologies of the fourth industrial revolution, in all industrial fields. Everything is connected to the network and data is exploding, and artificial intelligence can learn on its own and even intellectual judgment functions are possible. In particular, the Internet of Things provides a new communication environment that can be connected to anything, anytime, anywhere, enabling super-connections where everything is connected. Artificial intelligence technology is implemented so that computers can execute human perceptions, learning, reasoning, and natural language processing. Artificial intelligence is developing advanced technologies such as machine learning, deep learning, natural language processing, voice recognition, and visual recognition, and includes software, machine learning, and cloud technologies specialized in various applications such as safety, medical, defense, finance, and welfare. Through this, it is utilized in various fields throughout the industry to provide human convenience and new values. However, on the contrary, it is time to respond as intelligent and sophisticated cyber threats are increasing and accompanied by potential adverse functions such as securing the technical safety of new technologies. In this paper, we propose a new data modeling method to enable IoT integrated security control by utilizing artificial intelligence technology as a way to solve these adverse functions.

• The Journal of the Convergence on Culture Technology
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• v.8 no.3
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• pp.405-413
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• 2022

Since the COVID-19 outbreak, the active utilization of new health care service utilizing the ICT technology and data science such as telemedicine, smart hospital, AI dignosis has been increasingly found. In this study we examined the business model of Amazon healthcare which leads disruptive innovation in U.S. health care industry with the introduction of hybrid model of telemedicin, in-person care and customer-centric online drug delivery, home-use diagnostic kit, characterized by the integrated model combining medical care, drug delivery and the use of diagnostic kit. We showed using the multiproduct competition model that the synergy effect between the Amazon's original business areas and the healthcare business area causes the active market penetration and the increase in the customer value from utilization of the Amazon care. Using Hotelling's spatial competition model, we also showed that the competition in the health care market can be greater when consumer's choice of health care providers are available in telemedicine platform. In the long, run the issue of competition being weakened due to the exit of less competent healthcare providers may arise, to which the policymakers in the charge of fair competition in health care industry should pay attention.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.2
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• pp.1-9
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• 2023

Research using AI and big data is also being actively conducted in the health and medical fields such as disease diagnosis and treatment. Most of the existing research data used cohort data from research institutes or some patient data. In this paper, the difference in the prediction rate of survival and the factors affecting survival between breast cancer patients in their 40~50s and other age groups was revealed using health insurance review claim data held by the HIRA. As a result, the accuracy of predicting patients' survival was 0.93 on average in their 40~50s, higher than 0.86 in their 60~80s. In terms of that factor, the number of treatments was high for those in their 40~50s, and age was high for those in their 60~80s. Performance comparison with previous studies, the average precision was 0.90, which was higher than 0.81 of the existing paper. As a result of performance comparison by applied algorithm, the overall average precision of Decision Tree, Random Forest, and Gradient Boosting was 0.90, and the recall was 1.0, and the precision of multi-layer perceptrons was 0.89, and the recall was 1.0. I hope that more research will be conducted using machine learning automation(Auto ML) tools for non-professionals to enhance the use of the value for health insurance review claim data held by the HIRA.

• Journal of the Microelectronics and Packaging Society
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• v.29 no.4
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• pp.21-27
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• 2022

Flexible pressure sensor was developed using low-cost conductive graphite as printed electronics. Flexible pressure sensors are attracting attention as materials to be used in future industries such as medical, games, and AI. As a result of evaluating various electromechanical properties of the printed electrode for flexible pressure sensors, it showed a constant resistance change rate in a maximum tensile rate of 20%, 30° tension/bending, and a simple pulse test. A more appropriate matrix pattern was designed by simulating the electrodes for which this verification was completed. Utilizing the Serpentine pattern, we utilized a process that allows simultaneous fabrication and encapsulation of the matrix pattern. One side of the printed graphite electrode was O₂ plasma surface treated to increase adhesive strength, rotated 90 times, and two electrodes were made into one through a lamination process. As a result of pasting the matrix pattern prepared in this way to the wrist pulse position of the human body and proceeding with the actual measurement, a constant rate of resistance change was shown regardless of gender.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.165-184
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• 2023

Recently, Deep learning analysis of unstructured text data using language models, such as Google's BERT and OpenAI's GPT has shown remarkable results in various applications. Most language models are used to learn generalized linguistic information from pre-training data and then update their weights for downstream tasks through a fine-tuning process. However, some concerns have been raised that privacy may be violated in the process of using these language models, i.e., data privacy may be violated when data owner provides large amounts of data to the model owner to perform fine-tuning of the language model. Conversely, when the model owner discloses the entire model to the data owner, the structure and weights of the model are disclosed, which may violate the privacy of the model. The concept of offsite tuning has been recently proposed to perform fine-tuning of language models while protecting privacy in such situations. But the study has a limitation that it does not provide a concrete way to apply the proposed methodology to text classification models. In this study, we propose a concrete method to apply offsite tuning with an additional classifier to protect the privacy of the model and data when performing multi-classification fine-tuning on Korean documents. To evaluate the performance of the proposed methodology, we conducted experiments on about 200,000 Korean documents from five major fields, ICT, electrical, electronic, mechanical, and medical, provided by AIHub, and found that the proposed plug-in model outperforms the zero-shot model and the offsite model in terms of classification accuracy.

• Journal of The Korean Institute of Defense Technology
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• v.5 no.3
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• pp.8-17
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• 2023

The current weapon system is operated as a complex weapon system with various standards and protocols applied, so there is a risk of failure in smooth information exchange during combined and joint operations on the battlefield. The interoperability of weapon systems to carry out precise strikes on key targets through rapid situational judgment between weapon systems is a key element in the conduct of war. Since the Korean military went into service, there has been a need to change the configuration and improve performance of a large number of software and hardware, but there is no verification system for the impact on interoperability, and there are no related test tools and facilities. In addition, during combined and joint training, errors frequently occur during use after arbitrarily changing the detailed operation method and software of the weapon/power support system. Therefore, periodic verification of interoperability between weapon systems is necessary. To solve this problem, rather than having people schedule an evaluation period and conduct the evaluation once, AI should continuously evaluate the interoperability between weapons and power support systems 24 hours a day to advance warfighting capabilities. To solve these problems, To this end, preliminary research was conducted to improve defense interoperability capabilities by applying natural language processing techniques (①Word2Vec model, ②FastText model, ③Swivel model) (using published algorithms and source code). Based on the results of this experiment, we would like to present a methodology (automated evaluation of interoperability requirements evaluation / level measurement through natural language processing model) to implement an automated defense interoperability evaluation tool without relying on humans.