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Development and validation of a nomogram model of lung metastasis in breast cancer based on machine learning algorithm and cytokines.
BMC cancer
Apr 14, 2025
BACKGROUND: The relationship between cytokines and lung metastasis (LM) in breast cancer (BC) remains unclear and current clinical methods for identifying breast cancer lung metastasis (BCLM) lack precision, thus underscoring the need for an accurate...
Exploring the potential of cell-free RNA and Pyramid Scene Parsing Network for early preeclampsia screening.
BMC pregnancy and childbirth
Apr 14, 2025
BACKGROUND: Circulating cell-free RNA (cfRNA) is gaining recognition as an effective biomarker for the early detection of preeclampsia (PE). However, the current methods for selecting disease-specific biomarkers are often inefficient and typically on...
Machine learning-based disease risk stratification and prediction of metabolic dysfunction-associated fatty liver disease using vibration-controlled transient elastography: Result from NHANES 2021-2023.
BMC gastroenterology
Apr 14, 2025
BACKGROUND: Metabolic dysfunction-associated fatty liver disease (MAFLD) is a common chronic liver disease and represents a significant public health issue. Nevertheless, current risk stratification methods remain inadequate. The study aimed to use m...
Acoustic Features for Identifying Suicide Risk in Crisis Hotline Callers: Machine Learning Approach.
Journal of medical Internet research
Apr 14, 2025
BACKGROUND: Crisis hotlines serve as a crucial avenue for the early identification of suicide risk, which is of paramount importance for suicide prevention and intervention. However, assessing the risk of callers in the crisis hotline context is cons...
A method for predicting postpartum depression via an ensemble neural network model.
Frontiers in public health
Apr 14, 2025
INTRODUCTION: Postpartum depression (PPD) has numerous adverse impacts on the families of new mothers and society at large. Early identification and intervention are of great significance. Although there are many existing machine learning classifiers...
AADNet: Exploring EEG Spatiotemporal Information for Fast and Accurate Orientation and Timbre Detection of Auditory Attention Based on a Cue-Masked Paradigm.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Apr 14, 2025
Auditory attention decoding from electroencephalogram (EEG) could infer to which source the user is attending in noisy environments. Decoding algorithms and experimental paradigm designs are crucial for the development of technology in practical appl...
Opinions and Perspectives of Canadian Occupational Therapists on Artificial Intelligence.
Canadian journal of occupational therapy. Revue canadienne d'ergotherapie
Apr 13, 2025
Technology is rapidly being developed to improve healthcare outcomes. However, the attitudes and perceptions of occupational therapists (OTs) on artificial intelligence (AI) in healthcare are not yet known. This study aims to: explore Canadian OTs'...
Longitudinal brain age in first-episode mania youth treated with lithium or quetiapine.
European neuropsychopharmacology : the journal of the European College of Neuropsychopharmacology
Apr 13, 2025
It is unclear if lithium and quetiapine have neuroprotective effects, especially in early stages of bipolar and schizoaffective disorders. Here, an age-related multivariate brain structural measure (i.e., brain-PAD) at baseline and changes in respons...
The differential diagnosis of autism spectrum disorder in adults.
Expert review of neurotherapeutics
Apr 13, 2025
INTRODUCTION: Diagnosing autism spectrum disorder (ASD) in adults is challenging due to its heterogeneity and symptom overlap with other conditions. Making an accurate diagnosis can be difficult and overwhelming but is vital for proper accommodations...
The role of patient outcomes in shaping moral responsibility in AI-supported decision making.
Radiography (London, England : 1995)
Apr 13, 2025
INTRODUCTION: Integrating decision support mechanisms utilising artificial intelligence (AI) into medical radiation practice introduces unique challenges to accountability for patient care outcomes. AI systems, often seen as "black boxes," can obscur...
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