Inter-individual variability in symptoms and the dynamic nature of brain pathophysiology present significant challenges in constructing a robust diagnostic model for migraine. In this study, we aimed to integrate different types of magnetic resonance...
We developed an AI system capable of automatically classifying anterior eye images as either normal or indicative of corneal diseases. This study aims to investigate the influence of AI's misleading guidance on ophthalmologists' responses. This cross...
This study aimed to develop and validate machine learning (ML) models to predict the occurrence of delayed hyponatremia after transsphenoidal surgery for pituitary adenoma. We retrospectively collected clinical data on patients with pituitary adenoma...
BACKGROUND: Chronic kidney disease (CKD) imposes a significant global health and economic burden, impacting millions globally. Despite its high prevalence, public awareness and understanding of CKD remain limited, leading to delayed diagnosis and sub...
BACKGROUND: Identification of mother-infant pairs predisposed to early cessation of exclusive breastfeeding is important for delivering targeted support. Machine learning techniques enable development of transparent prediction models that enhance cli...
BACKGROUND: Chat Generative Pre-trained Transformer (ChatGPT) is a 175-billion-parameter natural language processing model that uses deep learning algorithms trained on vast amounts of data to generate human-like texts such as essays. Consequently, i...
BACKGROUND: The global prevalence of diabetes has been rising rapidly in recent years, leading to an increase in patients experiencing hyperglycemic crises like diabetic ketoacidosis (DKA) and hyperosmolar hyperglycemic state (HHS). Patients with imp...
Public understanding of medical innovations such as smart technology is decisive for its acceptance and implementation. Thus, it is important to understand what visions people develop of a technology based on initial information such as the label. We...
Self-rated health (SRH) is a significant predictor of future health outcomes. Despite the contribution of psychological factors in individuals' subjective health assessments, prior studies of machine learning-based prediction models primarily focused...
BACKGROUND: The role of cortical networks in health anxiety remain poorly understood. This study aimed to develop a predictive model for health anxiety, using a machine-learning approach based on resting-state functional connectivity (rsFC) with func...
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