• Title/Summary/Keyword: Medical bigdata

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Improving Legislation on the use of Healthcare Data for Research Purposes (보건의료 빅데이터의 연구목적 사용에 대한 법제 개선방안)

  • Park, Dae Woong;Jeong, Hyun Hak;Jeong, Myung Jin;Ryoo, Hwa Shin
    • The Korean Society of Law and Medicine
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    • v.17 no.2
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    • pp.315-346
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    • 2016
  • With the development of big data processing technology, the potential value of healthcare big data has attracted much attention. In order to realize these potential values, various research using the healthcare big data are essential. However, the big data regulatory system centered on the Personal Information Protection Act does not take into account the aspect of big data as an economic material and causes many obstacles to utilize it as a research purpose. The regulatory system of healthcare information, centered on the primary purpose of patient treatment, should be improved in a way that is compatible with the development of technology and easy to use for public interest. To this end, it is necessary to examine the trends of overseas legal system reflecting the concerns about the balance of protection and utilization of personal information. Based on the implications of the overseas legal system, we can derive improvement points in the following directions from our legal system. First, a legal system that specializes in healthcare information and encompasses protection and utilization is needed. De-identification, which is an exception to the Privacy Act, should also clearly define its level. It is necessary to establish a legal basis for linking healthcare big data to create synergy effects in research. It is also necessary to examine the introduction of the opt-out system on the basis of the discussion on the foreign debate and social consensus. But most importantly, it is the people's trust in these systems.

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A Study of the Recent Diseases in Korean Pediatrics and Adolescent Patients Treated with Oriental Medicine (최근 국내 한방 진료를 받은 소아·청소년 환자의 질환 진단명 분석)

  • Kim, Kyeong Ri;Lee, Jin Hwa
    • The Journal of Pediatrics of Korean Medicine
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    • v.32 no.1
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    • pp.54-74
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    • 2018
  • Objectives The purpose of this study is to investigate recent trend of diseases in Korean pediatrics and adolescent patients treated with oriental medicine. Methods Using data from the Korean statistical information service and healthcare bigdata hub, top 500 diseases pediatrics and adolescents that were treated with oriental medicine from 2012 to 2016 in admission and outpatient department was collected. Results From the inpatient study, majority of the subjects were between 15 to 19 years old (62.74%), followed by 10 to 14 years old, 5 to 9 years old and under 5 years old. In the outpatient department study, majority was 15 to 19 years old (36.51%), followed by 10 to 14 years old, under 5 years old, 5 to 9 years old. In systemic division of admission part, the most common disease was musculoskeletal related which was 72.32%, followed by brain, nerve, respiratory, dermatology and digestive related diseases. In systemic division of outpatient department, respiratory disease was the most common (41.81%), followed by digestive, dermatology, brain and nerve diseases. For under 5 years old patient group, respiratory disease was the most common, 29.86%, followed by dermatology, musculoskeletal, digestive related diseases. For 5 to 19 years old group of patients, musculoskeletal disease was most common. For the 5 to 14 years old patient group, respiratory related disease was the most common followed by dermatology and digestive diseases. For 15 to 19 years old patient group, digestive disease was the most common followed by respiratory and dermatology related diseases. For under 5 to 9 years old outpatient group, respiratory disease was the most common, and for under 5-year-old group, digestive, growth development, and dermatology disease were common. For the 5 to 9 years old group of patients, musculoskeletal related disease was the most common followed by digestive and dermatology related diseases. For the 10 to 19 years old patient group, musculoskeletal was the most common. For the 10 to 14 years old patient group, respiratory related disease was the most common followed by digestive, dermatology disease. For the 15 to 19 years old patient group, digestive related disease was the most common followed by respiratory, dermatology diseases. Musculoskeletal disease increased every year, in both inpatient and outpatient. Respiratory, brain, nerve, digestive related diseases were generally decreased. In outpatient, respiratory diseases were increased every year but brain, nerve, digestive related diseases were generally decreased. Conclusions More studies about the oriental medicine in chronic disease, such as allergy, metabolic syndrome, in Korean pediatrics and adolescents are needed.

Endovascular Treatments Performed Collaboratively by the Society of Korean Endovascular Neurosurgeons Members : A Nationwide Multicenter Survey

  • Kim, Tae Gon;Kwon, Oki;Shin, Yong Sam;Sung, Jae Hoon;Koh, Jun Seok;Kim, Bum-Tae
    • Journal of Korean Neurosurgical Society
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    • v.62 no.5
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    • pp.502-518
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    • 2019
  • Objective : Since less invasive endovascular treatment was introduced to South Korea in 1994, a considerable proportion of endovascular treatments have been performed by neuroradiology doctors, and endovascular treatments by vascular neurosurgeons have recently increased. However, few specific statistics are known regarding how many endovascular treatments are performed by neurosurgeons. Thus, authors compared endovascular treatments collaboratively performed by vascular neurosurgeons with all cases throughout South Korea from 2013 to 2017 to elucidate the role of neurosurgeons in the field of endovascular treatment in South Korea. Methods : The Society of Korean Endovascular Neurosurgeons (SKEN) has issued annual reports every year since 2014. These reports cover statistics on endovascular treatments collaboratively or individually performed by SKEN members from 2013 to 2017. The data was requested and collected from vascular neurosurgeons in various hospitals. The study involved 77 hospitals in its first year, and 100 in its last. National statistics on endovascular treatment from all over South Korea were obtained from the Healthcare Bigdata Hub website of the Health Insurance Review & Assessment Service based on the Electronic Data Interchange (EDI) codes (in the case of intra-arterial (IA) thrombolysis, however, statistics were based on a combination of the EDI and I63 codes, a cerebral infarction disease code) from 2013 to 2017. These two data sets were directly compared and the ratios were obtained. Results : Regionally, during the entire study period, endovascular treatments by SKEN members were most common in Gyeonggido, followed by Seoul and Busan. Among the endovascular treatments, conventional cerebral angiography was the most common, followed by cerebral aneurysmal coiling, endovascular treatments for ischemic stroke, and finally endovascular treatments for vascular malformation and tumor embolization. The number of endovascular treatments performed by SKEN members increased every year. Conclusion : The SKEN members have been responsible for the major role of endovascular treatments in South Korea for the recent 5 years. This was achieved through the perseverance of senior members who started out in the midst of hardship, the establishment of standards for the training/certification of endovascular neurosurgery, and the enthusiasm of current SKEN members who followed. To provide better treatment to patients, we will have to make further progress in SKEN.

Analysis of Major COVID-19 Issues Using Unstructured Big Data (비정형 빅데이터를 이용한 COVID-19 주요 이슈 분석)

  • Kim, Jinsol;Shin, Donghoon;Kim, Heewoong
    • Knowledge Management Research
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    • v.22 no.2
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    • pp.145-165
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    • 2021
  • As of late December 2019, the spread of COVID-19 pandemic began which put the entire world in panic. In order to overcome the crisis and minimize any subsequent damage, the government as well as its affiliated institutions must maximize effects of pre-existing policy support and introduce a holistic response plan that can reflect this changing situation- which is why it is crucial to analyze social topics and people's interests. This study investigates people's major thoughts, attitudes and topics surrounding COVID-19 pandemic through the use of social media and big data. In order to collect public opinion, this study segmented time period according to government countermeasures. All data were collected through NAVER blog from 31 December 2019 to 12 December 2020. This research applied TF-IDF keyword extraction and LDA topic modeling as text-mining techniques. As a result, eight major issues related to COVID-19 have been derived, and based on these keywords, this research presented policy strategies. The significance of this study is that it provides a baseline data for Korean government authorities in providing appropriate countermeasures that can satisfy needs of people in the midst of COVID-19 pandemic.

A Simulation Study on Image Quality of Virtual Monochromatic Image using Dual-energy Method (이중에너지 방법을 이용한 가상 단색 영상의 화질 시뮬레이션 연구)

  • Son, Ki-Hong;Lee, Soo-Yeul;Kim, Dae-Hong;Chung, Myung-Ae
    • Journal of the Korean Society of Radiology
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    • v.16 no.5
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    • pp.553-558
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    • 2022
  • The purpose of this work was a simulation study to evaluate the virtual monochromatic (VM) image quality of blood vessels compared to the monochromatic image. Dual-energy images were obtained based on the linear attenuation coefficients of five materials at 50 keV and 80 keV at low- and high-energies, respectively. A weighting factor is required to synthesize the VM image, and the liver and bone were used as basis materials to obtain the weighting factor. VM images were synthesized at energies ranging from 30 keV to 100 keV. Image quality was evaluated by Contrast to noise ratio (CNR) and noise by setting calcium and contrast medium as signals and blood as background. According to the results, the energies with the maximum CNR were 50 keV and 60 keV for calcium and contrast medium, respectively. The energies showing the minimum noise were 70 keV, 70 keV, and 60 keV in calcium, iodine contrast medium, and blood, respectively. The VM image can contribute to the improvement of diagnostic performance in CT examination because it can implement an image at the optimal energy that minimize noise and maximize CNR.

A Study on the Possibility of Pancreas Detection through Extraction of Effective Atomic Number using a Simulation such as Dual-energy CT (이중에너지 CT와 같은 시뮬레이션을 이용한 유효원자번호 추출을 통한 췌장 검출 가능성 연구)

  • Son, Ki-Hong;Lee, Soo-Yeul;Chung, Myung-Ae;Kim, Dae-Hong
    • Journal of the Korean Society of Radiology
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    • v.16 no.5
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    • pp.537-543
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    • 2022
  • The purpose of this simulation study was to evaluate the possibility of pancreas detection through effective atomic number information using dual-energy computed tomography(CT). The effective atomic number of 10 tissue-equivalent materials were estimated through stoichiometric calibration. For stoichiometric calibration, HU values at low-energy (80 kV) and high-energy (140 kV) for 10 tissue-equivalent materials were used. Based on this method, the effective atomic number image of the tissue-equivalent material was extracted through an iterative algorithm. According to the results, the attenuation ratio in accordance with the effective atomic number was estimated to have an R2 value of 0.9999, and the effective atomic number of Pancreas, Water, Liver, Blood, Spongiosa, and Cortical bone was overall within 1% accuracy compared to the theoretical value. Conventional pancreatic cancer examination uses a contrast medium, so there is a possibility of potential side effects of the contrast medium. In order to solve this problem, it is thought that it will be possible to contribute to an accurate and safe examination by extracting the effective atomic number using dual-energy CT without contrast enhancement. Based on this study, future research will be conducted on the detection of pancreatic cancer using the HU value of pancreatic cancer based on clinical images.

Transfer Learning based DNN-SVM Hybrid Model for Breast Cancer Classification

  • Gui Rae Jo;Beomsu Baek;Young Soon Kim;Dong Hoon Lim
    • Journal of the Korea Society of Computer and Information
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    • v.28 no.11
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    • pp.1-11
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    • 2023
  • Breast cancer is the disease that affects women the most worldwide. Due to the development of computer technology, the efficiency of machine learning has increased, and thus plays an important role in cancer detection and diagnosis. Deep learning is a field of machine learning technology based on an artificial neural network, and its performance has been rapidly improved in recent years, and its application range is expanding. In this paper, we propose a DNN-SVM hybrid model that combines the structure of a deep neural network (DNN) based on transfer learning and a support vector machine (SVM) for breast cancer classification. The transfer learning-based proposed model is effective for small training data, has a fast learning speed, and can improve model performance by combining all the advantages of a single model, that is, DNN and SVM. To evaluate the performance of the proposed DNN-SVM Hybrid model, the performance test results with WOBC and WDBC breast cancer data provided by the UCI machine learning repository showed that the proposed model is superior to single models such as logistic regression, DNN, and SVM, and ensemble models such as random forest in various performance measures.