UNLABELLED: This study aimed to develop and validate a risk prediction model for moderate to severe bleeding in children with immune thrombocytopenia (ITP). Data from 286 ITP patients were prospectively collected and randomly split into training (80%...
To investigate the potential of employing artificial intelligence (AI) -driven breast ultrasound analysis models for the classification of glandular tissue components (GTC) in dense breast tissue. A total of 1,848 healthy women with mammograms classi...
PURPOSE: To evaluate the diagnostic performance of combining ultrafast real-time cine MRI with morphological measurements on gastroesophageal reflux disease (GERD).
Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association
Apr 4, 2025
BACKGROUND & AIMS: Elemental diets (EDs) have desirable safety and efficacy profiles in several clinical settings partly because of modulation of gut microbiome. Palatability of EDs remains the main barrier to compliance/adherence, and their effect h...
OBJECTIVE: To develop an automated method for the joint and consistent evaluation of emphysema and mortality risk that provides quantification of data and model uncertainty.
International journal of medical informatics
Apr 4, 2025
BACKGROUND: Early identification and prevention of ventilator-associated pneumonia (VAP) in patients with mechanical ventilation (MV) through reliable prediction model undergoing a rigorous and standardized process is essential for clinical decision-...
BACKGROUND: Greenspace exposure is associated with lower depression risk. However, most studies have measured greenspace exposure using satellite-based vegetation indices, leading to potential exposure misclassification and limited policy relevance. ...
International journal of surgery (London, England)
Apr 3, 2025
BACKGROUND: Early identification of high-risk factors for inadequate analgesia and adverse reactions in obstetric patients is critical for improving outcomes. This study developed a machine learning model to predict these factors and optimize anesthe...
European journal of cancer (Oxford, England : 1990)
Apr 3, 2025
BACKGROUND: Psychological distress is prevalent among newly diagnosed cancer patients, often exacerbating treatment-related anxiety and depression. Artificial intelligence (AI)-driven interventions, such as large language models (LLMs), offer scalabl...
BACKGROUND: Early prediction of treatment response to neoadjuvant therapy (NAT) in breast cancer patients can facilitate timely adjustment of treatment regimens. We aimed to develop and validate a MRI-based enhanced self-attention network (MESN) for ...
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