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Acute coronary syndrome (ACS) has been one of the most important issues in global public health. The high recurrence risk of patients with coronary heart disease (CHD) has led to the importance of post-discharge care and secondary prevention of CHD. Previous studies provided binary results of ACS recurrence risk; however, studies providing the recurrence risk of an individual patient are rare. In this study, we conducted a model which provides the recurrence risk probability for each patient, along with the binary result, with two datasets from the Korea Health Insurance Review and Assessment Service and Chungbuk National University Hospital. The total data of 6,535 patients who had been diagnosed with ACS were used to build a machine learning model by using logistic regression. Data including age, gender, procedure codes, procedure reason, prescription drug codes, and condition codes were used as the model predictors. The model performance showed 0.893, 0.894, 0.851, 0.869, and 0.921 for accuracy, precision, recall, F1-score, and AUC, respectively. Our model provides the ACS recurrence probability of each patient as a personalized ACS recurrence risk, which may help motivate the patient to reduce their own ACS recurrence risk. The model also shows that acute transmural myocardial infarction of an unspecified site, and other sites and acute transmural myocardial infarction of an unspecified site contributed most significantly to ACS recurrence with an odds ratio of 97.908 as a procedure reason code and with an odds ratio of 58.215 as a condition code, respectively.

Validating and improving the quality of global address data are important tasks in a modern society where exchanges between countries are due to active Free Trade Agreements (FTAs) and e-commerce. Addresses may be constructed with different systems for each country; therefore, to verify and improve the quality of the address data, it is necessary to understand the address system of each country in advance. In the event of food risk, it is important to identify the administrative district from the address in order to take safety measures, such as predicting the contaminated area by tracking the distribution of food in the area. In this study, we propose a method that applies a deep learning approach to verify and improve the quality of the global address data required for imported food-safety management. The address entered by the user is classified to the administrative division levels of the relevant country and the quality of the address data is verified and improved by converting them into a standardized address. Finally, the results show that the accuracy of the model is found to be approximately 90% and the proposed method is able to verify and evaluate the overseas address data quality significantly.

Building a blockchain network is time-consuming and challenging. It requires extensive information technology (IT) knowledge and relevant skills. To simplify this task, global IT companies provide cloud-based blockchain services. In this paper, we proposed a cloud-based construction and management tool called smart blockchain network constructor (SBC) that could allow blockchain developers, operators and enterprises to easily deploy blockchain networks within their infrastructure and save time and cost. SBC employs Hyperledger Fabric, a well-known private blockchain platform. Ansible is an open-source IT automation engine that could support network-wide deployment. Instead of complex and repetitive text commands, SBC provides a user-friendly web interface that allows users to setup, deploy, and interact seamlessly with blockchain networks. To verify its usefulness and convenience, a blockchain network that could conduct food safety checks and an electronic voting decentralized app were built and tested with SBC. The process of blockchain network construction, which involves writing more than ten settings files and executing numerous command lines, can be replaced with simple input and click operations from a graphical user interface. A Hyperledger Fabric channel was used to enable independent and reliable tasks in both food safety and smart election tasks. As a result, an automated system was created to simulate the necessary manual work in constructing blockchain networks. We observed that the proposed automated system dramatically reduced the time consumption of blockchain network construction compared to manual construction. We strongly believe that this system will be highly beneficial to both blockchain operators and developers for their applications built on blockchain technology. [ABSTRACT FROM AUTHOR]