• International journal of advanced smart convergence
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• v.5 no.1
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• pp.47-50
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• 2016

Big Data is coming rapidly in recent times and keep the vast amount of data was utilized them. These data are utilized in many fields in particular, based on the patient data in the medical field to increase the therapeutic effect, as well as re-incidence to better treatment, lowering the readmission rates increased the quality of life. In this paper it is practiced to report basis of the analysis and verification of data using python. And it can be analyzed the data through a simple formula, from Select reason of Python to how it used; by Press analysis of Agriculture, Forestry and Fisheries research. In this process, a simple formula can be used that expression for analyzing the actual data so it taking advantage of the use of functions in real life.

• Convergence Security Journal
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• v.22 no.4
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• pp.85-97
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• 2022

With the development of big data technology, data is rapidly entering a hyperconnected intelligent society that accelerates innovative growth in all industries. The convergence industry, which holds and utilizes various high-quality data, is becoming a new growth engine, and big data is fused to various traditional industries. In particular, in the medical field, structured data such as electronic medical record data and unstructured medical data such as CT and MRI are used together to increase the accuracy of disease prediction and diagnosis. Currently, the importance and size of unstructured data are increasing day by day in the medical industry, but conventional data security technologies and policies are structured data-oriented, and considerations for the security and utilization of unstructured data are insufficient. In order for medical treatment using big data to be activated in the future, data diversity and security must be internalized and organically linked at the stage of data construction, distribution, and utilization. In this paper, the current status of domestic and foreign data security systems and technologies is analyzed. After that, it is proposed to add unstructured data-centered de-identification technology to the guidelines for unstructured data and technology application cases in the industry so that unstructured data can be actively used in the medical field, and to establish standards for judging personal information for unstructured data. Furthermore, an object feature-based identification ID that can be used for unstructured data without infringing on personal information is proposed.

• Journal of Preventive Medicine and Public Health
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• v.31 no.3 s.62
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• pp.471-480
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• 1998

This study was undertaken in order to estimate the accuracy of disease code of the Korean National Medical Insurance Data and disease the characteristics related to the accuracy. To accomplish these objectives, 2,431 cases coded as notifiable acute communicable diseases (NACD) were randomly selected from 1994 National Medical Insurance data file and family medicine specialists reviewed the medical records to confirm the diagnostic accuracy and investigate the related factors. Major findings obtained from this study are as follows : 1. The accuracy rate of disease code of NACD in National Medical Insurance data was very low, 10.1% (95% C.I. : 8.8-11.4). 2. The reasons of inaccuracy in disease code were 1) claiming process related administrative error by physician and non-physician personnel in medical institutions (41.0%), 2) input error of claims data by key punchers of National Medical Insurer (31.3%) and 3) diagnostic error by physicians (21.7%). 3. Characteristics significantly related with lowering the accuracy of disease code were location and level of the medical institutions in multiple logistic regression analysis. Medical institutions in Seoul showed lower accuracy than those in Kyonngi, and so did general hospitals, hospitals and clinics than tertiary hospitals. Physician related characteristics significantly lowering disease code accuracy of insurance data were sex, age group and specialty. Male physicians showed significantly lower accuracy than female physicians; thirties and fortieg age group also showed significantly lower accuracy than twenties, and so did general physicians and other specialists than internal medicine/pediatric specialists. This study strongly suggests that a series of policies like 1) establishment of peer review organization of National Medical Insurance data, 2) prompt nation-wide expansion of computerized claiming network of National Medical Insurance and 3) establishment and distribution of objective diagnostic criteria to physicians are necessary to set up a national disease surveillance system utilizing National Medical Insurance claims data.

• Journal of Information Processing Systems
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• v.20 no.4
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• pp.514-523
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• 2024

In the Internet of Medical Things, due to the sensitivity of medical information, data typically need to be retained locally. The training model of heart disease data can predict patients' physical health status effectively, thereby providing reliable disease information. It is crucial to make full use of multiple data sources in the Internet of Medical Things applications to improve model accuracy. As network communication speeds and computational capabilities continue to evolve, parties are storing data locally, and using privacy protection technology to exchange data in the communication process to construct models is receiving increasing attention. This shift toward secure and efficient data collaboration is expected to revolutionize computer modeling in the healthcare field by ensuring accuracy and privacy in the analysis of critical medical information. In this paper, we train and test a multiparty decision tree model for the Internet of Medical Things on a heart disease dataset to address the challenges associated with developing a practical and usable model while ensuring the protection of heart disease data. Experimental results demonstrate that the accuracy of our privacy protection method is as high as 93.24%, representing a difference of only 0.3% compared with a conventional plaintext algorithm.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.15
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• pp.6605-6608
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• 2015

Background: Colorectal cancer is a major cause of morbidity and mortality throughout the world. Colorectal cancer screening is an optimal way for reducing of morbidity and mortality and a clinical decision support system (CDSS) plays an important role in predicting success of screening processes. DSS is a computer-based information system that improves the delivery of preventive care services. The aim of this article was to detail engineering of information requirements and work flow design of CDSS for a colorectal cancer screening program. Materials and Methods: In the first stage a screening minimum data set was determined. Developed and developing countries were analyzed for identifying this data set. Then information deficiencies and gaps were determined by check list. The second stage was a qualitative survey with a semi-structured interview as the study tool. A total of 15 users and stakeholders' perspectives about workflow of CDSS were studied. Finally workflow of DSS of control program was designed by standard clinical practice guidelines and perspectives. Results: Screening minimum data set of national colorectal cancer screening program was defined in five sections, including colonoscopy data set, surgery, pathology, genetics and pedigree data set. Deficiencies and information gaps were analyzed. Then we designed a work process standard of screening. Finally workflow of DSS and entry stage were determined. Conclusions: A CDSS facilitates complex decision making for screening and has key roles in designing optimal interactions between colonoscopy, pathology and laboratory departments. Also workflow analysis is useful to identify data reconciliation strategies to address documentation gaps. Following recommendations of CDSS should improve quality of colorectal cancer screening.

• Korean Journal of Digital Imaging in Medicine
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• v.9 no.2
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• pp.5-9
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• 2007

As developing the medical treatment image portion with the change of these times, PACS, which is able to digitalize image portion data, has a lot of data-based image data. Applying this PACS, we would like to settle down RNSXI(real-name shooting X -ray of inspector) system. We interviewed with P ACS's operators of university hospitals which is using PACS in Seoul about the present conditions whether using of RNSXI or not. And we inquired the RNSXI equipments, applying PACS database, and Interface conditions undertook to do in our hospital. All university hospitals in Seoul are set up the P ACS system. But no hospital use the RNSXI. In our hospital, we can check inspector' name or initials who exposure x-ray with the PACS Viewer by looking over equipments(CR, DR, US, MG, MR, CT) and Interface of the DICOM Header data. However, some equipments like RF and Angio can not check inspector' name or initials. Under the Film/System environment, RNSXI system has been used frequently like that inspector's signature or initial added to a patient data. Though the digital medical treatment was developed, RNSXI system was declined. It is necessary to using RNSXI system in order to improving radiologists' rights, even if it is not under the application of the medical treatment image laws. If RNSXI system use, radiologists should specialize in their major and the Repeat rate should be reduced. In environment of PACS, RNSXI system can be used by linking both the equipments and the Interface with a production enterprise of P ACS. Therefore RNSXI system applying the P ACS datebase should settle down in our medical system for being provided lots of data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.12-14
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• 2021

For the past couple of years, the medical data has been stored in centralized systems which is not the ideal storage technique since all data can be altered, stolen, or even used for evil purposes and, furthermore, the data cannot be safely shared with other doctors and hospitals in case of patient's transfer, change of state or country, in addition, patient's health status cannot be tracked and the patient's medical history is unknown. Therefore, powerful decentralized technologies and expertise can help provide better health information and help doctors and patients to better understand the situations before and after treatment, and do more research based on immutable and trusted data. One of the proposed solutions is storing and securing data on the blockchain which is less scalable, slow and expensive. Introducing a scalable, robust medical data storage and sharing system based on AI/ML, IoT, IPFS, and blockchain.

• Progress in Medical Physics
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• v.28 no.2
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• pp.49-53
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• 2017

The energy distribution was calculated for an electron beam from an electron linear accelerator developed for medical applications using computational methods. The depth dose data for monoenergetic electrons from 0.1 MeV to 8.0 MeV were calculated by the DOSXYZ/nrc code. The calculated data were used to generate the energy distribution from the measured depth dose data by numerical iterations. The measured data in a previous work and an in-house computer program were used for the generation of energy distribution. As results, the mean energy and most probable energy of the energy distribution were 5.7 MeV and 6.2 MeV, respectively. These two values agreed with those determined by the IAEA dosimetry protocol using the measured depth dose.

• Health Policy and Management
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• v.6 no.2
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• pp.115-130
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• 1996

This study was performed to investigate the practical oriental medical expenses by the use of internal data of an oriental hospital due to the bias of medical insurance program data. The purpose of this study was to describe prevalent diseases of clinical department in the studied hospital, to analyze medical expenses structure and to verify the each cost share ration of expenses on insurer to insuree. Under this purpose, we analyzed actual medical expenses data of 1,611 hospitalized patients of the oriental medical university hospital with 150 beds that can be approached to internal data from Jan. 1, 1994 to Dec. 31, 1994. The major findings are as follows : 1. Upper five of most frequent diseases of admitted patients were Joul-Jung-Pung(55.5%), Yoo-Kak-Tong(7.3%), Yoo-/Tong(7.1%), Gu-An-Wa-Sa(2.7%) and sequale of Joul- Jung-Pung(2.4%) 2. In medical expenses structure, hospital ward fee was 47.1%, medication fee 41.3%, fee for procedure(acupuncture, moxibustion, negative therapy, physical therapy, etc) 11.1% and consultation fee 0.5%. In addition to the cost share ration of insuree & that of insurer was 75:25 respectly.

• Korea Journal of Hospital Management
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• v.28 no.3
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• pp.1-14
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• 2023

Purposes: It is very important to establish a clinical data warehouse based on a common data model to offset the different data characteristics of each medical institution and for drug surveillance. This study attempted to establish a clinical data warehouse for Dankook university hospital for drug surveillance, and to derive the main items necessary for development. Methodology/Approach: This study extracted the electronic medical record data of Dankook university hospital tracked for 9 years from 2013 (2013.01.01. to 2021.12.31) to build a clinical data warehouse. The extracted data was converted into the Observational Medical Outcomes Partnership Common Data Model (Version 5.4). Data term mapping was performed using the electronic medical record data of Dankook university hospital and the standard term mapping guide. To verify the clinical data warehouse, the use of angiotensin receptor blockers and the incidence of liver toxicity were analyzed, and the results were compared with the analysis of hospital raw data. Findings: This study used a total of 670,933 data from electronic medical records for the Dankook university clinical data warehouse. Excluding the number of overlapping cases among the total number of cases, the target data was mapped into standard terms. Diagnosis (100% of total cases), drug (92.1%), and measurement (94.5%) were standardized. For treatment and surgery, the insurance EDI (electronic data interchange) code was used as it is. Extraction, conversion and loading were completed. R language-based conversion and loading software for the process was developed, and clinical data warehouse construction was completed through data verification. Practical Implications: In this study, a clinical data warehouse for Dankook university hospitals based on a common data model supporting drug surveillance research was established and verified. The results of this study provide guidelines for institutions that want to build a clinical data warehouse in the future by deriving key points necessary for building a clinical data warehouse.