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Improved dynamic programming method for solving multi-objective and multi-stage decision-making problems.
Sci Rep
January 2025
School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, 510006, China.
Multi-objective and multi-stage decision-making problems require balancing multiple objectives at each stage and making optimal decision in multi-dimensional control variables, where the commonly used intelligent optimization algorithms suffer from low solving efficiency. To this end, this paper proposes an efficient algorithm named non-dominated sorting dynamic programming (NSDP), which incorporates non-dominated sorting into the traditional dynamic programming method. To improve the solving efficiency and solution diversity, two fast non-dominated sorting methods and a dynamic-crowding-distance based elitism strategy are integrated into the NSDP algorithm.
View Article and Find Full Text PDFSmartSkin-XAI: An Interpretable Deep Learning Approach for Enhanced Skin Cancer Diagnosis in Smart Healthcare.
Diagnostics (Basel)
December 2024
Department of Electrical Engineering and Computer Science, Texas A&M University-Kingsville, Kingsville, TX 78363, USA.
Skin cancer, particularly melanoma, poses significant challenges due to the heterogeneity of skin images and the demand for accurate and interpretable diagnostic systems. Early detection and effective management are crucial for improving patient outcomes. Traditional AI models often struggle with balancing accuracy and interpretability, which are critical for clinical adoption.
View Article and Find Full Text PDFHospital Medicare Spending per Beneficiary: A Longitudinal Study.
J Healthc Manag
January 2025
Division of Health Care Delivery Research, Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Jacksonville, Florida.
Goal: While studies have examined quality and health outcomes related to the Centers for Medicare & Medicaid Services' (CMS's) Hospital Value-Based Purchasing (HVBP) Program, a significant gap exists in the literature regarding the relationship between pay-for-performance initiatives and hospital financial performance in the program's Efficiency and Cost Reduction domain. This study examined the association between hospitals' cost inefficiency and participation in the HVBP Program by estimating the probability and magnitude of improvement or achievement in the program's Efficiency and Cost Reduction domain.
Methods: The 2014-2019 Efficiency and Cost Reduction domain data were obtained from CMS and merged with the American Hospital Association's Annual Survey Database.
Deep learning-integrated MRI brain tumor analysis: feature extraction, segmentation, and Survival Prediction using Replicator and volumetric networks.
Sci Rep
January 2025
Department of Computer Engineering, Inha University, Incheon, Republic of Korea.
The most prevalent form of malignant tumors that originate in the brain are known as gliomas. In order to diagnose, treat, and identify risk factors, it is crucial to have precise and resilient segmentation of the tumors, along with an estimation of the patients' overall survival rate. Therefore, we have introduced a deep learning approach that employs a combination of MRI scans to accurately segment brain tumors and predict survival in patients with gliomas.
View Article and Find Full Text PDFPerformance evaluation of a machine learning-based methodology using dynamical features to detect nonwear intervals in actigraphy data in a free-living setting.
Sleep Health
January 2025
Department of Human and Development and Family Studies, Pennsylvania State University, University Park, Pennsylvania, USA.
Goal And Aims: One challenge using wearable sensors is nonwear time. Without a nonwear (e.g.
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