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.

Colorectal cancer is the third most common cancer in Korea and the third leading cause of death from cancer. Treatment outcomes for colon cancer are steadily improving due to national health screening programs with advances in diagnostic methods, surgical techniques, and therapeutic agents.. The Korea Colon Cancer Multidisciplinary (KCCM) Committee intends to provide professionals who treat colon cancer with the most up-to-date, evidence-based practice guidelines to improve outcomes and help them make decisions that reflect their patients’ values and preferences. These guidelines have been established by consensus reached by the KCCM Guideline Committee based on a systematic literature review and evidence synthesis and by considering the national health insurance system in real clinical practice settings. Each recommendation is presented with a recommendation strength and level of evidence based on the consensus of the committee.

Objective: Early identification of COVID-19 in patients is important to prevent significant worsening of the disease. This study was undertaken to verify whether MEWS (Modified Early Warning Score), NEWS(National Early Warning Score), ROX index, and CURB-65, which are early diagnostic tools for severe respiratory diseases, could be applied to patients visiting the emergency room for COVID-19. Methods: This retrospective observational study included patients who visited an emergency medical center from September 1 to October 31, 2020, and from January 1 to February 28, 2021. Based on the vital signs and blood tests during the emergency room visit, severity evaluation tools and early diagnostic tools for severe cases were used and compared according to their area under the curve (AUC) values. The primary outcome was in-hospital mortality, while the secondary outcomes were intensive care unit admission rate and the need for mechanical ventilation based on these four tools (MEWS, NEWS, ROX index, and CURB-65). Results: A total of 667 patients were analyzed. No significant difference was determined between the non-survivor group and survivor group in the MEWS values (P=0.13), but statistically significant differences were observed for NEWS (5 vs. 1, P<0.05), CURB-65 (2 vs. 1, P<0.05), and ROX index (16.61 vs. 23.1, P<0.01). The AUC value of NEWS for death prediction indicated a good predictive power at 0.80, while that of MEWS showed a low predictive power at 0.57, which was statistically significant. Moreover, the AUC values of CURB-65 and ROX index did not differ significantly from values obtained for NEWS. Conclusion As early diagnostic tools for predicting death in COVID-19 patients, NEWS, ROX index, and CURB-65 showed excellent discrimination ability, whereas MEWS showed statistically and significantly lower discrimination ability.

Colonoscopic polypectomy is effective in decreasing the incidence and mortality of colorectal cancer (CRC). Premalignant polyps discovered during colonoscopy are associated with the risk of metachronous advanced neoplasia. Postpolypectomy surveillance is the most important method for managing advanced metachronous neoplasia. A more efficient and evidence-based guideline for postpolypectomy surveillance is required because of the limited medical resources and concerns regarding colonoscopy complications. In these consensus guidelines, an analytic approach was used to address all reliable evidence to interpret the predictors of CRC or advanced neoplasia during surveillance colonoscopy. The key recommendations state that the high-risk findings for metachronous CRC following polypectomy are as follows: adenoma ≥10 mm in size; 3 to 5 (or more) adenomas; tubulovillous or villous adenoma; adenoma containing high-grade dysplasia; traditional serrated adenoma; sessile serrated lesion containing any grade of dysplasia; serrated polyp of at least 10 mm in size; and 3 to 5 (or more) sessile serrated lesions. More studies are needed to fully comprehend the patients who are most likely to benefit from surveillance colonoscopy and the ideal surveillance interval to prevent metachronous CRC.

Palinacousis is a rare auditory phenomenon characterized by the persistence of sounds beyond their actual duration. It has been associated with various brain conditions such as stroke, tumor, and seizure in the temporoparietal lobe. We present a case report of a 43-yearold man who developed palinacousis following cerebral venous thrombosis and seizure with lesions including the left auditory cortex. This case highlights the intriguing relationship between cerebral venous infarction, seizure, and the development of palinacousis in specific brain regions.

Objective: This study examined the characteristics of patients visiting the emergency department (ED) with fever after the chronification of the coronavirus disease 2019 (COVID-19) pandemic. Methods: This retrospective observational study analyzed the medical records of patients who visited the ED with fever from May 1 to October 31, 2021, and the corresponding period in 2019. This study was conducted at a single center in Seoul, Korea. Results: There was no statistical difference in the comorbidities of the patients of the two groups: the AC (after the COVID-19 pandemic) group and the BC (before the COVID-19 pandemic) group. As for the level of consciousness at the time of ED arrival, there was a significantly larger decrease in consciousness (verbal response or less) in the AC group than in the BC group (P=0.002). In the case of the National Early Warning Score (NEWS), the proportion was higher in the AC group in the moderate-risk and high-risk groups (P=0.003). The median time from symptom onset to ED arrival was 15.7 hours in the BC group and 13.8 hours in the AC group, and there was no significant difference (P=0.137). When leaving the ED, the AC group had a higher admission rate to the ward and intensive care unit than the BC group. There was no statistical difference in the in-hospital mortality between the two groups (2.9% and 2.4%, respectively; P=0.62). Conclusion Patients who visited the emergency room with fever after one year of the COVID-19 pandemic showed a similar time from symptom onset to ED arrival compared to patients who visited before the COVID-19 pandemic. In addition, there was no difference in in-hospital mortality among these patients compared to those with fever before the COVID-19 pandemic.