• Genomics & Informatics
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• v.11 no.4
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• pp.186-190
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• 2013

The advances in electronic medical records (EMRs) and bioinformatics (BI) represent two significant trends in healthcare. The widespread adoption of EMR systems and the completion of the Human Genome Project developed the technologies for data acquisition, analysis, and visualization in two different domains. The massive amount of data from both clinical and biology domains is expected to provide personalized, preventive, and predictive healthcare services in the near future. The integrated use of EMR and BI data needs to consider four key informatics areas: data modeling, analytics, standardization, and privacy. Bioclinical data warehouses integrating heterogeneous patient-related clinical or omics data should be considered. The representative standardization effort by the Clinical Bioinformatics Ontology (CBO) aims to provide uniquely identified concepts to include molecular pathology terminologies. Since individual genome data are easily used to predict current and future health status, different safeguards to ensure confidentiality should be considered. In this paper, we focused on the informatics aspects of integrating the EMR community and BI community by identifying opportunities, challenges, and approaches to provide the best possible care service for our patients and the population.

• Journal of Physiology & Pathology in Korean Medicine
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• v.35 no.5
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• pp.132-138
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• 2021

Artificial intelligence technology sheds light on new ways of innovating acupuncture research. As acupoint selection is specific to target diseases, each acupoint is generally believed to have a specific indication. However, the specificity of acupoint selection may be not always same with the specificity of acupoint indication. In this review, we propose that the specificity of acupoint indication can be inferred from clinical data using reverse inference. Using forward inference, the prescribed acupoints for each disease can be quantified for the specificity of acupoint selection. Using reverse inference, targeted diseases for each acupoint can be quantified for the specificity of acupoint indication. It is noteworthy that the selection of an acupoint for a particular disease does not imply the acupoint has specific indications for that disease. Electronic medical record includes various symptoms and chosen acupoint combinations. Data mining approach can be useful to reveal the complex relationships between diseases and acupoints from clinical data. Combining the clinical information and the bodily sensation map, the spatial patterns of acupoint indication can be further estimated. Interoperable medical data should be collected for medical knowledge discovery and clinical decision support system. In the era of artificial intelligence, machine learning can reveal the associations between diseases and prescribed acupoints from large scale clinical data warehouse.

• Journal of Computing Science and Engineering
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• v.8 no.2
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• pp.94-106
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• 2014

Schema matching is widely used in many applications, such as data integration, ontology merging, data warehouse and dataspaces. In this paper, we propose a novel matching technique that is based on the order of attributes appearing in the schema structure of query results. The appearance order embodies the extent of the importance of an attribute for the user examining the query results. The core idea of our approach is to collect statistics about the appearance order of attributes from the query logs, to find correspondences between attributes in the schemas to be matched. As a first step, we employ a matrix to structure the statistics around the appearance order of attributes. Then, two scoring functions are considered to measure the similarity of the collected statistics. Finally, a traditional algorithm is employed to find the mapping with the highest score. Furthermore, our approach can be seen as a complementary member to the family of the existing matchers, and can also be combined with them to obtain more accurate results. We validate our approach with an experimental study, the results of which demonstrate that our approach is effective, and has good performance.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.11
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• pp.3465-3472
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• 2009

The aims of this study is to medical record data warehouse which had been collected from hospital information systems. continuous patient 2,118 60.5%, informally patient 1,385 39.5%. In using survival factors sex, age, area, insurance, admission-course, medical treatment, out-patient lesson, out-patient form, conference diagnosis, operation, cancer, medical reservation. As a result of making a predictive modeling using the logistic regression, the fitness of the predictive modeling of informally patient was 66.0% and neural network, the predictive was 66.72% and CHAID, the predictive was 63.25%, which is a data mining. The expected modeling of the informally patients, the hospital through the continuous patient management and trust of hospital.

• The Journal of Korean Medicine
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• v.40 no.4
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• pp.29-40
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• 2019

Objectives: To analyze the prescription frequency of various herbs as either individual or major herbs (in terms of dosage) and their usage patterns in the treatment of different diseases for standardization of traditional Korean medicine. Methods: We analyzed the prescription database of patients at the Pusan National University Korean Medicine Hospital from the date of establishment of the hospital to February 2013. The complete prescription data were extracted from the electronic medical records of patients, and the prescription frequencies of individual herbs, particularly, of major herbs, were analyzed in terms of gender, age, and international classification of diseases (ICD) code. Results: The prescription frequency of individual herbs based on age and gender showed a similar pattern. Herbal mixtures were also distributed in a similar manner. The use of some herbs differed according to age and gender (Table 1.). The herbs that were used at high frequencies for a given ICD code had similar usage patterns in different categories. However, some major herbs in the "Jun (King)" category were used uniquely for a given ICD code (Table 2.). There was significant difference between male and female on ICD code E and N, but the other ICD codes had small differences. The ratio of herbal medicine by gender showed different usage patterns in each gender. Conclusions: The findings of our study provide fundamental data that reflect the real clinical conditions in South Korea, and therefore, can contribute to the standardization of TKM.

• Asian Journal of Business Environment
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• v.7 no.3
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• pp.17-22
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• 2017

Purpose - Clarifying one of the biggest public Hospital Costs and incomes according to patients' case mix. It leads to prepare financial information about pubic medical tariffs and hospital operational costs. Research design, data, and methodology - This study calculates the costs both, with and without taking into account capital costs. This holds for comparison of hoteling based on case mix in all medical procedures. The checklists were reviewed and filled by reviewing accounting documents of the hospital, warehouse exclusion list, and daily books of laundry and CSR. Data was analyzed descriptively by using Excel. Results - In both cases, the hospital is losing in terms of hoteling. Because the buildings and equipment are new, this loss is not tangible. However, this will be revealed when costs of reconstruction and replacement of equipment. The loss rate per day of hospitalization was 569318 Rials for Coronary Care Unit (CCU), 528171 Rials for Post Intensive Care Unit (Post ICU), 474570 Rials for ICU, 233183 Rials for Post CCU and 204803 for Surgical ward. Conclusions - Income of hoteling was lower than its costs. ANOVA showed a strong relationship between case mix and hospital costs as well as case mix and its income. This suggests that optimal case mix can minimize the costs and maximize income.

• Proceedings of the Korea Association of Information Systems Conference
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• 2000.05a
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• pp.53-63
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• 2000

의사결정 지원을 위해 각광받고 있는 데이터 웨어하우스 시스템은 주제 지향적, 통합적, 시계열적, 비휘발적인 데이터 저장공간을 보유하여 사용자가 쉽게 데이터에 접근하여 원하는 분석을 수행할 수 있도록 도와주는 고품질의 정보제공 시스템이다. 본 논문에서는 구체적인 데이터 웨어하우스 시스템을 소개함에 있어 국내 병원의 건강진단 데이터 웨어하우스 시스템 프로토타입 개발을 시도한다. 이에 데이터 웨어하우스 시스템의 구성에 대해 살펴본 후, 데이터 웨어하우스 시스템 개발 툴인 Cognos사의 PowerPlay를 이용하여 건강진단 데이터 웨어하우스 시스템 개발을 시도하고, 그 구축 방법과 결과를 소개한다.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06c
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• pp.184-188
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• 2008

최근 국내 디지털 병원들이 점차 기업화 되면서 각 지역별 분산 및 독립 운영되는 의료통계 정보 활용의 중요성이 증대되고 있다. 또한 각종 연구목적 및 의료 서비스 경쟁력 향상 등을 위해 신속 정확한 의사결정지원 시스템인 데이터 웨어하우스(DW; Data Warehouse) 구축의 필요성이 대두되고 있다. 본 논문에서는 단일 병원 내의 데이터 웨어하우스가 아닌, 전국적으로 분산 운영되고 있는 병원의 다양한 의료정보를 통합하고자 한다. 따라서 Staging 영역을 활용한 분산된 의료정보시스템 통합 방안을 제시한다.

• Journal of Society of Preventive Korean Medicine
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• v.13 no.3
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• pp.19-28
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• 2009

Background : Recently, Medical Act was amended to encourage the induction of combined treatment between Oriental Medicine doctors and Western doctors. As yet, the information infra for combined treatment has not been studied. Objectives : This study aimed to design the architecture of information system for combined treatment of the Oriental and the Western Medicine. Methods : First, we defined the information of combined treatment through the analysis of research trends from the inside and outside of the country. Because the data compatibility is very important, the definition of information must be ahead of anything else. Second, we designed the architecture of information system based on the prior definition. Results : We classified the information for combined treatment by subject such as law, clinic, research, manpower, facilities, and education. In this paper information system examined in three aspects. First the infra layer is organized as hardware, netware, and security. Second is data warehouse layer for the storing, filtering, and extraction of data. Third is service layer which is related to data transmission. And Finally all information for combined treatment is provided through the portal system for medical consumer, political planner, and R&D researcher. Conclusion : In this paper, we studied the essential factors of combined treatment information in the view point of information system. But the detailed design and implementation of information system must be followed to effect this results.

• Journal of Cancer Prevention
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• v.23 no.4
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• pp.183-190
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• 2018

Background: As the number of big-cohort studies increases, validation becomes increasingly more important. We aimed to validate administrative database categorized as colorectal cancer (CRC) by the International Classification of Disease (ICD) 10th code. Methods: Big-cohort was collected from Clinical Data Warehouse using ICD 10th codes from May 1, 2003 to November 30, 2016 at Seoul National University Bundang Hospital. The patients in the study group had been diagnosed with cancer and were recorded in the ICD 10th code of CRC by the National Health Insurance Service. Subjects with codes of inflammatory bowel disease or tuberculosis colitis were selected for the control group. For the accuracy of registered CRC codes (C18-21), the chart, imaging results, and pathologic findings were examined by two reviewers. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for CRC were calculated. Results: A total of 6,780 subjects with CRC and 1,899 control subjects were enrolled. Of these patients, 22 subjects did not have evidence of CRC by colonoscopy, computed tomography, magnetic resonance imaging, or positron emission tomography. The sensitivity and specificity of hospitalization data for identifying CRC were 100.00% and 98.86%, respectively. PPV and NPV were 99.68% and 100.00%, respectively. Conclusions: The big-cohort database using the ICD 10th code for CRC appears to be accurate.