Development of Personas and Journey Maps for Artificial Intelligence Agents Supporting the Use of Health Big Data: Human-Centered Design Approach.

Background: The rapid proliferation of artificial intelligence (AI) requires new approaches for human-AI interfaces that are different from classic human-computer interfaces. In developing a system that is conducive to the analysis and use of health big data (HBD), reflecting the empirical characteristics of users who have performed HBD analysis is the most crucial aspect to consider. Recently, human-centered design methodology, a field of user-centered design, has been expanded and is used not only to develop types of products but also technologies and services.

Trends of fear and anger on YouTube during the initial stage of the COVID-19 outbreak in South Korea.

Background: The COVID-19 pandemic has been the most widespread and threatening health crisis experienced by the Korean society. Faced with an unprecedented threat to survival, society has been gripped by social fear and anger, questioning the culpability of this pandemic. This study explored the correlation between social cognitions and negative emotions and their changes in response to the severe events stemming from the COVID-19 pandemic in South Korea.

Exploring the Knowledge Structure and Trends for Severe COVID-19 Risk Factors Using Text Network Analysis.

This study was aimed to identify knowledge structure and trends in severe COVID-19 risk factor using text network analysis. The 22,628 papers published during from January 2020 to December 2021. We analyzed and visualized using Text Rank analyzer and Gephi software.

Identification of Risk Groups for and Factors Affecting Metabolic Syndrome in South Korean Single-Person Households Using Latent Class Analysis and Machine Learning Techniques: Secondary Analysis Study.

Background: The rapid increase of single-person households in South Korea is leading to an increase in the incidence of metabolic syndrome, which causes cardiovascular and cerebrovascular diseases, due to lifestyle changes. It is necessary to analyze the complex effects of metabolic syndrome risk factors in South Korean single-person households, which differ from one household to another, considering the diversity of single-person households.

Objective: This study aimed to identify the factors affecting metabolic syndrome in single-person households using machine learning techniques and categorically characterize the risk factors through latent class analysis (LCA).

Obstacles to Health Big Data Utilization Based on the Perceptions and Demands of Health Care Workers in South Korea: Web-Based Survey Study.

Background: This study focuses on the potential of health big data in the South Korean context. Despite huge data reserves and pan-government efforts to increase data use, the utilization is limited to public interest research centered in public institutions that have data. To increase the use of health big data, it is necessary to identify and develop measures to meet the various demands for such data from individuals, private companies, and research institutes.

Characterization of the pathogenicity of extraintestinal pathogenic Escherichia coli isolates from pneumonia-infected lung samples of dogs and cats in South Korea.

This study aimed to investigate the pathogenicity of extraintestinal pathogenic Escherichia coli (ExPEC) isolated from dog and cat lung samples in South Korea. A total of 101 E. coli isolates were analyzed for virulence factors, phylogroups, and O-serogroups, and their correlation with bacterial pneumonia-induced mortality was elucidated.

Protocol of BEYOND trial: Clinical BEnefit of sodium-glucose cotransporter-2 (SGLT-2) inhibitors in rhYthm cONtrol of atrial fibrillation in patients with diabetes mellitus.

SGLT-2 inhibitor, traditionally used for glycemic control, has several beneficial effects that can help manage heart failure (HF). SGLT-2 inhibitors reduce the risk of cardiovascular mortality in patients with HF. As atrial fibrillation (AF) is closely associated with HF and diabetes mellitus (DM) is a risk factor for AF, we assume that SGLT-2 inhibitors will also show therapeutic benefits regarding AF, especially for rhythm control.

The critical role of job embeddedness: The impact of psychological empowerment and learning orientation on organizational commitment.

Job embeddedness (JE) has been recognized as a key factor to address the issue of employee turnover and employee attitudes. This study explores underlying mechanisms of job embeddedness that link the organizational environment and the individuals' perceptions of the job. Particularly, the effects of psychological empowerment and learning orientation on organizational commitment were examined.

Failure mode and effects analysis of telehealth service of minority elderly for sustainable digital transformation.

Background: Telehealth services are time- and cost-saving solutions for disease management for older adults. Minority older individuals with multiple risk factors have an increasing demand for telehealth services. There are insufficient data on patient safety in telehealth services.

Effects of job embeddedness and nursing working environment on turnover intention among trauma centre nurses: A cross-sectional study.

Aim: To investigate the effects of job embeddedness and nursing working environment on trauma centre nurses' turnover intention.

Background: Trauma centre nurses have higher average turnover intention than hospital nurses. However, factors that increase the turnover intention of trauma centre nurses remain unexplored.

The mediating effect of workplace incivility on organization culture in South Korea: A descriptive correlational analysis of the turnover intention of nurses.

Purpose: The purpose of this study was to understand the mediating effect of workplace incivility on the relationship between nursing organizational culture and turnover intention among nurses.

Design: A descriptive survey was used to collect data. The participants were 170 nurses with more than six months of clinical experience at university hospitals or hospitals with over 500 beds in South Korea.

Identifying the Risk Factors Associated with Nursing Home Residents' Pressure Ulcers Using Machine Learning Methods.

Background: Machine learning (ML) can keep improving predictions and generating automated knowledge via data-driven predictors or decisions.

Objective: The purpose of this study was to compare different ML methods including random forest, logistics regression, linear support vector machine (SVM), polynomial SVM, radial SVM, and sigmoid SVM in terms of their accuracy, sensitivity, specificity, negative predictor values, and positive predictive values by validating real datasets to predict factors for pressure ulcers (PUs).

Methods: We applied representative ML algorithms (random forest, logistic regression, linear SVM, polynomial SVM, radial SVM, and sigmoid SVM) to develop a prediction model (N = 60).

Text network analysis of research topics and trends on global health nursing literature from 1974 -2017.

Aims: The purpose of this study is to examine the relationship between keywords in existing global health nursing studies during 44 years (1974-2017) and to develop schematic diagrams of the relationship between these keywords from a macro perspective. It is to identify the trend of the literature in global health nursing field.

Design: A descriptive bibliometric analysis of publications in global health nursing.

A Technology-Mediated Interventional Approach to the Prevention of Metabolic Syndrome: A Systematic Review and Meta-Analysis.

Background: Technology-mediated interventions help overcome barriers to program delivery and spread metabolic syndrome prevention programs on a large scale. A meta-analysis was performed to evaluate the impact of these technology-mediated interventions on metabolic syndrome prevention.

Methods: In this meta-analysis, from 30 January 2018, three databases were searched to evaluate interventions using techniques to propagate diet and exercise lifestyle programs for adult patients with metabolic syndrome or metabolic risk.

Application of Machine Learning Methods in Nursing Home Research.

A machine learning (ML) system is able to construct algorithms to continue improving predictions and generate automated knowledge through data-driven predictors or decisions. Objective: The purpose of this study was to compare six ML methods (random forest (RF), logistics regression, linear support vector machine (SVM), polynomial SVM, radial SVM, and sigmoid SVM) of predicting falls in nursing homes (NHs). We applied three representative six-ML algorithms to the preprocessed dataset to develop a prediction model ( = 60).

Effects of the e-Motivate4Change Program on Metabolic Syndrome in Young Adults Using Health Apps and Wearable Devices: Quasi-Experimental Study.

Background: The health behaviors of young adults lag behind those of other age groups, and active health management is needed to improve health behaviors and prevent chronic diseases. In addition, developing good lifestyle habits earlier in life could reduce the risk of metabolic syndrome (MetS) later on.

Objective: The aim of this study is to investigate the effects of the e-Motivate4Change program, for which health apps and wearable devices were selected based on user needs.

A Prospective Analysis of Health Information Portals in Four Years.

Objectives: We examined 17 health information portals to determine the status of web-based health information services in the United States (U.S.A.

The Effects of Laughter Therapy for the Relief of Employment-Stress in Korean Student Nurses by Assessing Psychological Stress Salivary Cortisol and Subjective Happiness.

Objectives: Korean student nurses may be exposed to stress caused by their future employment (employment stress). The aim of this study was to investigate the effects of a Laughter Program on psychological stress, by assessing salivary cortisol and the subjective happiness of student nurses in order to relieve employment stress.

Methods: A quasi-experimental, non-equivalent, control-group, and pre-test/post-test was conducted in 4 year student nurses ( = 48) from 2 universities in Korea at a time when participants' final exams and job searches were simultaneously occurring.

Outbreak of African swine fever in South Korea, 2019.

African swine fever, a fatal haemorrhagic disease of swine, was confirmed in domestic pigs for the first time in South Korea in September 2019. The causative virus belonged to the p72 genotype II and had an additional tandem repeat sequence in the intergenic region (IGR) between the I73R and I329L.

Comparison of the effect of propofol and desflurane on S-100β and GFAP levels during controlled hypotension for functional endoscopic sinus surgery: A randomized controlled trial.

Background: Although surgical field visualization is important in functional endoscopic sinus surgery (FESS), the complications associated with controlled hypotension for surgery should be considered. Intraoperative hypotension is associated with postoperative stroke, leading to subsequent hypoxia with potential neurologic injury. We investigated the effect of propofol and desflurane anesthesia on S-100β and glial fibrillary acidic protein (GFAP) levels which are early biomarkers for cerebral ischemic change during controlled hypotension for FESS.

leaf extract facilitates oligodendrocyte development.

Treatment of multiple sclerosis is effective when anti-inflammatory, neuroprotective and regenerative strategies are combined. () has anti-inflammatory, anti-oxidative properties, which may be beneficial for multiple sclerosis. However, there have been no reports on the effects of on myelination, which is critical for regenerative processes.

African Swine Fever Virus in Pork Brought into South Korea by Travelers from China, August 2018.

We tested samples of pork products confiscated from travelers to South Korea for African swine fever virus (ASFV). We detected ASFV in 4 food items confiscated from travelers from Shenyang, China, in August 2018. Surveillance of pork products at country entry points is needed to mitigate the risk for ASFV introduction.

Research topics and trends in medical education by social network analysis.

Background: As studies analyzing the networks and relational structures of research topics in academic fields emerge, studies that apply methods of network and relationship analysis, such as social network analysis (SNA), are drawing more attention. The purpose of this study is to explore the interaction of medical education subjects in the framework of complex systems theory using SNA and to analyze the trends in medical education.

Methods: The authors extracted keywords using Medical Subject Headings terms from 9,379 research articles (162,866 keywords) published in 1963-2015 in PubMed.