Seung-MinJi

0. AbstractObjective In this study, we investigated the effects of endocrine therapy and related drugs on the body composition and bone metabolism of patients with breast cancer. Additionally, using body composition-related indicators in machine learning algorithms, the risks of osteoporosis in patients with breast cancer and healthy women were predicted. Methods We enrolled postmenopausal patie..

0. AbstractOsteoporosis is common in breast cancer patients, but gender-specific research on its incidence and risk factors is limited. This study examined the incidence and risk of osteoporosis in male and female breast cancer patients and analyzed the risk factors for fractures. This nationwide retrospective cohort study used data from the Korean National Insurance database, identifying invasi..

0. AbstractHypertension is an immense challenge in public health. As one of the most prevalent medical conditions worldwide, it is a major cause of premature death. At present, the detection, diagnosis and monitoring of hypertension are subject to several limitations. In this review, we conducted a literature search on blood pressure measurement using only a smartphone, which has the potential t..

0. AbstractElectronic Health Records (EHRs) contain vast clinical data that are difficult for providers to synthesize. Generative AI with Large Language Models (LLMs) can summarize records to reduce cognitive burden, but ensuring accuracy requires reliable evaluation. Human review is the gold standard but is costly and slow. To address this, we introduce and validate an automated LLM-based metho..

0. AbstractThe surveillance protocol for early-stage non–small cell lung cancer (NSCLC) is not contingent upon individualized risk factors for recurrence. This study aimed to use comprehensive data from clinical practice to develop a deep-learning model for practical longitudinal monitoring.A multimodal deep-learning model with transformers was developed for real-time recurrence prediction using..

0. AbstractAs individuals have become overloaded with information, Recommender Systems (RS) were created to provide machine generated recommendations. Significant advancements in RS have been made thanks to Machine Learning methods; Deep Learning (DL) in particular has become extremely popular. Despite the fact that Deep neural networks (DNNs) upgrade notably the performance of RS, they make the..

0. AbstractWith the continuous advancement of artificial intelligence (AI), particularly in widespread domains such as healthcare and environmental applications, there is an increasing demand for model interpretability. Understanding the decisionmaking process of models contributes to building trust in them. Hence, the development of Explainable AI (XAI) has become crucial. This study proposes a..

0. AbstractModel interpretability is crucial for understanding and trusting the decisions made by complex machine learning models, such as those built with XGBoost. SHAP (SHapley Additive exPlanations) values have become a popular tool for interpreting these models by attributing the output to individual features. However, the technical nature of SHAP explanations often limits their utility to r..