• The Journal of KAIRB
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• v.5 no.1
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• pp.21-32
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• 2023

Purspose: The purpose of this study is to examine the meaning and definition of vulnerable subjects in clinical trials in light of domestic and international regulations and guidelines, to analyze the contents of standard operation procedures (SOPs) among advanced general hospitals in Korea that conduct clinical trials, and to examine deliberation procedures for operation plans. Methods: The study examined how vulnerable research subjects were defined and described in related regulations and the classification of vulnerable research subjects presented in the IRB/HRPP SOPs of 18 clinical trial institutions, including 11 AAHRPP-accreditated general hospitals in Korea, as well as the operation of the IRB deliberation. Results: Among all domestic and international regulations and guidelines, only the The Council for International Organization of Medical Sciences (CIOMS) guidelines explain why vulnerability is related to judgments on the severity of physical, psychological, and social harm, why individuals are vulnerable, and for what reasons. However, the classification of vulnerable subjects by institutions differed from the classification by the International Conference on Harmonization-Good Clinical Practice (ICH-GCP). A total of the 16 institutions classified children and minors as vulnerable research subjects. 14 institutions classified subjects who cannot consent freely were classified as vulnerable subjects. 15 institutions classified sujects who can be affected by the organizational hierarchy were classified as vulnerable subjects. Subjects in emergency situations were regarded as vulnerable research subjects in 8 of institutions, while people in wards, patients with incurable diseases, and the economically poor including the unemployed were categorized as vulnerable research subjects in 7, 4, and 4 of institutions, respectively. Additionally, some research subjects were not classified as vulnerable by ICH-GCP but were classified as vulnerable by domestic institutions 15 of the institutions classified pregnant women and fetuses as vulnerable, 11 classified the elderly as vulnerable, and 6 classified foreigners as vulnerable. Conclution: The regulations and institutional SOPs classify subjects differently, which may affect subject protection. There is a need to improve IRBs' classifications of vulnerable research subjects. It is also necessary to establish the standards according to the differences in deliberation processes. Further, it is recommended to maintain a consistent review of validity, assessment of risk/benefit, and a review using checklists and spokeperson. The review of IRB is to be carried out in a manner that respects human dignity by taking into account the physical, psychological, and social conditions of the subjects.

• Journal of Acupuncture Research
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• v.32 no.4
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• pp.47-68
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• 2015

Objectives : The purpose of this study is to review and propose improvements for the Romanization and English expressions in the WHO international classification of diseases 11th revision beta version (ICD-11b) traditional medicine chapter. Methods : ICD-11b as of October 5, 2015, was reviewed. Romanization and English expressions were analyzed with reference to existing standards such as the Basic Principles of Romanization stipulated by the National Institute of Korean Language, and the Korean Standard Classification of Diseases (KCD), suggested improvements followed. Results : Following the Basic Principles of Romanization, 131 ICD-11b rubrics need improvement in the Romanization of Korean. When compared to KCD-6 comparable rubrics, 161 ICD-11b rubrics are the same and 64 are different. When compared to KCD-7 comparable rubrics, 118 ICD-11b rubrics are the same, and 51 are different. In KCD-6, there are 127 rubrics that do not match with items in ICD-11b. In KCD-7, there are 123 rubrics that do not match with items in ICD-11b. Conclusions : ICD-11b may be improved by correcting the Romanization and consideration of English expressions suggested in this study.

• International journal of advanced smart convergence
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• v.13 no.2
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• pp.61-68
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• 2024

In the quest for advancing diabetes diagnosis, this study introduces a novel two-step machine learning approach that synergizes the probabilistic predictions of Logistic Regression with the classification prowess of Random Forest. Diabetes, a pervasive chronic disease impacting millions globally, necessitates precise and early detection to mitigate long-term complications. Traditional diagnostic methods, while effective, often entail invasive testing and may not fully leverage the patterns hidden in patient data. Addressing this gap, our research harnesses the predictive capability of Logistic Regression to estimate the likelihood of diabetes presence, followed by employing Random Forest to classify individuals into diabetic, pre-diabetic or nondiabetic categories based on the computed probabilities. This methodology not only capitalizes on the strengths of both algorithms-Logistic Regression's proficiency in estimating nuanced probabilities and Random Forest's robustness in classification-but also introduces a refined mechanism to enhance diagnostic accuracy. Through the application of this model to a comprehensive diabetes dataset, we demonstrate a marked improvement in diagnostic precision, as evidenced by superior performance metrics when compared to other machine learning approaches. Our findings underscore the potential of integrating diverse machine learning models to improve clinical decision-making processes, offering a promising avenue for the early and accurate diagnosis of diabetes and potentially other complex diseases.

• Safety and Health at Work
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• v.8 no.1
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• pp.105-115
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• 2017

Background: The goal of this study is to develop a general population job-exposure matrix (GPJEM) on asbestos to estimate occupational asbestos exposure levels in the Republic of Korea. Methods: Three Korean domestic quantitative exposure datasets collected from 1984 to 2008 were used to build the GPJEM. Exposure groups in collected data were reclassified based on the current Korean Standard Industrial Classification ($9^{th}$ edition) and the Korean Standard Classification of Occupations code ($6^{th}$ edition) that is in accordance to international standards. All of the exposure levels were expressed by weighted arithmetic mean (WAM) and minimum and maximum concentrations. Results: Based on the established GPJEM, the 112 exposure groups could be reclassified into 86 industries and 74 occupations. In the 1980s, the highest exposure levels were estimated in "knitting and weaving machine operators" with a WAM concentration of 7.48 fibers/mL (f/mL); in the 1990s, "plastic products production machine operators" with 5.12 f/mL, and in the 2000s "detergents production machine operators" handling talc containing asbestos with 2.45 f/mL. Of the 112 exposure groups, 44 groups had higher WAM concentrations than the Korean occupational exposure limit of 0.1 f/mL. Conclusion: The newly constructed GPJEM which is generated from actual domestic quantitative exposure data could be useful in evaluating historical exposure levels to asbestos and could contribute to improved prediction of asbestos-related diseases among Koreans.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.10
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• pp.4447-4450
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• 2015

Background: Nasopharyngeal cancer is a disease with distinct ethnic and geographical distribution. The aim of this review was to describe the epidemiological characteristics of nasopharyngeal cancer in Iran from 2004 to 2009 because no systematic study has been performed to evaluate the trends of its incidence yet. Materials and Methods: The data were derived from the databases of the National Cancer Data System Registry in the period of 2004-2009. Nasopharyngeal cancers were classified according to the International Classification of Diseases for Oncology. Incidence rates and trends were calculated and evaluated by gender, age decade, and histopathology types. Results: A total of 1,637 nasopharyngeal cancers were registered in Iran from 2004 to 2009 giving an incidence of 0.38 per 100,000. The male-to-female ratio was 2.08:1. The trend of incidence was found to have increased, with a significant increase observed in males. Undifferentiated carcinoma was the most common histopathology type in all the age decades. Conclusions: Because the incidence of nasopharyngeal cancers in Iran has increased, especially in males, further studies are recommended for understanding of the etiological factors involved in the rise of the disease.

• The Journal of Internal Korean Medicine
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• v.38 no.5
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• pp.658-667
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• 2017

Objectives: This is a case report regarding the effect of Korean traditional medicine on vision disorder and quadriplegia in a patient with neuromyelitis optica. Methods: We treated a patient who was diagnosed with neuromyelitis optica with Korean traditional medicine, including acupuncture, moxibustion, and herbal medicine (Gigugyanghyeol-tang gamibang) for 106 days. We evaluated the patient with the International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI), Modified Barthel Index (MBI), Modified Ashworth Scale (MAS) Grade, and Numeric Rating Scale-11 (NRS-11). Results: After treatment, the patient's symptoms were improved. The ISNCSCI scores increased from 42 to 66 in motor score, from 152 to 196 in sensory score, and from A to D in the ASIA impairment scale; the MBI score increased from 9 to 33, while the score of the MAS Grade decreased from I+ to I, and the NRS-11 scores of vision disorder, spasticity, and tingling decreased from 10 to 7, 3, and 2-3, respectively. Conclusions: Korean traditional medicine may be effective for treatment of vision disorder and quadriplegia in patients with neuromyelitis optica.

• Korean Journal of Veterinary Service
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• v.41 no.2
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• pp.97-104
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• 2018

Recently, demographic studies of veterinary medical database have been conducted to understand patterns of disease occurrence. Understanding incidence of breed-related disease would provide appropriate guidance for future health care strategies and offer useful information for early diagnosis of disease. However, in veterinary medicine, theses research has not yet been investigated in the Republic of Korea. The purpose of this study was to investigate the prevalence of common medical disorders among dog breeds examined at primary-care veterinary clinics in Jeollabuk-Do, Republic of Korea. The data were analyzed based on World Health Organization's International Classification of Disease. A total 13,176 medical records of canine patients were analyzed from six primary veterinary clinics in Jeollabuk-Do from January to December 2016. Results showed that the most common health problems were 'disease of skin' (17.7%); followed by 'diseases of digestive system' (12.26%), 'preventive medicine' (10.08%), and 'diseases of ear and mastoid process' (10.4%). In seven out of ten breeds, the most common medical disorder was skin disease. For poodle such as Pomeranian and Chihuahua, digestive system disease was most prevalent. On the other hand, respiratory system disease was found to be higher in Pomeranian than other breeds; while ear and mastoid process disease was most common for Maltese and Poodle. This study can help owners, breeders, and veterinarians prevent and manage various diseases of popular breeds in Jeollabuk-Do in the future.

• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.526-538
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• 2021

Machine and deep learning-based models are emerging techniques that are being used to address prediction problems in biomedical data analysis. DNA sequence prediction is a critical problem that has attracted a great deal of attention in the biomedical domain. Machine and deep learning-based models have been shown to provide more accurate results when compared to conventional regression-based models. The prediction of the gene sequence that leads to cancerous diseases, such as prostate cancer, is crucial. Identifying the most important features in a gene sequence is a challenging task. Extracting the components of the gene sequence that can provide an insight into the types of mutation in the gene is of great importance as it will lead to effective drug design and the promotion of the new concept of personalised medicine. In this work, we extracted the exons in the prostate gene sequences that were used in the experiment. We built a Deep Neural Network (DNN) and Bi-directional Long-Short Term Memory (Bi-LSTM) model using a k-mer encoding for the DNA sequence and one-hot encoding for the class label. The models were evaluated using different classification metrics. Our experimental results show that DNN model prediction offers a training accuracy of 99 percent and validation accuracy of 96 percent. The bi-LSTM model also has a training accuracy of 95 percent and validation accuracy of 91 percent.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.127-134
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• 2022

Generally developed for medical testing, electrocardiogram (ECG) recordings seizure the cardiac electrical signals from the surface of the body. ECG study can consequently be a vital first step to support analyze, comprehend, and expect cardiac ailments accountable for 31% of deaths globally. Different tools are used to analyze ECG signals based on computational methods, and explicitly machine learning method. In all abovementioned computational simulations are prevailing tools for cataloging and clustering. This review demonstrates the different effective methods for heart disease based on computational methods for ECG analysis. The accuracy in machine learning and three-dimensional computer simulations, among medical inferences and contributions to medical developments. In the first part the classification and the methods developed to get data and cataloging between standard and abnormal cardiac activity. The second part emphases on patient analysis from entire ECG recordings due to different kind of diseases present. The last part represents the application of wearable devices and interpretation of computer simulated results. Conclusively, the discussion part plans the challenges of ECG investigation and offers a serious valuation of the approaches offered. Different approaches described in this review are a sturdy asset for medicinal encounters and their transformation to the medical world can lead to auspicious developments.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.53-64
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• 2023

Early detection of endometrial carcinoma in uterus is essential for effective treatment. Endometrial carcinoma is the worst kind of endometrium cancer among the others since it is considerably more likely to affect the additional parts of the body if not detected and treated early. Non-invasive medical computer vision, also known as medical image processing, is becoming increasingly essential in the clinical diagnosis of various diseases. Such techniques provide a tool for automatic image processing, allowing for an accurate and timely assessment of the lesion. One of the most difficult aspects of developing an effective automatic categorization system is the absence of huge datasets. Using image processing and deep learning, this article presented an artificial endometrium cancer diagnosis system. The processes in this study include gathering a dermoscopy images from the database, preprocessing, segmentation using hybrid Fuzzy C-Means (FCM) and optimizing the weights using the Whale Optimization Algorithm (WOA). The characteristics of the damaged endometrium cells are retrieved using the feature extraction approach after the Magnetic Resonance pictures have been segmented. The collected characteristics are classified using a deep learning-based methodology called Long Short-Term Memory (LSTM) and Bi-directional LSTM classifiers. After using the publicly accessible data set, suggested classifiers obtain an accuracy of 97% and segmentation accuracy of 93%.