Computer methods and programs in biomedicine update最新文献

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Computer methods and programs in biomedicine update

Pub Date : 2025-01-01

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Computer methods and programs in biomedicine update

Pub Date : 2025-01-01

引用次数: 0

Computer methods and programs in biomedicine update

Pub Date : 2025-01-01

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ECgMLP: A novel gated MLP model for enhanced endometrial cancer diagnosis ECgMLP：一种增强子宫内膜癌诊断的新型门控MLP模型

Computer methods and programs in biomedicine update

Pub Date : 2025-01-01 DOI: 10.1016/j.cmpbup.2025.100181

Md. Alif Sheakh , Sami Azam , Mst. Sazia Tahosin , Asif Karim , Sidratul Montaha , Kayes Uddin Fahim , Niusha Shafiabady , Mirjam Jonkman , Friso De Boer

Endometrial cancеr is the fourth fastеst-growing cancеr among women worldwide, affecting the uterus's lining. This research proposes a novel approach called ECgMLP for the automated diagnosis of endometrial cancer by analyzing histopathological images. Several preprocessing techniques are employed to increase the quality of the images, including normalization, Non-Local Means denoising, and alpha-beta enhancement. Effective segmentation is achieved through a combination of Otsu thresholding, morphological operations, distance transformations, and the watershed approach to identify major regions of interest. Through a sequence of blocks, the ECgMLP architecture processes input images to remove unimportant patterns. Model hyperparameters are improved via ablation research. The evaluations show a maximum accuracy of 99.26 % for identifying multi-class histopathological categories of endometrial tissue, which is higher than the previous best technique. The proposed model offers an automated, correct diagnosis, enhancing clinical processes. This proposition could be added to the current tools for finding endometrial cancer early, leading to better patient outcomes.

子宫内膜癌是全球妇女中生长速度第四快的癌症，主要影响子宫内膜。这项研究提出了一种名为 ECgMLP 的新方法，用于通过分析组织病理学图像自动诊断子宫内膜癌。该方法采用了多种预处理技术来提高图像质量，包括归一化、非局部均值去噪和α-β增强。通过结合大津阈值、形态学运算、距离变换和分水岭方法来识别主要感兴趣区，从而实现有效的分割。通过一系列块，ECgMLP 架构处理输入图像以去除不重要的模式。通过消融研究改进了模型超参数。评估结果显示，识别子宫内膜组织多类组织病理学类别的最高准确率为 99.26%，高于之前的最佳技术。所提出的模型可提供自动、正确的诊断，从而改善临床过程。这一建议可被添加到现有的早期发现子宫内膜癌的工具中，从而为患者带来更好的治疗效果。

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引用次数: 0

A Caputo fractional-order model with MCMC for rabies transmission dynamics 狂犬病传播动力学的MCMC Caputo分数阶模型

Computer methods and programs in biomedicine update

Pub Date : 2025-01-01 DOI: 10.1016/j.cmpbup.2025.100206

Jufren Zakayo Ndendya , Joshua A. Mwasunda , Stephen Edward , Nyimvua Shaban Mbare

Rabies continues to pose a severe public health threat, particularly in regions with high interactions between humans and infected dog populations. This study develops a fractional-order mathematical model using the Caputo derivative to capture the memory and hereditary effects in rabies transmission dynamics. The model incorporates key intervention strategies, including public health education, treatment, and culling of stray and infected dogs, to evaluate their effectiveness in controlling rabies outbreaks. The Markov Chain Monte Carlo (MCMC) method is utilized for parameter estimation, enhancing model precision and predictive accuracy. Stability analysis demonstrates that the disease-free equilibrium is locally asymptotically stable when effective reproduction number

R_{e} < 1

. Numerical simulations reveal that fractional-order model provides a more flexible and realistic representation of rabies spread compared to classical integer-order model. The results highlight the significant impact of public health education, treatment and targeted culling in reducing infection rates. The findings offer crucial insights for policymakers and public health officials in designing optimal intervention strategies to achieve sustainable rabies control.

狂犬病继续构成严重的公共卫生威胁，特别是在人与受感染犬群高度互动的地区。本研究开发了一个分数阶数学模型，利用卡普托导数来捕捉狂犬病传播动力学中的记忆和遗传效应。该模型纳入了主要的干预策略，包括公共卫生教育、治疗和扑杀流浪狗和感染狗，以评估其控制狂犬病爆发的有效性。采用马尔可夫链蒙特卡罗（MCMC）方法进行参数估计，提高了模型精度和预测精度。稳定性分析表明，当有效繁殖数为1时，无病平衡是局部渐近稳定的。数值模拟结果表明，与传统的整阶模型相比，分数阶模型能更灵活、更真实地描述狂犬病的传播。研究结果强调了公共卫生教育、治疗和有针对性的扑杀在降低感染率方面的重大影响。这些发现为决策者和公共卫生官员设计最佳干预策略以实现可持续的狂犬病控制提供了重要见解。

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引用次数: 0

Smart product service systems for remote patient monitoring under uncertainty: A hierarchical framework from a healthcare provider perspective 不确定性下用于远程患者监测的智能产品服务系统：从医疗保健提供者的角度来看的分层框架

Computer methods and programs in biomedicine update

Pub Date : 2025-01-01 DOI: 10.1016/j.cmpbup.2024.100174

Yeneneh Tamirat Negash , Faradilah Hanum , Liria Salome Calahorrano Sarmiento

Background

This study contributes to the integration of smart product service systems (smart PSSs) for remote patient monitoring (RPM). Integrating smart PSSs into RPM improves service delivery by enabling personalized care plans and shaping a patient-centered workflow for intelligent RPM. However, a gap exists in identifying intelligent RPM attributes and understanding their interrelationships. In addition, prior studies of RPM have yielded mixed results, with some studies demonstrating positive impacts and others showing no effect or even negative consequences on patient health. This inconsistency highlights the need for further investigation into how RPM systems are designed and utilized.

Objectives

First, the proposed intelligent RPM development criteria are validated through a qualitative assessment. Second, the interrelationships among intelligent RPM attributes are analyzed. Finally, the driving factors of intelligent RPM development are identified.

Methods

A hybrid methodology that combines the fuzzy Delphi method (FDM), the fuzzy decision-making trial and evaluation laboratory (FDEMATEL), and an analytical network process (ANP) is introduced to establish a hierarchical model of intelligent RPM attributes. Thirty healthcare industry experts specializing in chronic disease management participated in the study. Linguistic variables were utilized to manage the uncertainty inherent in expert opinions.

Results

The cause group encompassed operational efficiency, enhanced analytics, and sustainable service management, whereas the effect group comprised patient satisfaction and platform technology. The driving criteria included personalized treatment plans, real-time monitoring, mobile app development, and accessibility.

Conclusion

This study advances the understanding of how smart PSSs can be integrated into healthcare delivery. The developed hierarchical framework provides a roadmap for healthcare providers to implement and optimize intelligent RPM systems.

背景本研究有助于将智能产品服务系统（smart PSS）整合到远程患者监护（RPM）中。将智能产品服务系统集成到 RPM 中，可实现个性化护理计划，并为智能 RPM 塑造以患者为中心的工作流程，从而改善服务的提供。然而，在确定智能 RPM 属性和了解其相互关系方面还存在差距。此外，先前对 RPM 的研究结果不一，有些研究显示了积极影响，有些研究则显示对患者健康没有影响，甚至有负面影响。目标首先，通过定性评估验证所提出的智能 RPM 开发标准。其次，分析智能 RPM 属性之间的相互关系。方法采用模糊德尔菲法（FDM）、模糊决策试验和评估实验室（FDEMATEL）以及分析网络过程（ANP）相结合的混合方法，建立智能 RPM 属性的分层模型。30 位专门从事慢性病管理的医疗行业专家参与了研究。结果原因组包括运营效率、增强分析和可持续服务管理，而影响组包括患者满意度和平台技术。驱动标准包括个性化治疗方案、实时监控、移动应用开发和可及性。所开发的分层框架为医疗机构实施和优化智能 RPM 系统提供了路线图。

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Computer methods and programs in biomedicine update

Pub Date : 2025-01-01

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Computer methods and programs in biomedicine update

Pub Date : 2025-01-01

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Computer methods and programs in biomedicine update

Pub Date : 2025-01-01

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Computer methods and programs in biomedicine update

Pub Date : 2025-01-01

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