Publications by authors named "Changi Kim"
Article Synopsis
- - The study aimed to develop and validate a deep learning model that predicts mortality in ischemic stroke patients by incorporating brain diffusion weighted imaging (DWI), apparent diffusion coefficient (ADC), and clinical factors.
- - Data from a large group of stroke patients was divided into training, validation, and testing sets, with a new integrated model created that combined radiological and clinical data.
- - The improved integrated model outperformed previous prediction methods, showing strong potential for accurately identifying high-risk patients within one year of their stroke.
This study aimed to develop and validate an automated machine learning (ML) system that predicts 3-month functional outcomes in acute ischemic stroke (AIS) patients by combining clinical and neuroimaging features. Functional outcomes were categorized as unfavorable (modified Rankin Scale ≥ 3) or not. A clinical model employing optimal clinical features (Model_A), a convolutional neural network model incorporating imaging data (Model_B), and an integrated model combining both imaging and clinical features (Model_C) were developed and tested to predict unfavorable outcomes.
View Article and Find Full Text PDFChest radiography is an essential tool for diagnosing community-acquired pneumonia (CAP), but it has an uncertain prognostic role in the care of patients with CAP. The purpose of this study was to develop a deep learning (DL) model to predict 30-day mortality from diagnosis among patients with CAP by use of chest radiographs to validate the performance model in patients from different time periods and institutions. In this retrospective study, a DL model was developed from data on 7105 patients from one institution from March 2013 to December 2019 (3:1:1 allocation to training, validation, and internal test sets) to predict the risk of all-cause mortality within 30 days after CAP diagnosis by use of patients' initial chest radiographs.
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- There are diverse bacterial communities in rice seeds that are passed down from parent plants to their offspring, influencing the endophyte composition.
- A study found that the richness, evenness, and diversity of these bacterial communities can change over generations due to crossbreeding, inbreeding, and environmental factors.
- Key bacterial groups like Herbaspirillum, Microbacterium, and others may form a "core microbiota," suggesting they consistently play a significant role in shaping the endophytic communities in rice seeds across different locations and generations.
We summarize our experience and propose methods for early diagnosis and treatment of intravascular large B cell lymphoma (IVL). A total of 16 patients with IVL between 1994 and 2007 were included and analyzed in this study. Predicted survival durations were short until September 2003.
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