Carbapenemase-producing Enterobacterales (CPE) are considered among the highest threats to global health by WHO. Their detection is difficult and time-consuming. We developed a random-forest machine learning (ML) model, CarbaDetector, to predict carb...
Self-driving laboratories accelerate the application of the scientific method and the discovery process through high-throughput experimentation, intelligent perception and planning, and effective human-robot collaboration. However, detecting anomalie...
BACKGROUND: Missing data are a common challenge in electronic health record (EHR)-based prediction modeling. Traditional imputation methods may not suit prediction or machine learning models, and real-world use requires workflows that are implementab...
BACKGROUND: The unstructured data of Chinese cancer electronic health records (EHRs) contains valuable medical expertise. Accurate medical entity recognition is crucial for building a medical-assisted decision system. Named entity recognition (NER) i...
BACKGROUND: Cancer survivorship is a complicated, chronic, and long-lasting experience, causing uncertainty and a wide range of physical and emotional health concerns. Due to the complexity of cancer, patients often seek out multiple sources of healt...
BACKGROUND: As artificial intelligence (AI) technology permeates health care settings, nurse leaders must position themselves to shape its development, implementation, and impact, guiding meaningful change that benefits nurses and care delivery. Nurs...
The heterogeneity of psychotic disorders leads to instability in subjectively defined diagnoses. This study used a machine learning framework termed common orthogonal basis extraction (COBE) to decompose electroencephalography-based functional connec...
Cognitive abilities are closely tied to mental health from early childhood. This study explores how neurobiological units of analysis of cognitive abilities-multimodal neuroimaging and polygenic scores (PGS)-represent this connection. Using data from...
Medical oncology (Northwood, London, England)
Nov 14, 2025
Radiogenomics links quantitative features from routine CT and PET/CT with tumor genomics to non-invasively profile non-small cell lung cancer (NSCLC). This review synthesizes the current workflow-from image acquisition and segmentation to feature ext...
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