Publications by authors named "SeJin Heo"

This study aimed to develop and evaluate an artificial intelligence model to predict 28-day mortality of pneumonia patients at the time of disposition from emergency department (ED). A multicenter retrospective study was conducted on data from pneumonia patients who visited the ED of a tertiary academic hospital for 8 months and from the Medical Information Mart for Intensive Care (MIMIC-IV) database. We combined chest X-ray information, clinical data, and CURB-65 score to develop three models with the CURB-65 score as a baseline.

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This study aimed to develop and validate a transformer-based early warning score (TEWS) system for predicting adverse events (AEs) in the emergency department (ED). We conducted a retrospective study analyzing adult ED visits at a tertiary hospital. The TEWS was developed to predict five AEs within 24 h: vasopressor use, respiratory support, intensive care unit admission, septic shock, and cardiac arrest.

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Objectives: This study aimed to explore communication challenges between parents and healthcare providers in paediatric emergency departments (EDs) and to define the roles and functions of an artificial intelligence (AI)-assisted communication agent that could bridge existing gaps.

Design: A qualitative study using in-depth interviews and affinity diagram methodology to analyse interview data.

Setting: A tertiary paediatric ED in South Korea.

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: This study evaluates the impact of temporary telemedicine implementation on primary care visits, which surged during the COVID-19 pandemic in South Korea. : This study was conducted using national claims data from February 24, 2020 to February 23, 2021. The study included 1,926,300 patients with acute mild respiratory diseases and 1,031,174 patients with acute mild gastrointestinal diseases.

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Objective: To develop a scale to predict refractory septic shock (SS) based on clinical variables recorded during initial evaluations of patients.

Methods: Multicenter retrospective study of data for patients with suspected infection registered in the Marketplace for Medical Information in Intensive Care (MIMIC-IV). These data were used for the development and internal validation of the refractory SS scale (RSSS).

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Article Synopsis
  • The study analyzed the effectiveness and safety of telemedicine for chronic diseases in South Korea during the COVID-19 pandemic, focusing on a temporary telemedicine policy.
  • It utilized national health insurance claims data from before and after the policy's implementation, comparing patients who used telemedicine with those who did not across four chronic diseases.
  • Results indicated that telemedicine improved medication adherence for hypertension and diabetes without increasing hospital admissions, while those who did not use telemedicine faced higher admission rates.
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Background: Effective communication between patients and healthcare providers in the emergency department (ED) is challenging due to the dynamic nature of the ED environment. This study aimed to trial a chat service enabling patients in the ED and their family members to ask questions freely, exploring the service's feasibility and user experience.

Objectives: To identify the types of needs and inquiries from patients and family members in the ED that could be addressed through the chat service and to assess the user experience of the service.

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Aim: This study introduces RealCAC-Net, an artificial intelligence (AI) system, to quantify carotid artery compressibility (CAC) and determine the return of spontaneous circulation (ROSC) during cardiopulmonary resuscitation.

Methods: A prospective study based on data from a South Korean emergency department from 2022 to 2023 investigated carotid artery compressibility in adult patients with cardiac arrest using a novel AI model, RealCAC-Net. The data comprised 11,958 training images from 161 cases and 15,080 test images from 134 cases.

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Background: Many cases of deep vein thrombosis (DVT) are diagnosed in the emergency department, and abbreviated lower extremity venous point-of-care ultrasound (POCUS) has already shown an accuracy comparable to that of specialists. This study aimed to identify the learning curve necessary for emergency medicine (EM) residents to achieve expertise-level accuracy in diagnosing DVT through a 3-point lower extremity venous POCUS.

Methods: This prospective study was conducted at an emergency department between May 2021 and October 2022.

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Background: In the wake of challenges brought by the COVID-19 pandemic to conventional medical education, the demand for innovative teaching methods has surged. Nurse training, with its focus on hands-on practice and self-directed learning, encountered significant hurdles with conventional approaches. Augmented reality (AR) offers a potential solution to addressing this issue.

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Article Synopsis
  • The text addresses an error found in a previously published article, specifically the one with DOI: 10.1016/j.lanwpc.2023.100733.
  • It indicates that the correction is being made to ensure the accuracy of the information presented in the article.
  • This update is important for maintaining the integrity of academic research and ensuring that readers have access to the most reliable data.
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Article Synopsis
  • Emergency departments (ED) use triage to prioritize patients, and a new machine learning tool called Score for Emergency Risk Prediction (SERP) was developed to improve this process using data from three Korean hospitals without data sharing.
  • The study analyzed adult emergency visits from 2016 to 2017, focusing on predicting 2-day mortality rates for better patient outcomes.
  • Results indicated that the developed SERP models achieved high accuracy in predicting mortality, with inter-hospital validation showing an area under the ROC curve (AUROC) of at least 0.899, demonstrating effective risk assessment across different hospitals.
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This study aimed to compare the accuracy of real-time trans-tracheal ultrasound (TTUS) with capnography to confirm intubation in cardiopulmonary resuscitation (CPR) while wearing a powered air-purifying respirator (PAPR). This setting reflects increased caution due to contagious diseases. This single-center, prospective, comparative study enrolled patients requiring CPR while wearing a PAPR who visited the emergency department of a tertiary medical center from December 2020 to August 2022.

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Bacteremia is a life-threatening condition that has increased in prevalence over the past two decades. Prompt recognition of bacteremia is important; however, identification of bacteremia requires 1 to 2 days. This retrospective cohort study, conducted from 10 November 2014 to November 2019, among patients with suspected infection who visited the emergency department (ED), aimed to develop and validate a simple tool for predicting bacteremia.

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We sought to determine whether blade size influences the first-pass success (FPS) rate when performing endotracheal intubation (ETI) with a C-MAC video laryngoscope (VL) in emergency department (ED) patients. This single-center, retrospective, observational study was conducted between August 2016 and July 2022. A total of 1467 patients was divided into two categories based on the blade size used during the first ETI attempt: blade-3 (n = 365) and blade-4 groups (n = 1102).

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Article Synopsis
  • To improve resuscitation outcomes during cardiac emergencies, the PReCAP model was created to predict the likelihood of return of spontaneous circulation (ROSC) at the scene of an out-of-hospital cardiac arrest (OHCA).
  • This model analyzes various prehospital data from the Pan-Asian Resuscitation Outcome Study (PAROS) database, which includes a significant number of patients, to provide real-time predictions on key survival metrics.
  • The PReCAP model shows strong predictive capabilities with AUROC scores ranging from 0.80 to 0.93 for different outcomes, indicating its potential use across diverse populations and locations.
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  • The study aimed to create a clinical support system using federated learning to improve the triage process in emergency departments by predicting when a revised Korea Triage Acuity Scale (KTAS) is needed.
  • Researchers analyzed data from over 11 million patients across different levels of emergency medical centers in South Korea, using patient demographics and initial KTAS scores to develop the prediction model.
  • The model showed varying performance levels in predicting triage needs across different cohorts, with higher rates of hospital admissions and mortality among patients with revised KTAS scores.
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  • The study aims to predict latent shock in patients by analyzing sequential changes in vital signs during emergency department visits.
  • Researchers used a large dataset of over 93,000 ED visits and applied various machine learning models, including logistic regression and neural networks, to create and validate their prediction model.
  • The model showed promising results, with AUROC values indicating strong predictive capability, outperforming traditional methods like the shock index in forecasting latent shock up to three hours in advance.
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This pilot study aimed to develop a new, reliable, and easy-to-use method for the evaluation of diastolic function through the M-mode measurement of mitral valve (MV) movement in the parasternal long axis (PSLA), similar to E-point septal separation (EPSS) used for systolic function estimation. Thirty healthy volunteers from a tertiary emergency department (ED) underwent M-mode measurements of the MV anterior leaflet in the PSLA view. EPSS, A-point septal separation (APSS), A-point opening length (APOL), and E-point opening length (EPOL) were measured in the PSLA view, along with the E and A velocities and e' velocity in the apical four-chamber view.

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Background: Field triage is critical in injury patients as the appropriate transport of patients to trauma centers is directly associated with clinical outcomes. Several prehospital triage scores have been developed in Western and European cohorts; however, their validity and applicability in Asia remains unclear. Therefore, we aimed to develop and validate an interpretable field triage scoring systems based on a multinational trauma registry in Asia.

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Various efforts have been made to diagnose acute cardiovascular diseases (CVDs) early in patients. However, the sole option currently is symptom education. It may be possible for the patient to obtain an early 12-lead electrocardiogram (ECG) before the first medical contact (FMC), which could decrease the physical contact between patients and medical staff.

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Background: Although chemotherapy-induced febrile neutropenia (FN) is the most common and life-threatening oncologic emergency, the characteristics and outcomes associated with return visits to the emergency department (ED) in these patients are uncertain. Hence, we aimed to investigate the predictive factors and clinical outcomes of chemotherapy-induced FN patients returning to the ED.

Method: This single-center, retrospective observational study spanning 14 years included chemotherapy-induced FN patients who visited the ED and were discharged.

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Background: This study reports trends in pediatric out-of-hospital cardiac arrest (OHCA) and factors affecting clinical outcomes by age group.

Methods: We identified 4,561 OHCA patients younger than 18 years between January 2009 and December 2018 in the Korean OHCA Registry. The patients were divided into four groups: group 1 (1 year or younger), group 2 (1 to 5 years), group 3 (6 to 12 years), and group 4 (13 to 17 years).

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Synopsis of recent research by authors named "SeJin Heo"

  • - SeJin Heo's recent research focuses on innovative technological applications and methods in emergency medicine, including chat services for improved patient communication, artificial intelligence in carotid artery assessments, and the use of augmented reality in nursing education.
  • - Findings indicate that AI models can enhance clinical decision-making and diagnostics, particularly in critical situations, such as evaluating carotid artery compressibility and predicting outcomes during CPR.
  • - The studies also emphasize the significance of training and improving procedural skills among emergency medicine residents, highlighting learning curves in diagnosing conditions like deep vein thrombosis through point-of-care ultrasound.