(Computer methods and programs in biomedicine[TA])
7,811 results
  • Interpretable Machine Learning Model Using Oxygenation and Respiratory Variability to Predict Hemorrhagic Stroke Mortality: A Multicenter Validation Study. [Journal Article]
    Comput Methods Programs Biomed. 2026 Jul 29; 286:109572. [Online ahead of print]Feng J, Zhang H, … Wei BCM
  • CONCLUSIONS: We developed and externally validated a machine learning model incorporating oxygenation and respiratory variability for mortality risk estimation in patients with hemorrhagic stroke. The model showed strong internal performance and acceptable external discrimination after a 72-hour ICU observation window. However, the low external positive predictive value and cross-cohort differences in event prevalence and treatment patterns limit immediate clinical application. The model should therefore be considered a risk-estimation framework that requires recalibration and prospective validation before integration into clinical decision-support systems.
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  • Adaptive graph convolutional neural network incorporating ECG for individualized motor imagery EEG classification. [Journal Article]
    Comput Methods Programs Biomed. 2026 Jul 27; 286:109564. [Online ahead of print]Li S, Luo G, … Wang CCM
  • CONCLUSIONS: Experimental results demonstrate that HAD-GCN significantly improves cross-subject classification performance and prediction reliability while maintaining strong generalization capabilities. The proposed multi-level adaptive approach consistently enhances classification accuracy for individual subjects, highlighting its potential for practical applications in EEG-based technologies. Our code is available at https://github.com/chuanlaiair/HAD-GCN.
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  • Applications of quantum AI in brain disorder diagnosis: A systematic review. [Review]
    Comput Methods Programs Biomed. 2026 Jul 22; 286:109565. [Online ahead of print]Jafari M, Tang Z, … Li YCM
  • CONCLUSIONS: QAI shows emerging potential for brain disorder analysis, particularly through hybrid quantum-classical learning, quantum neural architectures, quantum-kernel methods, and quantum-inspired optimization. Nevertheless, current evidence remains preliminary and requires larger datasets, subject-level and external validation, fair classical benchmarking, noise-resilient circuits, real quantum hardware evaluation, explainability, and clinical validation.
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  • AdaPR: Adaptive Plane Reformatting for 4D flow MRI using deep reinforcement learning. [Journal Article]
    Comput Methods Programs Biomed. 2026 Jul 21; 286:109546. [Online ahead of print]Bisbal J, Sotelo J, … Uribe SCM
  • CONCLUSIONS: AdaPR provides robust, orientation-independent plane reformatting for 4D flow MRI, achieving flow quantification comparable to expert observers. Its adaptability across datasets and scanners makes it a promising candidate for other medical imaging applications beyond 4D flow MRI.
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  • Understanding palliative care trajectories through clustering, calibrated prediction models, and explainable AI. [Journal Article]
    Comput Methods Programs Biomed. 2026 Jul 22; 286:109554. [Online ahead of print]Migiddorj B, Batterham M, … Win KTCM
  • CONCLUSIONS: Palliative care needs follow a continuous severity gradient, with prediction accuracy varying across disease stages, most closely aligned to functional status. Prediction is most accurate near the end of life and least accurate earlier, highlighting a clinical paradox where intervention potential is greatest when predictions are most uncertain. Methodologically, these findings show that calibration improves prediction reliability, while clustering enhances interpretability without increasing accuracy. The value of machine learning lies not in maximizing accuracy alone, but in combining calibrated predictions with interpretable stratification to support stage-specific care planning.
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  • AGSI: Adaptive group-enhanced strategy for iterative integration of single-cell multi-omics. [Journal Article]
    Comput Methods Programs Biomed. 2026 Jul 23; 286:109556. [Online ahead of print]Zhang F, Shang J, … Liu JXCM
  • CONCLUSIONS: AGSI provides a robust and scalable solution for multi-omics integration that preserves biological interpretability while achieving superior technical performance. AGSI is well suited to biomedical analyses requiring accurate cell type identification. The implementation code is available at https://github.com/CDMBlab/AGSI.
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