BMC medical informatics and decision making
Jul 15, 2025
BACKGROUND: Hemorrhage is a prevalent and critical condition in the intensive care unit (ICU), characterized by high incidence, elevated mortality rates, and substantial therapeutic challenges. Accurate prediction of mortality in patients with hemorr...
To develop and validate artificial intelligence models based on contrast-enhanced CT(CECT) images of venous phase using deep learning (DL) and Radiomics approaches to predict lymphovascular invasion in gastric cancer prior to surgery. We retrospectiv...
Emergency department (ED) overcrowding has become a critical issue in hospital management, leading to increased patient wait times and higher rates of individuals leaving without being seen (LWBS). This study aims to identify key factors influencing ...
OBJECTIVES: This study aimed to develop an accurate prediction model for the risk of Non-alcoholic fatty liver disease (NAFLD) using the random survival forests (RSF), and to investigate the distribution of NAFLD risk with time.
BACKGROUND: Accurately assessing the risk stratification of cN0 papillary thyroid carcinoma (PTC) preoperatively aids in making treatment decisions. We integrated preoperative ultrasound and cytological images of patients to develop and validate a mu...
BACKGROUND: Accurately distinguishing the different molecular subtypes of 2021 World Health Organization (WHO) grade 4 Central Nervous System (CNS) gliomas is highly relevant for prognostic stratification and personalized treatment.
Helicobacter pylori (H. pylori) is the most common carcinogenic pathogen globally and the leading cause of gastric cancer. Here, we develop a reinforcement learning-based AI Clinician system to personalise treatment selection and evaluate its ability...
BACKGROUND: Standardized registries, such as the International Classification of Diseases (ICD) codes, are commonly built using administrative codes assigned to patient encounters. However, patients with fall injury are often coded using subsequent i...
OBJECTIVE: To study the association between cerebral small vessel diseases (CSVD) and unfavorable hematoma morphology in primary intracerebral hemorrhage (ICH).
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