Jun-Nyoung Heo

Background and Purpose- The prediction of long-term outcomes in ischemic stroke patients may be useful in treatment decisions. Machine learning techniques are being increasingly adapted for use in the medical field because of their high accuracy. This study investigated the applicability of machine learning techniques to predict long-term outcomes in ischemic stroke patients. Methods- This was a retrospective study using a prospective cohort that enrolled patients with acute ischemic stroke. Fav

BACKGROUND: Prioritizing patients in need of intensive care is necessary to reduce the mortality rate during the COVID-19 pandemic. Although several scoring methods have been introduced, many require laboratory or radiographic findings that are not always easily available. OBJECTIVE: The purpose of this study was to develop a machine learning model that predicts the need for intensive care for patients with COVID-19 using easily obtainable characteristics-baseline demographics, comorbidities, an

BACKGROUND: Unavailability or saturation of the intensive care unit may be associated with the fatality of COVID-19. Prioritizing the patients for hospitalization and intensive care may be critical for reducing the fatality of COVID-19. This study aimed to develop and validate a new integer-based scoring system for predicting patients with COVID-19 requiring intensive care, using only the predictors available upon triage. METHODS: This is a retrospective study using cohort data from the Korean C

BACKGROUND: COVID-19 has officially been declared as a pandemic, and the spread of the virus is placing sustained demands on public health systems. There are speculations that the COVID-19 mortality differences between regions are due to the disparities in the availability of medical resources. Therefore, the selection of patients for diagnosis and treatment is essential in this situation. Military personnel are especially at risk for infectious diseases; thus, patient selection with an evidence

Artificial intelligence (AI) is revolutionizing stroke care by enhancing diagnosis, treatment, and outcome prediction. This review examines 505 original studies on AI applications in ischemic stroke, categorized into outcome prediction, stroke risk prediction, diagnosis, etiology prediction, and complication and comorbidity prediction. Outcome prediction, the most explored category, includes studies predicting functional outcomes, mortality, and recurrence, often achieving high accuracy and outp

BACKGROUND: We aimed to develop and validate machine learning models to diagnose patients with ischemic stroke with cancer through the analysis of histopathologic images of thrombi obtained during endovascular thrombectomy. METHODS: This was a retrospective study using a prospective multicenter registry which enrolled consecutive patients with acute ischemic stroke from South Korea who underwent endovascular thrombectomy. This study included patients admitted between July 1, 2017 and December 31

BACKGROUND: Clear guidelines for a patient with suspected COVID-19 infection are unavailable. Many countries rely on assessments through a national hotline or telecommunications, but this only adds to the burden of an already overwhelmed health care system. In this study, we developed an algorithm and a web application to help patients get screened. OBJECTIVE: This study aims to aid the general public by developing a web-based application that helps patients decide when to seek medical care duri

This study aimed to investigate whether there was a difference in one-year outcome after stroke between patients treated with antiplatelet and anticoagulation (OAC + antiplatelet) and those with anticoagulation only (OAC), when comorbid atherosclerotic disease was present with non-valvular atrial fibrillation (NVAF). This was a retrospective study using a prospective cohort of consecutive patients with ischemic stroke. Patients with NVAF and comorbid atherosclerotic disease were assigned to the

BACKGROUND AND PURPOSE: Left atrial or left atrial appendage (LA/LAA) thrombi are frequently observed during cardioembolic evaluation in patients with ischemic stroke. This study aimed to investigate stroke outcomes in patients with LA/LAA thrombus. METHODS: This retrospective study included patients admitted to a single tertiary center in Korea between January 2012 and December 2020. Patients with nonvalvular atrial fibrillation who underwent transesophageal echocardiography or multi-detector c

Introduction: Prediction of outcome in stroke patients can help both physicians and patients in making treatment decisions and managing prognostic expectations. Machine learning techniques are being increasingly used in the field of medical research. Hypothesis: We hypothesized that models developed with machine learning techniques are useful for predicting long-term functional outcomes in patients with acute ischemic stroke. Methods: The model was developed with a prospective cohort of acute is

Abstract Background Unavailability or saturation of the intensive care unit may be associated with the fatality of COVID-19. Prioritizing the patients for hospitalization and intensive care may be critical for reducing the fatality of COVID-19. This study aimed to develop and validate a new integer-based scoring system for predicting patients with COVID-19 requiring intensive care, using only the predictors available upon triage. Methods This is a retrospective study using cohort data from the K

Background Non-contrast CT (NCCT) is widely used imaging modality for acute stroke imaging but often fails to detect subtle early ischemic changes. Such underestimation can lead clinicians to overlook tissue-level information. This study aimed to develop and externally validate automated software for detecting ischemic lesions on NCCT and to assess its clinical feasibility in stroke patients undergoing endovascular thrombectomy. Methods In this retrospective, multicenter cohort study (May 2011–A