No abstract available.
Hepacivirus ; Hepatitis C* ; Hepatitis*

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

PURPOSE: Complex clinical information in cerebral angiointervention unit requires effective management of statistical analysis for the classification of diagnosis and intervention including follow-up data from the interventional treatment. We present an application of Microsoft Access program for the management of patient data in cerebral angiointervention unit which suggests practical methods in recording and analyzing the patient data. MATERIALS AND METHODS: Since January 2002, patient information from cerebral angiointervention was managed by a database with over 4000 patients. We designed a program which incorporates six items; Table, Query, Form, Report, Page and Macro. Patient data, follow-up data and information regarding diagnosis and intervention were established in the Form section, related by serial number, and connected to each other to an independent Table. Problems in running the program were corrected by establishing Entity Relationship (ER) diagrams of Tables to define relationships between Tables. Convenient Queries, Forms and Reports were created to display expected information were applied from selected Tables. RESULTS: The relationship program which incorporated six items conveniently provided the number of cases per year, incidence of disease, lesion site, and case analysis based on interventional treatment. We were able to follow the patients after the interventional procedures by creating queries and reports. Lists of disease and patients files were identified easily each time by the Macro function. In addition, product names, size and characteristics of materials used were indexed and easily available. CONCLUSION: Microsoft Access program is effective in the management of patient data in cerebral angiointervention unit. Accumulation of large amounts of complex data handled by multiple users may require client/sever solutions such as a Microsoft SQL Server.
Classification ; Diagnosis ; Follow-Up Studies ; Humans ; Incidence ; Running ; Statistics as Topic

STUDY DESIGN: A retrospective study was designed to evaluate the clinical result and difference between short segment and long segment fixation, which was undertaken by posterior approach for thoracic and lumbar spine fractures. OBJECTIVE: To determine and compare the mechanical maintenance and ability of correction, and clinical and neurologic recov-ery between short segment and long segment fusion group. SUMMARY OF BACKGROUND DATA: The long segment instrumentation is a cause of decrease of motion segment in thoracic and lumbar spine. In short segment fusion, screw failures were reported. MATERIALS AND METHODS: From 1989 thorough 1997, 54 patients who had been operated on by the posterior approach with transpedicular screw fixation for spine injuries were divided into two groups. The authors applied the short segment transpedic-ular instrumentation including fractured vertebra. Short segment group included 35 cases, and long segment group, 19 cases. The mean follow-up period was one year and eight months for short segment group, two years and seven months for long segment one. The results were evaluated by comparing the anterior vertebral height, sagittal index in simple roentgenogram and neurologic recovery. RESULTS: The average of anterior vertebral height which was 50.7% at preoperation, became 78.7% after the operation and measured 74.9% at final follow-up in long segment fusion group, while in short segment fusion group it was 59.7%, 79.3% and 77.7%, respectively. The average of sagittal index of 17.5degreeat preoperation became 6.7degreeafter the operation, and measured 8degreeat final follow-up in long segment fusion group, while in short segment fusion group it was 19.9degree, 10.4degree, and 12.1degree, respectively. Overall clinical results had no statistical significant difference between two groups. Of the thirty-six patients with neurologic deficits, twenty-two improved by over the one Frankel grade. CONCLUSIONS: The authors conclude that the short segment transpedicular instrumentation including fractured vertebra is a successful method of thoracolumbar and lumbar burst fractures.
Follow-Up Studies ; Humans ; Neurologic Manifestations ; Retrospective Studies ; Spine

Objectives: ：This study aimed to investigate quality of life, severity of depression, suicidality, subjective health and subjective stress of depression with subjective cognitive decline in Korean adults. Methods: ：We used the 7th KNHANES data to enroll 415 participants with a score of 10 or higher on Patient Health Questionnaire-9 (PHQ-9), aged 20-64. Depression was divided into two groups based on the presence/absence of subjective cognitive decline. Demographic and psychological characteristics were compared between two groups. Correlation analysis of subjective cognitive decline, quality of life, depression, suicidal idea was car-ried out. To detect which variables influenced quality of life, a multiple regression analysis was carried out. Results: ：Among the 415 participants, 98 had depression with subjective cognitive decline. We identified sig-nificant differences in age, marital status, education, employment between the two groups. After adjusting for these variables, depression with subjective cognitive decline had lower EuroQol-5D index scores, more severe depressive symptoms without cognition and worse subjective health than depression without cognitive decline. There was a significant correlation between subjective cognitive decline and quality of life (r=-0.236, p<0.001), suicidal idea (r=0.182, p<0.001), depression score without cognition (r=0.108, p=0.028). Through multiple regression analysis, subjective cognitive decline was predictor of reduced quality of life (β=-0.178, p<0.001). Conclusions ：Depression with subjective cognitive decline has poor quality of life and severe depression. Cognitive decline should be considered to improve treatment result in depression.

PURPOSE: The purpose of this study is to investigate differences in organ-specific cancer incidence according to the region and population size in Korea. MATERIALS AND METHODS: We reviewed the data of the cancer registration program of Gyeongnam Regional Cancer Center between 2008 and 2011. Age-standardized rates of cancer incidence were analyzed according to population size of the region and administrative zone. RESULTS: Incidence of thyroid cancer has been increasing rapidly in both urban and rural areas. However, the thyroid cancer incidence was much lower in rural areas than in urban areas and megalopolis such as Seoul. Gastric cancer was relatively more common in rural areas, in megalopolis near the sea (Ulsan, Busan, and Incheon), and other southern provinces (Chungcheongnam-do, Gyeongsangbuk-do, and Gyeongsangnam-do). A detailed analysis in Gyeongsangnam-do revealed that rural areas have relatively low incidence of thyroid and colorectal cancer, and relatively high incidence of gastric and lung cancer compared to urban areas. CONCLUSION: This study suggests that there are some differences in cancer incidence by population size. Thyroid and colorectal cancer incidence was increasing, and gastric and lung cancer was slightly decreasing in urban areas, whereas gastric and lung cancer incidence still remains high in rural areas.
Busan ; Colorectal Neoplasms ; Epidemiology ; Gyeongsangbuk-do ; Gyeongsangnam-do ; Incidence* ; Korea* ; Lung Neoplasms ; Population Density ; Rural Population ; Seoul ; Stomach Neoplasms ; Thyroid Gland ; Thyroid Neoplasms ; Urbanization