• The Plant Pathology Journal
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• v.17 no.6
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• pp.373.4-373
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• 2001

• Electronics and Telecommunications Trends
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• v.38 no.3
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• pp.66-77
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• 2023

Governments intend to use the public warning system to deliver timely and accurate information using accessible communication technologies for protecting the population and reducing damage to life and property. In particular, many countries implement system interworking with smartphones to notify of disasters or emergencies. In Korea, since 2020, due to the influence of the coronavirus disease, frequent emergency text messages led people to turn off related notifications, and complaints for receiving irrelevant messages from nearby warning areas have increased. Therefore, technical improvements for issuing more accurate disaster information to a specific region should be devised through a geography-based emergency disaster message transmission service. We analyze development trends of public warning systems and service cases of geography-based emergency text transmission services in various countries.

• YAKHAK HOEJI
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• v.54 no.6
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• pp.455-460
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• 2010

We evaluated the inappropriateness of metformin use in patients with type 2 diabetes and chronic medical conditions to identify the frequency of the prescription metformin in violation of the food and drug administration (FDA) black box warning. We reviewed medical records of 307 outpatients who received metformin at endocrinology department in a hospital setting between January 1, 2005 and August 30, 2009. Of the 307 outpatients, 25 discontinued treatment of metformin due to elevated serum creatinine level (Scr${\geq}$1.5 mg/dl in male, Scr${\geq}$1.4 mg/dl in female), cancers, and/or liver disease. 5 were lost to follow-up. 89 (29.0%) of the patients had cardiovascular disease, 54.1% for hypertension, 9.8% for liver disease, and 60 (20.8%) for chronic kidney disease. 12 patients (3.9%) with chronic kidney disease and/or elevated serum creatinine level, and 1 patient (0.3%) with lactic acidosis were contraindicated to metformin use. Metformin should be avoided in 7 outpatients (2.3%) with active hepatitis and 1 patient (2.6%) with liver cirrhosis. Of the 307 outpatients, 13 (4.2%) patients who received metformin at the first visit and 16 (8.7%) patients who received metformin at the last visit violated to black box warning. 8 (2.6%) of the patients were in precautionary conditions to metformin use. Adjusted mean difference of serum creatinine was -0.16 mg/dl [95% CI: -0.22 to -0.11 (p<0.05)] and adjusted mean difference of alanine aminotransferase was 4.46 IU/l [95% CI: 2.47 to 6.44 (p<0.05)] between the first visit and the last visit. Critical number of elderly patients who are at the high risks of drug-disease and drug-laboratory interaction is exposed to the inappropriate metformin use in violation of black box warning. The periodic evaluation of metformin use and monitoring prescription through drug utility review (DUR) system is needed to improve patients' safety and to reduce adverse drug events.

• Journal of Digital Contents Society
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• v.19 no.3
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• pp.587-593
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• 2018

The syndromic surveillance system is designed to identify illness clusters before diagnoses are confirmed and reported to public health agencies, to provide rapid public health response, and thereby to reduce morbidity and mortality. Korea Centers for Disease Control and Prevention (KCDC) has implemented the emergency department-based syndromic surveillance system. To design upgraded and enhanced functions of the current syndromic surveillance system in KCDC for the early warning of adverse disease events, we surveyed many papers. This paper describes the operation status of syndromic surveillance system in other countries and the improvement of the syndromic surveillance system in KCDC.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.295-298
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• 2014

In recently, a climatic change(such as subtropical climate and frequent unusual high temperature) and the open-trade policies of agricultural & livestock products are increasing the outbreak risk of highly pathogenic avian influenza(HPAI) and foot and mouth disease(FMD), and accordingly the socio-economic damage and impacts are also increasing due to the cases such as damage from the last 5 times of FMD outbreak(3,800 billion won), from 10 years public control cost of Pine Wilt Disease (PWD)(238.3 billion won), and from the increased invasive pests of exotic plant like isoptera. Therefore, the establishment of new operation strategy of IoT(Internet of Things) based satellite early warning system(SEWS) for plants and animals as a subsystem of national disaster response and management system is being required, where the forensic technology & measures should be applied as a government policy to estimate the post compensation and to carry out the legal responsibility.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.8
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• pp.1460-1465
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• 2016

Lots of costs threaten the sustainability of the national health-guarantee system. Despite research by the national center for disease control and prevention on health care dynamics with its auditing systems, there are still restrictions of time limitation, sample limitation, and, target diseases limitation. Against this backdrop, using huge volume of total data, many technologies could be fully adopted to the preliminary forecasting and its target-disease expanding of health. With structured data from the national health insurance and unstructured data from the social network service, we attempted to design a model to predict disease. The model can enhance national health and maximize social benefit by providing a health warning service. Also, the model can reduce the advent increase of national health cost and predict timely disease occurrence based on Big Data analysis. We researched related medical prediction cases and performed an experiment with a pilot project so as to verify the proposed model.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.67-77
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• 2023

This study represents an innovative research conducted in the smart farm environment, developing a deep learning-based disease and pest detection model and applying it to the Intelligent Internet of Things (IoT) platform to explore new possibilities in the implementation of digital agricultural environments. The core of the research was the integration of the latest ImageNet models such as Pseudo-Labeling, RegNet, EfficientNet, and preprocessing methods to detect various diseases and pests in complex agricultural environments with high accuracy. To this end, ensemble learning techniques were applied to maximize the accuracy and stability of the model, and the model was evaluated using various performance indicators such as mean Average Precision (mAP), precision, recall, accuracy, and box loss. Additionally, the SHAP framework was utilized to gain a deeper understanding of the model's prediction criteria, making the decision-making process more transparent. This analysis provided significant insights into how the model considers various variables to detect diseases and pests.

• Journal of Veterinary Clinics
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• v.41 no.2
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• pp.71-78
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• 2024

Changes in elimination behavior, including urination and defecation, are common clinical signs of numerous disorders in cats. Therefore, this study attempted to automatically measure the elimination behavior of cats using the litter box and develop an early warning system for the guardian in case of abnormalities. To construct an early warning system for abnormal changes through cat elimination behavior, it consisted of a litter box, an automatic sensor for data collection and data wifi transmission, a server for data analysis, and a mobile phone app for result transmission and early warning. To establish the reference interval (RI), the elimination behavior was monitored for more than 2 weeks using a motion sensor within a litter box in 37 healthy cats and 19 diseased cats. The data were expressed as daily total visits, daily total stay duration, average stay duration per elimination, weekly total visits, and weekly total stay duration. Healthy cats showed median daily total visits of 3 times/day (RI 1.0-7.0) and daily total stay duration of 192 s/day (RI 8.0-452.0). For weekly data, the median total visits were 20 times/week (RI 3.0-35.25) and the median total stay duration was 1,147 s/week (RI 80.0-2,249.5). The average stay duration per elimination was 59 s/elimination (RI 4.67-132.0). Diseased cats showed more frequent elimination behavior than healthy cats (p < 0.001). Otherwise, for each elimination, diseased cats had shorter stay durations than healthy cats (p < 0.001). This study established the RIs of elimination behavior parameters (frequency and duration) in healthy cats. The present study might help guardians and veterinarians detect changes in elimination behaviors in diseased cats at an early stage.

• Korean Journal of Agricultural and Forest Meteorology
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• v.4 no.1
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• pp.23-28
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• 2002

Sets of weather variables for estimation of LWD were evaluated using CART(Classification And Regression Tree) models. Input variables were sets of hourly observations of air temperature at 0.3-m and 1.5-m height, relative humidity(RH), and wind speed that were obtained from May to September in 1997, 1998, and 1999 at 15 weather stations in iowa, Illinois, and Nebraska, USA. A model that included air temperature at 0.3-m height, RH, and wind speed showed the lowest misidentification rate for wetness. The model estimated presence or absence of wetness more accurately (85.5%) than the CART/SLD model (84.7%) proposed by Gleason et al. (1994). This slight improvement, however, was insufficient to justify the use of our model, which requires additional measurements, in preference to the CART/SLD model. This study demonstrated that the use of measurements of temperature, humidity, and wind from automated stations was sufficient to make LWD estimations of reasonable accuracy when the CART/SLD model was used. Therefore, implementation of crop disease-warning systems may be facilitated by application of the CART/SLD model that inputs readily obtainable weather observations.

• Journal of information and communication convergence engineering
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• v.5 no.2
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• pp.98-103
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• 2007

Long term Electrocardiogram (ECG) [1] analysis plays a key role in heart disease analysis. A combined detection of QRS-complex and P-wave in ECG signal for ubiquitous healthcare system was designed and implemented which can be used as an advanced warning device. The ECG features are used to detect life-threating arrhythmias, with an emphasis on the software for analyzing QRS complex and P-wave in wireless ECG signals at server after receiving data from base station. Based on abnormal ECG activity, the server will transfer alarm conditions to a doctor's Personal Digital Assistant (PDA). Doctor can diagnose the patients who have survived from cardiac arrhythmia diseases.