OBJECTIVES: This study aimed to investigate the impact of primary tumor resection (PTR) on survival outcomes for patients with metastatic non-small cell neuroendocrine tumors (mNSCLC-NETs), develop a predictive model to identify which patients may be...
BACKGROUND: The clinical diagnosis, severity assessment, and outcome prognostication of community-acquired pneumonia (CAP) remain challenging due to the complex disease pathophysiology. Accurate outcome prediction is crucial for optimizing patient ma...
OBJECTIVES: To develop a cascaded machine learning model based on MRI radiomics features from cartilage and subchondral bone to predict the incidence and progression of knee osteoarthritis (KOA), thereby addressing the need for early intervention in ...
BMC medical informatics and decision making
Oct 31, 2025
The nature of decisions in the healthcare domain necessitates accurate, interpretable, and reliable AI solutions. Explanation Guided Learning (EGL) explores the integration of explanation annotations into learning models to align human and model expl...
BACKGROUND AND OBJECTIVES: We aimed to identify and optimize contributing factors associated with allergic diseases by machine/deep learning algorithms among school-age children aged 6-14 years.
PURPOSE: Objectives were to develop a machine learning (ML) model based on electronic health record (EHR) data to predict the risk of vomiting within a 96-hour window after admission to the pediatric oncology and hematopoietic cell transplant (HCT) s...
PURPOSE: The decline in activities of daily living (ADL) among older persons is a significant public health concern. Sarcopenia is a major risk factor for ADL disability. This study aimed to develop and validate an interpretable machine learning (IML...
This study utilized well-known supervised machine learning algorithms to NFHS‑5 data of West Bengal, India, to predict the place of birth (home vs facility) by integrating CHW (community health worker) contact factors and women participant's percepti...
The use of bacteriophages for biological control of bacterial infections is a promising approach to combat antimicrobial resistant bacteria. Prediction of phage-bacteria interactions is key to identify sensitive bacterial strains to phage therapy. Si...
Automated brain tumor detection represents a fundamental challenge in contemporary medical imaging, demanding both precision and computational feasibility for practical implementation. This research introduces a novel Vision Transformer (ViT) framewo...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.