• Quality Improvement in Health Care
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• v.19 no.1
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• pp.16-26
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• 2013

Health information technology (HIT) is one of the most familiar tools to healthcare providers. It is used in routine practice to reduce cost, to improve clinical performance, and to improve patient safety. Patient safety is the driving force of recent expansion of HIT industry. But there are many evidences that it can be harmful to patient safety. Role of HIT and HIT-related error became big issues because more and more healthcare providers and healthcare organizations are willing to adopt it. Adoption rate of HIT in Korea is higher than that of United States. But researches of HIT regarding patient safety are rare. In this article, types of HIT, their mechanisms of improving patient safety and HIT-related errors were reviewed. Status of HIT in terms of patient safety in Korea was also reviewed. Knowledge of how HIT can improve patient safety, its' limitation, and how to make it safer is crucial to whom have to use it to improve patient safety. Impact of HIT on patient safety must be evaluated actively in Korea. HIT which was proven to improve patient safety must be widely adopted. Government must prepare a strategic plan to improve HIT quality, support hospitals financially and institutionally to introduce qualified HIT, and develop HIT infrastructures and standard designed for patient safety.

• Journal of Advanced Navigation Technology
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• v.23 no.6
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• pp.584-588
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• 2019

Wireless body area network (WBAN) is a networking technology that enables early detection of abnormal health conditions, real-time medical monitoring, and telemedicine support systems. The internet of things (IoT) for healthcare, which has become an issue recently, is one of the most promising areas for improving the quality of human life. It must meet the high QoS requirements of the medical communication system like any other communication system. Therefore, the bit error rate (BER) threshold was chosen to accommodate the QoS requirements of the WBAN communication system. In this paper, we calculated BER performance of WBAN channel using IR-UWB PPM modulation and analyzed link budget and system margin of WBAN according to various system parameters.

• Journal of the Optical Society of Korea
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• v.17 no.4
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• pp.328-335
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• 2013

Due to the rapid advancement of auto-refractor technology, most optometry shops provide refraction services. Despite their speed and convenience, the measurement values provided by auto-refractors include a significant degree of error due to psychological and physical factors. Therefore, there is a need for repetitive testing to obtain a smaller mean error value. However, even repetitive testing itself might not be sufficient to ensure accurate measurements. Therefore, research on a method of measurement that can complement auto-refractor measurements and provide confirmation of refraction results needs to be conducted. The customized optometry model described herein can satisfy the above requirements. With existing technologies, using human eye measurement devices to obtain relevant individual optical feature parameters is no longer difficult, and these parameters allow us to construct an optometry model for individual eyeballs. They also allow us to compute visual images produced from the optometry model using the CODE V macro programming language before recognizing the diffraction effects visual images with the neural network algorithm to obtain the accurate refractive diopter. This study attempts to combine the optometry model with the back-propagation neural network and achieve a double check recognition effect by complementing the auto-refractor. Results show that the accuracy achieved was above 98% and that this application could significantly enhance the service quality of refraction.

• The Journal of Korean Physical Therapy
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• v.35 no.2
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• pp.43-47
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• 2023

Purpose: This study compared the movement control ability of the ankle joint according to the type of muscle contraction, namely, eccentric or concentric contractions. Methods: Thirty-four healthy adult subjects participated in this study. As a single group, before the experiment, the subjects were trained on achieving the required position of the ankle around the target point by manually controlling the ankle dorsiflexion by 10°. Concentric contraction starts at 0° and continues until the target point of 10° is reached. During an eccentric contraction, the ankle joint starts at 20° ankle dorsiflexion and continues till the target point is reached. Movements using eccentric contraction and concentric contraction were randomly performed 3 times each. Results: The results of comparing the difference in the movement control ability of each type of muscle contraction of ankle dorsiflexion showed that the measurement-remeasurement error was significant in eccentric contraction. Conclusion: In this study, we found a difference in the ability to control movement according to whether the contraction is eccentric or concentric. Therefore, we propose that the ability to control movement is affected by the type of muscle contraction.

• Korean Journal of Radiology
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• v.24 no.7
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• pp.698-714
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• 2023

In recent years, artificial intelligence, especially object detection-based deep learning in computer vision, has made significant advancements, driven by the development of computing power and the widespread use of graphic processor units. Object detection-based deep learning techniques have been applied in various fields, including the medical imaging domain, where remarkable achievements have been reported in disease detection. However, the application of deep learning does not always guarantee satisfactory performance, and researchers have been employing trial-and-error to identify the factors contributing to performance degradation and enhance their models. Moreover, due to the black-box problem, the intermediate processes of a deep learning network cannot be comprehended by humans; as a result, identifying problems in a deep learning model that exhibits poor performance can be challenging. This article highlights potential issues that may cause performance degradation at each deep learning step in the medical imaging domain and discusses factors that must be considered to improve the performance of deep learning models. Researchers who wish to begin deep learning research can reduce the required amount of trial-and-error by understanding the issues discussed in this study.

• Korean Journal of Radiology
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• v.24 no.11
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• pp.1151-1163
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• 2023

Objective: To develop a deep-learning-based bone age prediction model optimized for Korean children and adolescents and evaluate its feasibility by comparing it with a Greulich-Pyle-based deep-learning model. Materials and Methods: A convolutional neural network was trained to predict age according to the bone development shown on a hand radiograph (bone age) using 21036 hand radiographs of Korean children and adolescents without known bone development-affecting diseases/conditions obtained between 1998 and 2019 (median age [interquartile range {IQR}], 9 [7-12] years; male:female, 11794:9242) and their chronological ages as labels (Korean model). We constructed 2 separate external datasets consisting of Korean children and adolescents with healthy bone development (Institution 1: n = 343; median age [IQR], 10 [4-15] years; male: female, 183:160; Institution 2: n = 321; median age [IQR], 9 [5-14] years; male: female, 164:157) to test the model performance. The mean absolute error (MAE), root mean square error (RMSE), and proportions of bone age predictions within 6, 12, 18, and 24 months of the reference age (chronological age) were compared between the Korean model and a commercial model (VUNO Med-BoneAge version 1.1; VUNO) trained with Greulich-Pyle-based age as the label (GP-based model). Results: Compared with the GP-based model, the Korean model showed a lower RMSE (11.2 vs. 13.8 months; P = 0.004) and MAE (8.2 vs. 10.5 months; P = 0.002), a higher proportion of bone age predictions within 18 months of chronological age (88.3% vs. 82.2%; P = 0.031) for Institution 1, and a lower MAE (9.5 vs. 11.0 months; P = 0.022) and higher proportion of bone age predictions within 6 months (44.5% vs. 36.4%; P = 0.044) for Institution 2. Conclusion: The Korean model trained using the chronological ages of Korean children and adolescents without known bone development-affecting diseases/conditions as labels performed better in bone age assessment than the GP-based model in the Korean pediatric population. Further validation is required to confirm its accuracy.

• Korean Journal of Acupuncture
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• v.24 no.3
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• pp.17-31
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• 2007

Objectives : Moxibustion therapeutics is one of the most popular oriental treatments in Korea. In this study, we operate the Telephone Survey for grasping clinical actual state moxibustion therapeutics in Korea. Methods : Survey questions were developed based on consensus of acupuncture professors. The list of the Korean medical doctors with experiences more than 10 years is provided by the Association of the Korean Oriental Medicine. A stratified random sample of Korean medical doctors is drawn for the telephone interviews. We choose a bound on the error of estimation equal to 6.5 percentage, and the sample size is 260 for the national sample. Telephone interviews with them were conducted by the well-trained interviewers of Korea Institute of Oriental Medicine in Medical researcher from 26th March 2007 to 6th April 2007. Results : Ninty -four percents of Korean oriental medical doctors were male and most commonly, clinical experience of doctors were 20-29 years(47.3%). Sixty-seven percent of Korean oriental medical doctors used moxibustion therapeutics. The most common treatment disease was Musculo-skeletal disorder(38.3%), Digestive disorder(28.6%), Gynecology(14.1%). Indirect moxibustion were as frequent as 65.5% of moxibustion method. The most common reason of unused respondents was 'Lots of smell and smoke'(28.3%), 'The wound left a scar'(20.8%), 'Less effects'(20%), etc. Eighty-three percents Korean oriental medical doctors were against that moxibustion therapy used without doctor's examination Conclusions : This survey provides unique insight into the perception of the Korea medical doctor at moxibustion therapeutics. Future research need to provide more in-depth insight into doctor views of the experience.

• Proceedings of the KOSOMBE Conference
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• v.1991 no.05
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• pp.65-70
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• 1991

A hybrid model which uses a probabilistic model and a MLP(multi layer perceptron) model for pattern recognition of EMG(electromyogram) signals is proposed in this paper. MLP model has problems which do not guarantee global minima of error due to learning method and have different approximation grade to bayesian probabilities due to different amounts and quality of training data, the number of hidden layers and hidden nodes, etc. Especially in the case of new test data which exclude design samples, the latter problem produces quite different results. The error probability of probabilistic model is closely related to the estimation error of the parameters used in the model and fidelity of assumtion. Generally, it is impossible to introduce the bayesian classifier to the probabilistic model of EMG signals because of unknown priori probabilities and is estimated by MLE(maximum likelihood estimate). In this paper we propose the method which get the MAP(maximum a posteriori probability) in the probabilistic model by estimating the priori probability distribution which minimize the error probability using the MLP. This method minimize the error probability of the probabilistic model as long as the realization of the MLP is optimal and approximate the minimum of error probability of each class of both models selectively. Alocating the reference coordinate of EMG signal to the outside of the body make it easy to suit to the applications which it is difficult to define and seperate using internal body coordinate. Simulation results show the benefit of the proposed model compared to use the MLP and the probabilistic model seperately.

• The KIPS Transactions:PartB
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• v.17B no.4
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• pp.275-286
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• 2010

Reversible watermarking which can preserve the original quality of the digital contents and protect the copyright has been studied actively. Especially, in medical, military, and art fields, the need for reversible watermarking is increasing. This paper proposes a high-capacity reversible watermarking through predicted error expansion and error estimation compensation. Watermark is embedded by expanding the difference histogram between the original value and the predicted value. Differently from previous methods calculating the difference between adjacent pixels, the presented method calculates the difference between the original value and the predicted value, and that increases the number of the histogram value, where the watermark is embedded. As a result, the high capacity is achieved. The inserted watermark is extracted by restoring the histogram between the original value and the predicted value. To prove the performance, the presented algorithm is compared with other previous methods on various test images. The result supports that the presented algorithm has a perfect reversibility, a high image quality, and a high capacity.

• Journal of Electrical Engineering and Technology
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• v.2 no.1
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• pp.136-141
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• 2007

The Purpose Of This Paper Is To Use A Tactile Sensor To Compensate The Error Rate. Most Automated Sphygmomanometers Use The Oscillometric Method And Characteristic Ratio To Estimate Systolic And Diastolic Blood Pressure. However, Based On The Fact That Maximum Amplitude Of The Oscillometric Waveform And Characteristic Ratio Are Affected By Compliance Of The Aorta And Large Arteries, A Method To Measure The Artery Stiffness By Using A Tactile Sensor Was Chosen In Order To Integrate It With The Sphygmomanometer In The Future Instead Of Using Photoplethysmography. Since Tactile Sensors Have Very Weak Movements, Efforts Were Made To Maintain The Subject's Arm In A Fixed Position, And A 40hz Low Pass Filter Was Used To Eliminate Noise From The Power Source As Well As High Frequency Noise. An Analyzing Program Was Made To Get Time Delay Between The First And Second Peak Of The Averaged Digital Volume Pulse(${\Delta}t_{dvp}$), And The Subject's Height Was Divided By ${\Delta}t_{dvp}$ To Calculate The Stiffness Index Of The Arteries($Si_{dvp}$). Regression Equations Of Systolic And Diastolic Pressure Using $Si_{dvp}$ And Mean Arterial Pressure(Map) Were Computed From The Test Group (60 Subjects) Among A Total Of 121 Subjects(Age: $44.9{\pm}16.5$, Male: Female=40:81) And Were Tested In 61 Subjects To Compensate The Error Rate. Error Rates Considering All Subjects Were Systolic $4.62{\pm}9.39mmhg$, And Diastolic $14.40{\pm}9.62mmhg$, And Those In The Test Set Were $3.48{\pm}9.32mmhg,\;And\;14.34{\pm}9.67mmhg$ Each. Consequently, Error Rates Were Compensated Especially In Diastolic Pressure Using $Si_{dvp}$, Various Slopes From Digital Volume Pulse And Map To Systolic-$1.91{\pm}7.57mmhg$ And Diastolic $0.05{\pm}7.49mmhg$.