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Stacking classifiers based on integrated machine learning model: fusion of CT radiomics and clinical biomarkers to predict lymph node metastasis in locally advanced gastric cancer patients after neoadjuvant chemotherapy.
BMC cancer
May 6, 2025
BACKGROUND: The early prediction of lymph node positivity (LN+) after neoadjuvant chemotherapy (NAC) is crucial for optimizing individualized treatment strategies. This study aimed to integrate radiomic features and clinical biomarkers through machin...
Exploring Therapists' Approaches to Treating Eating Disorders to Inform User-Centric App Design: Web-Based Interview Study.
JMIR formative research
May 6, 2025
BACKGROUND: The potential for digital interventions in self-management and treatment of mild to moderate eating disorders (EDs) has already been established. However, apps are infrequently recommended by ED therapists to their clients. Those that are...
Assessing autobiographical memory consistency: Machine and human approaches.
Behavior research methods
May 6, 2025
Memory is far from a stable representation of what we have encountered. Over time, we can forget, modify, and distort the details of our experiences. How autobiographical memory-the memories we have for our personal past-changes has important ramific...
Unsupervised Domain Adaptation With Synchronized Self-Training for Cross- Domain Motor Imagery Recognition.
IEEE journal of biomedical and health informatics
May 6, 2025
Robust decoding performance is essential for the practical deployment of brain-computer interface (BCI) systems. Existing EEG decoding models often rely on large amounts of annotated data collected through specific experimental setups, which fail to ...
AgeML: Age Modeling With Machine Learning.
IEEE journal of biomedical and health informatics
May 6, 2025
An approach to age modeling involves the supervised prediction of age using machine learning from subject features. The derived age metrics are used to study the relationship between healthy and pathological aging in multiple body systems, as well as...
WavFace: A Multimodal Transformer-Based Model for Depression Screening.
IEEE journal of biomedical and health informatics
May 6, 2025
Depression, a prevalent mental health disorder with severe health and economic consequences, can be costly and difficult to detect. To alleviate this burden, recent research has been exploring the depression screening capabilities of deep learning (D...
MHFNet: A Multimodal Hybrid-Embedding Fusion Network for Automatic Sleep Staging.
IEEE journal of biomedical and health informatics
May 6, 2025
Scoring sleep stages is essential for evaluating the status of sleep continuity and comprehending its structure. Despite previous attempts, automating sleep scoring remains challenging. First, most existing works did not fuse local and global tempora...
FlexibleSleepNet:A Model for Automatic Sleep Stage Classification Based on Multi-Channel Polysomnography.
IEEE journal of biomedical and health informatics
May 6, 2025
In the task of automatic sleep stage classification, deep learning models often face the challenge of balancing temporal-spatial feature extraction with computational complexity. To address this issue, this study introduces FlexibleSleepNet, a lightw...
Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma From Multi-Sequence Magnetic Resonance Imaging Based on Deep Fusion Representation Learning.
IEEE journal of biomedical and health informatics
May 6, 2025
Recent studies have identified microvascular invasion (MVI) as the most vital independent biomarker associated with early tumor recurrence. With advancements in medical technology, several computational methods have been developed to predict preopera...
Cognitive Load Prediction From Multimodal Physiological Signals Using Multiview Learning.
IEEE journal of biomedical and health informatics
May 6, 2025
Predicting cognitive load is a crucial issue in the emerging field of human-computer interaction and holds significant practical value, particularly in flight scenarios. Although previous studies have realized efficient cognitive load classification,...
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