Background: Myocarditis can be classified into 2 categories: fulminant myocarditis (FM) and nonfulminant myocarditis. FM is the most severe type, characterized by its acute and explosive nature, posing a sudden and life-threatening risk with a high fatality rate. Limited research has been conducted on FM characteristics using cluster analysis. This study introduces the following-leading clustering algorithm (`) as a unique method and utilizes it to generate a dual map and timeline view of FM themes, aiming to gain a better understanding of FM. Methods: The metadata were obtained from the Web of Science (WoS) database using an advanced search strategy based on the topic (TS= ((“Fulminant”) AND (“Myocarditis”))). The analysis comprised 3 main components: descriptive analytics, which involved identifying the most influential entities using CJAL scores and analyzing publication trends, author collaborations using the FLCA algorithm, and generating a dual map and timeline view of FM themes using the FLCA algorithm. The visualizations included radar plots divided into 4 quadrants, stacked bar and line charts, network charts, chord diagrams, a dual map overlay, and a timeline view. Results: The findings reveal that the prominent entities in terms of countries, institutes, departments, and authors were the United States, Huazhong University of Science and Technology (China), Cardiology, and Enrico Ammirati from Italy. A dual map, based on the research category, was created to analyze the relationship between citing and cited articles. It showed that articles related to cells and clinical medicine/surgery were frequently cited by articles in the fields of general health/public/nursing and clinical medicine/surgery. Additionally, a visual timeline view was presented on Google Maps, showcasing the themes extracted from the top 100 cited articles. These visualizations were successfully and reliably generated using the FLCA algorithm, offering insights from various perspectives. Conclusion: A new FLCA algorithm was utilized to examine bibliometric data from 1989 to 2022, specifically focusing on FM. The results of this analysis can serve as a valuable guide for researchers, offering insights into the thematic trends and characteristics of FM research development. This, in turn, can facilitate and promote future research endeavors in this field.

Background: The US Centers for Disease Control and Prevention (CDC) regularly issues “travel health notices” that address disease outbreaks of novel coronavirus disease (COVID)-19 in destinations worldwide. The notices are classified into 3 levels based on the risk posed by the outbreak and what precautions should be in place to prevent spreading. What objectively observed criteria of these COVID-19 situations are required for classification and visualization? This study aimed to visualize the epidemic outbreak and the provisional case fatality rate (CFR) using the Rasch model and Bayes's theorem and developed an algorithm that classifies countries/regions into categories that are then shown on Google Maps. Methods: We downloaded daily COVID-19 outbreak numbers for countries/regions from the GitHub website, which contains information on confirmed cases in more than 30 Chinese locations and other countries/regions. The Rasch model was used to estimate the epidemic outbreak for each country/region using data from recent days. All responses were transformed by using the logarithm function. The Bayes's base CFRs were computed for each region. The geographic risk of transmission of the COVID-19 epidemic was thus determined using both magnitudes (i.e., Rasch scores and CFRs) for each country. Results: The top 7 countries were Iran, South Korea, Italy, Germany, Spain, China (Hubei), and France, with values of {4.53, 3.47, 3.18, 1.65, 1.34 1.13, 1.06} and {13.69%, 0.91%, 47.71%, 0.23%, 24.44%, 3.56%, and 16.22%} for the outbreak magnitudes and CFRs, respectively. The results were consistent with the US CDC travel advisories of warning level 3 in China, Iran, and most European countries and of level 2 in South Korea on March 16, 2020. Conclusion: We created an online algorithm that used the CFRs to display the geographic risks to understand COVID-19 transmission. The app was developed to display which countries had higher travel risks and aid with the understanding of the outbreak situation.

Background: Studies in the past have identified factors related to the nursing staff’s intention to leave the unit, institution, and profession. However, none has successfully predicted the nurse's intention to quit the job (NIQJ). Whether NIQJ can be predicted be predicted is an interesting topic in healthcare management. A model to predict the NIQJ for novice nurses in hospitals should be investigated and developed in this mobile computer age. Objective: The aim of this study is to build a model to develop an app for automatic prediction and classification of NIQJ using a smaller number of items to help assess NIQJ and take necessary actions before nurses quit the job.Methods: We recruited 1104 novice nurses working in six medical centers in Taiwan to complete 100-item questionnaires related to NIQJ in October 2018. The k-mean was used to divide nurses into two classes (i.e., NIQJ and Non- NIQJ) based on five- NIQJ items regarding leave intention. Feature variables were chosen from 100 relevant items. Two models, including artificial neural network (ANN) and convolutional neural network (CNN), were compared across four scenarios made up by two training sets (n=1104 and n=804B) and their corresponding testing (n=300a) sets to verify the model accuracy (e.g., sensitivity, specificity, area under the receiver operating characteristic curve, AUC) and stability and generalization (e.g., using the training set to predict the testing set). An app predicting NIQJ was then developed involving the model's estimated parameters as a website assessment.Results: We observed that (1) 24 feature variables extracted from this study in ANN model yielded a higher AUC of 0.82 (95% CI 0.80-0.84) based on the total 1104 cases, (2) the ANN performed better than CNN on both accuracy, stability and generalization, and (3) an ready and available app for predicting NIQJ was successfully developed in this study.Conclusions: The 24-item ANN model with the 53 parameters estimated by the ANN for improving the accuracy of NIQJ has been developed with the use of Excel (Microsoft Corp). The app would help team leader and HR department to pick up nurse’s NIQJ before actions are taken, allowing them to make plans accordingly.