Collecting valid information from electronic sources to detect the potential outbreak of infectious disease is time-consuming and labor-intensive. The automated identification of relevant information using machine learning is necessary to respond to a potential disease outbreak. A total of 2864 documents were collected from various websites and subsequently manually categorized and labeled by two reviewers. Accurate labels for the training and test data were provided based on a reviewer consensus. Two machine learning algorithms—ConvNet and bidirectional long short-term memory (BiLSTM)—and two classification methods—DocClass and SenClass—were used for classifying the documents. The precision, recall, F1, accuracy, and area under the curve were measured to evaluate the performance of each model. ConvNet yielded higher average, min, and max accuracies (87.6%, 85.2%, and 91.1%, respectively) than BiLSTM with DocClass, while BiLSTM performed better than ConvNet with SenClass with average, min, and max accuracies of 92.8%, 92.6%, and 93.3%, respectively. The performance of BiLSTM with SenClass yielded an overall accuracy of 92.9% in classifying infectious disease occurrences. Machine learning had a compatible performance with a human expert given a particular text extraction system. This study suggests that analyzing information from the website using machine learning can achieve significant accuracies in the presence of abundant articles/documents.

Kim YJ, et al. J Clin Neurol. 2020 Oct;16(4):612-623. https://doi.org/10.3988/jcn.2020.16.4.612

Oh C, et al. J Korean Med Sci. 2021 Apr;36(18):e116. https://doi.org/10.3346/jkms.2021.36.e116

Kim M, et al. Korean Circ J. 2024 Jan;54(1):1-12. https://doi.org/10.4070/kcj.2023.0262

Public reporting is a way to promote quality of healthcare. However, evidence supporting improved quality of care using public reporting in patients with acute myocardial infarction (AMI) is disputed. This study aims to describe the impact of public reporting of AMI care on hospital quality improvement in Korea. Patients with AMI admitted to the emergency room with ICD-10 codes of I21.0 to I21.9 as the primary or secondary diagnosis were identified from the national health insurance claims data (2007–2012). Between 2007 and 2012, 43,240/83,378 (51.9%) patients manifested ST segment elevation myocardial infarction (STEMI). Timely reperfusion rate increased (β = 2.78, p = 0.001). The mortality rate of STEMI patients was not changed (β = −0.0098, p = 0.384) but that of NSTEMI patients decreased (β = −0.465, p = 0.001). Public reporting has a substantial impact on the process indicators of AMI in Korea because of the increased reperfusion rate. However, the outcome indicators such as mortality did not significantly change, suggesting that public reporting did not necessarily improve the quality of care.

Purpose To investigate the risk of genitourinary (GU) cancer in kidney transplant recipients (KTRs) compared to that in the general population, focusing on potential risk factors and clinical implications. Methods Using a national cohort of approximately 360,000 individuals, including 31,542 KTRs, we conducted a retrospective analysis of the data from 2007 to 2018. Propensity score matching was used to compare KTRs with a healthy population, adjusting for age, sex, diabetes, hypertension, and hypercholesterolemia. Results We identified a significantly increased risk of GU cancers, particularly bladder and kidney cancers, in KTRs. Multivariate analysis revealed a higher risk of GU cancer associated with kidney transplantation [hazard ratio (HR) 2.133, 95% confidence interval (CI) 1.641–2.772] and hypercholesterolemia (HR 1.725, 95% CI 1.227–2.425), with older age and male sex also being significant risk factors. Conversely, no significant increase in prostate cancer risk was observed in KTRs compared to the general population. Conclusion This national cohort-based study indicated an increased risk of GU cancer in KTRs, underscoring the need for targeted cancer surveillance and pre- and post-transplant counseling. These findings provide valuable insights for the development of cancer surveillance programs for KTRs and highlight the necessity for further research in this field.