Ewha Womans University · Medicine
Professor Myeong Gyu Kim's research lab specializes in health data science, focusing on the application of artificial intelligence and natural language processing to real-world health data. The lab investigates pharmacovigilance signals using social media, evaluates the metabolic effects of dietary patterns and supplements (such as ketogenic diets and n-3 fatty acids), and combats health misinformation—particularly during public health crises like the COVID-19 pandemic—using advanced NLP models like BERT. A central theme is leveraging digital health data to improve public health surveillance and decision-making.
Figures are computed from collected data and may differ slightly.
Social media, along with conventional pharmacovigilance measures, can be used to detect signals associated with new black box warnings, labeling changes, or withdrawals. Several challenges remain; however, social media will be useful for signal detection of frequently mentioned drugs in specialized health care social networks and forums. Further studies are required to advance natural language processing and mine real-world data on social media.
The SVM classifiers that were built in this study were highly precise and accurate and will help to automatically identify the nonmedical use and side effects of methylphenidate using Twitter.
This study suggests unfavorable effects of KDs on TC and LDL-C in normal-weight adults. Although an increase in HDL-C can compensate for unfavorable changes in lipids, normal-weight individuals should consider the risk of hypercholesterolemia when consuming a KD. Results for triglycerides were inconsistent.
The lipid-modifying effects of krill oil and fish oil do not differ. The reduction in triglycerides depends on the dose of n-3 fatty acids consumed.
Garlic-related misinformation is prevalent whenever a virus outbreak occurs. With the outbreak of COVID-19, garlic-related misinformation is spreading through social media, including Twitter. Bidirectional Encoder Representations from Transformers (BERT) can be used to classify misinformation from a vast number of tweets. This study aimed to apply the BERT model for classifying misinformation on garlic and COVID-19 on Twitter, using 5929 original tweets mentioning garlic and COVID-19 (4151 for f
Coffee is widely consumed worldwide and impacts glucose metabolism. After a previous meta-analysis that evaluated the effects of coffee consumption on insulin resistance and sensitivity, additional randomized controlled trials (RCTs) were conducted. This meta-analysis aimed to evaluate the effects of coffee consumption on insulin resistance or sensitivity. We selected RCTs that evaluated the effects of coffee consumption for seven days or more on insulin sensitivity or resistance using surrogate
This meta-analysis was conducted to derive an integrated conclusion about the influence of glutathione S-transferase (GST) genetic polymorphisms on busulfan pharmacokinetic (PK) parameters and veno-occlusive disease (VOD). Studies which analysed the effect of GST genetic polymorphisms on area under the curve (AUC), clearance (CL) or VOD were searched for and selected. A pooled analysis was conducted using Comprehensive Meta-Analysis programme. Nineteen studies were included in this meta-analysis
Uridine 5'-diphospho-glucuronosyltransferases (UGTs) are involved in the metabolism of lamotrigine, but whether the UGT1A4 and UGT2B7 genetic polymorphisms affect lamotrigine concentration remains controversial. Thus, the objective of this meta-analysis was to analyse the influence of UGT1A4 and UGT2B7 genetic polymorphisms on lamotrigine concentration. Through searching, screening, selection, data extraction and quantitative analyses, the influence of UGT1A4 and UGT2B7 genetic polymorphisms on
In our study, it was observed that weight had a significant effect on CL. Genetic polymorphisms did not affect CsA pharmacokinetics. Prospective studies with a larger number of participants is needed to validate the results of this study.
This case-non-case study aims to detect signals not currently listed on cephalosporin drug labels. From 2009 to 2018, adverse event (AE) reports concerning antibacterial drugs (anatomical therapeutic chemical (ATC) code J01) in the Korea Adverse Events Reporting System (KAERS) database were examined. For signal detection, three indices of disproportionality, proportional reporting ratio (PRR), reporting odds ratio (ROR), and information component (IC), were calculated. The list of signals was co
A survey of CRCs revealed the reasons governing patient participation and withdrawal in cancer clinical trials, thereby providing a novel insight into strategies for promoting subject enrollment and reducing withdrawal from cancer clinical trials.
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