Publications by authors named "Jeong Rye Park"
Corrigendum: Detection of COVID-19 epidemic outbreak using machine learning.
Front Public Health
February 2024
[This corrects the article DOI: 10.3389/fpubh.2023.
View Article and Find Full Text PDFBackground: The coronavirus disease (COVID-19) pandemic has spread rapidly across the world, creating an urgent need for predictive models that can help healthcare providers prepare and respond to outbreaks more quickly and effectively, and ultimately improve patient care. Early detection and warning systems are crucial for preventing and controlling epidemic spread.
Objective: In this study, we aimed to propose a machine learning-based method to predict the transmission trend of COVID-19 and a new approach to detect the start time of new outbreaks by analyzing epidemiological data.
Although deep learning architecture has been used to process sequential data, only a few studies have explored the usefulness of deep learning algorithms to detect glaucoma progression. Here, we proposed a bidirectional gated recurrent unit (Bi-GRU) algorithm to predict visual field loss. In total, 5413 eyes from 3321 patients were included in the training set, whereas 1272 eyes from 1272 patients were included in the test set.
View Article and Find Full Text PDFArticle Synopsis
- The study aimed to evaluate the impact of data preprocessing and augmentation on the performance of recurrent neural networks (RNN) in predicting visual field (VF) outcomes using a large multi-central dataset.
- It utilized data from five glaucoma clinics, resulting in a total of 5,430 and 13,747 visual fields by standardizing test intervals to 365 days and 180 days, respectively, for patients with multiple VF tests.
- The findings revealed that a periodic RNN model significantly outperformed an aperiodic model in prediction accuracy, indicating that a higher frequency of perimetric tests and an increased number of input VFs led to improved predictions, particularly with the 6-long short-term memory (LSTM) setup.