Development and Validation of a Real-Time Service Model for Noise Removal and Arrhythmia Classification Using Electrocardiogram Signals.

Arrhythmias range from mild nuisances to potentially fatal conditions, detectable through electrocardiograms (ECGs). With advancements in wearable technology, ECGs can now be monitored on-the-go, although these devices often capture noisy data, complicating accurate arrhythmia detection. This study aims to create a new deep learning model that utilizes generative adversarial networks (GANs) for effective noise removal and ResNet for precise arrhythmia classification from wearable ECG data.

Factors Predicting Older People's Acceptance of a Personalized Health Care Service App and the Effect of Chronic Disease: Cross-Sectional Questionnaire Study.

Background: Mobile health (mHealth) services enable real-time measurement of information on individuals' biosignals and environmental risk factors; accordingly, research on health management using mHealth is being actively conducted.

Objective: The study aims to identify the predictors of older people's intention to use mHealth in South Korea and verify whether chronic disease moderates the effect of the identified predictors on behavioral intentions.

Methods: A cross-sectional questionnaire study was conducted among 500 participants aged 60 to 75 years.

Evaluation of an air quality warning system for vulnerable and susceptible individuals in Korea: an interrupted time series analysis.

Objectives: This study was conducted to elucidate the effects of an air quality warning system (AQWS) implemented in January 2015 in Korea by analyzing changes in the incidence and exacerbation rates of environmental diseases.

Methods: Data from patients with environmental diseases were extracted from the National Health Insurance Service-National Sample Cohort database from 2010 to 2019, and data on environmental risk factors were acquired from the AirKorea database. Patient and meteorological data were linked based on residential area.

Which indicator should be used? A comparison between the incidence and intensity of catastrophic health expenditure: using difference-in-difference analysis.

Background: Catastrophic health expenditure (CHE) represents out-of-pocket payment as a share of household income. Most previous studies have focused on incidence aspects when assessing health policy effects. However, because CHE incidence is a binary variable, the effect of the health policy could not accurately be evaluated.

Is Avoidable Hospitalization Experienced Prior to Infection Associated With COVID-19-Related Deaths?

This study aimed to determine the effect of the presence or absence of avoidable hospitalization before acquiring coronavirus disease (COVID-19) on COVID-19-related deaths. This study used the total NHIS-COVID-19 dataset comprising domestic COVID-19 patients, provided by the National Health Insurance Service (NHIS) in South Korea. We conducted logistic regression and double robust estimation (DRE) to confirm the effect of avoidable hospitalization on COVID-19-related deaths.

Chronic Disease Management for People With Hypertension.

To assess the effectiveness of continuity of care policies by identifying the impact of a chronic disease management program on the continuity of care in patients with hypertension in South Korea. The propensity score matching method was used to control selection bias, and the difference-in-differences method was used to compare the impact on the treatment and control groups according to the policy intervention. The continuity of care index of hypertensive patients using the difference-in-differences analysis outcome of the chronic disease management program was higher than that of the non-participating hypertensive patients.