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A data-driven multicriteria decision model for healthcare workforce retention strategies 医疗保健人力保留策略的数据驱动多标准决策模型

Healthcare analytics (New York, N.Y.)

Pub Date : 2025-06-13 DOI: 10.1016/j.health.2025.100403

Debora Di Caprio , Sofia Sironi , Fan-Yun Lan , Ramin Rostamkhani

The retention of nurses and physicians in Hospitals is a global problem affecting the healthcare system worldwide. This study focuses on the healthcare workforce retention problem considering the current situation in Taiwan. Healthcare staff in Taiwan are undergoing a critical phase, with an increasing number of experienced workers leaving their job to go to work for private organizations or as freelancers. We develop a data-driven four-phase methodology based on the design of a satisfaction index that allows to rank different groups of employees against a given set of criteria. First, criteria are identified and clustered to describe different job dimensions (phase 1). Hence, subjective evaluations of the criteria are collected from healthcare workers while experts provide pairwise comparisons among them (phase 2). An adjusted analytic hierarchy process (AHP) is used to weight the job dimensions and the criteria within each job dimension (phase 3). Finally, the satisfaction index is formalized and computed for different groups of employees (phase 4). The methodology has been implemented with data collected from healthcare workers employed in three healthcare institutions in Northern Taiwan. The proposed index represents a novel decision support tool for managers and policy makers in designing intervention strategies able to address different needs of different groups of employees. Besides, it allows for innovative applications to quality management (QM) by extending the standard QM approach to hospitals and healthcare centers far beyond the common focus on patients' satisfaction. Finally, the mathematical formulation of the index is very flexible and allows for applications to any employment sector through a variety of analyses based on different categorizations of the workers.

护士和医生在医院的保留是一个全球性的问题，影响全球医疗保健系统。本研究针对目前台湾医疗保健人力保留问题进行研究。台湾的医护人员正处于一个关键阶段，越来越多有经验的医护人员离开工作岗位，去私人机构工作或成为自由职业者。我们开发了一种基于满意度指数设计的数据驱动的四阶段方法，该指数允许根据给定的一组标准对不同组的员工进行排名。首先，确定标准并将其聚类以描述不同的工作维度（阶段1）。因此，从卫生保健工作者那里收集对标准的主观评价，而专家在他们之间进行两两比较（阶段2）。采用调整后的层次分析法（AHP）对工作维度和每个工作维度内的标准进行加权（阶段3）。最后，对不同员工群体的满意度指数进行形式化和计算（阶段4）。本研究以台湾北部三所医疗机构的医护人员为研究对象。该指标为管理者和政策制定者设计干预策略提供了一种新的决策支持工具，能够满足不同员工群体的不同需求。此外，通过将标准质量管理方法扩展到医院和医疗保健中心，它允许质量管理（QM）的创新应用程序，远远超出了对患者满意度的通常关注。最后，该指数的数学公式非常灵活，并允许通过基于不同类别的工人的各种分析应用于任何就业部门。

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引用次数: 0

An analytical approach to modeling conjunctival viral disease using fuzzy logic and time-delay dynamics 基于模糊逻辑和时滞动力学的结膜病毒病建模分析方法

Healthcare analytics (New York, N.Y.)

Pub Date : 2025-06-06 DOI: 10.1016/j.health.2025.100404

Muhammad Tashfeen , Hothefa Shaker Jassim , Fazal Dayan , Muhammad Azizur Rehman , Alwahab Dhulfiqar Zoltán , Husam A. Neamah

Conjunctivitis, commonly known as pink eye, is the inflammation of the conjunctiva, often accompanied by redness, itchiness, and the discharge of thick white or greyish pus. Highly contagious in settings involving close contact, it poses significant public health and economic concerns. This study proposes a fuzzy mathematical modeling framework to investigate Conjunctival Viral Disease (CVD) transmission dynamics, with particular attention to the roles of asymptomatic carriers and environmental influences. Unlike conventional models that rely solely on deterministic parameters, the incorporation of fuzzy theory allows for representing uncertainties and variabilities inherent in real-world disease transmission. The model further incorporates time-delay terms to account for incubation periods and other latent effects, enhancing the accuracy of system dynamics. This fuzzy framework performs key analyses, including identifying equilibrium points, computation of the basic reproduction number, sensitivity analysis, and assessment of local and global stability. Numerical solutions are obtained using the Forward Euler and Nonstandard Finite Difference (NSFD) methods. The NSFD scheme is rigorously examined for convergence, non-negativity, boundedness, and consistency properties. Simulation results confirm that the NSFD approach maintains the qualitative features of the model even under larger time steps. Overall, the study underscores the importance of integrating fuzzy logic and time delays in epidemic modeling and presents a robust methodological approach for understanding and managing the spread of infectious diseases in uncertain and dynamic environments.

结膜炎，俗称红眼病，是结膜的炎症，常伴有红肿、发痒，并排出浓稠的白色或灰色脓液。该病在密切接触的环境中具有高度传染性，造成重大的公共卫生和经济问题。本研究提出了一个模糊数学模型框架来研究结膜病毒病（CVD）的传播动力学，特别关注无症状携带者和环境影响的作用。与仅依赖确定性参数的传统模型不同，模糊理论的结合允许表示现实世界疾病传播中固有的不确定性和可变性。该模型进一步纳入了时滞项，以考虑潜伏期和其他潜在影响，提高了系统动力学的准确性。该模糊框架执行关键分析，包括确定平衡点，计算基本再现数，敏感性分析以及局部和全局稳定性评估。采用正演欧拉和非标准有限差分（NSFD）方法得到了数值解。对NSFD方案的收敛性、非负性、有界性和一致性进行了严格的检验。仿真结果表明，即使在较大的时间步长下，NSFD方法仍能保持模型的定性特征。总体而言，该研究强调了在流行病建模中集成模糊逻辑和时间延迟的重要性，并为在不确定和动态环境中理解和管理传染病的传播提供了强有力的方法方法。

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引用次数: 0

An attention-based loss function and synthetic minority oversampling technique for alleviating class imbalance in predicting diabetes 基于注意力的损失函数和合成少数派过采样技术在糖尿病预测中的应用

Healthcare analytics (New York, N.Y.)

Pub Date : 2025-06-01 DOI: 10.1016/j.health.2025.100399

Santanu Roy , Reshma Rachel Cherish , Gifty Roy

Diabetes is a chronic disease due to higher blood sugar (or Glucose) levels in the blood. This study proposes a novel attention-based loss function and a lightweight artificial neural network (ANN) called Diabetic Lite (DB-Lite) for diabetes prediction in the Pima Indian Diabetes Dataset (PIDD). We show that the Pima dataset has many challenges. It is a small and imbalanced dataset; moreover, many features are non-linearly correlated in this dataset. The novelties of this research work are as follows: (i) A novel loss function of attention-based binary cross entropy (ABCE) is proposed for the first time to alleviate the statistical imbalance present within the Pima dataset. This ABCE loss function is incorporated in the DB-Lite model, which is trained from scratch. (ii) A Swish activation function is deployed in the hidden layer of DB-Lite instead of Rectified Linear Unit (ReLU) to deal with the non-linear dependency of features with the final outcome. (iii) The synthetic minority oversampling technique (SMOTE) is used as a pre-processing technique to mitigate the class imbalance problem from the Pima dataset. (iv) An adaptive learning rate is utilized while training the model to speed up the convergence of the DB-Lite model. Our final proposed framework has achieved 99.7% accuracy, 99.4% precision, 99.8% recall, and 99.6% F1 score in testing, which is the best result on this Pima dataset. The Welch t-testing (as a statistical hypothesis testing) and 10-fold cross-validation are utilized to prove the validity of the proposed loss function.

糖尿病是一种由于血液中高血糖（或葡萄糖）水平引起的慢性疾病。本研究提出了一种新的基于注意力的损失函数和一种称为diabetes Lite （DB-Lite）的轻量级人工神经网络（ANN），用于皮马印第安人糖尿病数据集（PIDD）的糖尿病预测。我们表明，Pima数据集存在许多挑战。这是一个小而不平衡的数据集；此外，该数据集中的许多特征是非线性相关的。本研究的新颖之处在于：(1)首次提出了一种新的基于注意力的二元交叉熵（ABCE）损失函数，以缓解Pima数据集中存在的统计不平衡。这个ABCE损失函数被纳入DB-Lite模型中，该模型是从头开始训练的。（ii）在DB-Lite的隐藏层部署Swish激活函数，而不是ReLU (Rectified Linear Unit)，以处理特征与最终结果的非线性依赖关系。（iii）采用合成少数派过采样技术（SMOTE）作为预处理技术，缓解了Pima数据集的类不平衡问题。（iv）在训练模型的同时，利用自适应学习率加快DB-Lite模型的收敛速度。我们最终提出的框架在测试中达到了99.7%的准确率，99.4%的精密度，99.8%的召回率和99.6%的F1分数，这是该Pima数据集上的最佳结果。使用Welch t检验（作为统计假设检验）和10倍交叉验证来证明所提出的损失函数的有效性。

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引用次数: 0

An analytical approach to assessing the spatial equity and allocation of healthcare resources in Shanghai 上海市卫生资源空间公平与配置分析方法

Healthcare analytics (New York, N.Y.)

Pub Date : 2025-06-01 DOI: 10.1016/j.health.2025.100400

Hong-Yan Li , Jing Guo, Chuang-Hao Yang

The rational allocation of healthcare resources is vital for establishing a healthcare system that aligns with the levels of economic and social development. As a comprehensive discipline integrating geography, cartography, remote sensing, and computer science, Geographic Information System (GIS) can visualize and analyze spatial information through mapping. By utilizing GIS's statistical analysis and data visualization functions, this study provides a more efficient and intuitive analysis of Shanghai's spatial healthcare resource allocation and a more comprehensive assessment of its current allocation status. To examine the spatial correlation and spatial proximity, we apply the Global Moran Index (Moran's I), the Local Indicators of Spatial Association (LISA) test, and Hot Spot Analysis (Getis-Ord Gi∗) for assessment. Furthermore, by utilizing the Lorenz curve and Gini coefficient, this study provides a new perspective by expanding the measurement dimensions for assessing healthcare resource allocation in Shanghai. The results show that: From the global spatial correlation perspective, the allocation of healthcare resources in Shanghai exhibits spatial clustering. From the local spatial correlation perspective, healthcare resources in Shanghai show significant regional disparities, with resources concentrated in central urban areas. And from a multidimensional perspective, the equity of allocation of healthcare resources in Shanghai in 2022 was higher when measured by population (0.298 ± 0.063) and economy (0.292 ± 0.027) than by geographic area (0.612 ± 0.100) and green spaces (0.590 ± 0.110) of the Gini coefficient. These findings offer valuable insights for promoting the structural optimization and spatial distribution of healthcare resources in Shanghai.

合理配置医疗卫生资源，是建立与经济社会发展水平相适应的医疗卫生体系的关键。地理信息系统（Geographic Information System， GIS）是一门集地理学、地图学、遥感学和计算机科学于一体的综合性学科，它能够通过制图实现空间信息的可视化和分析。本研究利用GIS的统计分析和数据可视化功能，对上海市空间卫生资源配置进行了更高效、直观的分析，并对其配置现状进行了更全面的评估。为了检验空间相关性和空间接近性，我们应用全球Moran指数（Moran's I）、空间关联局部指标（LISA）测试和热点分析（Getis-Ord Gi∗）进行评估。此外，本研究运用Lorenz曲线和基尼系数，拓展了上海市卫生资源配置的测量维度，为评估上海市卫生资源配置提供了新的视角。结果表明：从全球空间关联角度看，上海市卫生资源配置呈现空间集聚性；从区域空间关联角度看，上海市卫生资源存在显著的区域差异，资源集中在中心城区。从多维度看，以人口（0.298±0.063）和经济（0.292±0.027）衡量的2022年上海市卫生资源配置公平性高于以地理面积（0.612±0.100）和绿地（0.590±0.110）衡量的基尼系数。研究结果对促进上海市卫生资源的结构优化和空间布局具有重要的参考价值。

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引用次数: 0

An equity-based spatial analytics framework for evaluating pharmacy accessibility using geographical information systems 利用地理信息系统评价药房可及性的基于公平的空间分析框架

Healthcare analytics (New York, N.Y.)

Pub Date : 2025-05-21 DOI: 10.1016/j.health.2025.100401

Sara Al-Naabi , Noura Al Nasiri , Talal Al-Awadhi , Meshal Abdullah , Ammar Abulibdeh

Healthcare services have a significant impact on socioeconomic and health development globally. In Oman, rapid development since the 1970s has led to a focus on the equitable distribution of public services. This research aims to evaluate the spatial accessibility and distribution of pharmacies in Muscat Governorate, Oman, using Geographical Information Systems (GIS) and spatial analysis techniques. The primary objective is to measure the equity in the spatial distribution of pharmacies within Muscat Governorate. The study utilizes spatial datasets, including administrative areas, pharmacy locations, settlement locations, transportation networks, and non-spatial datasets such as demographic data. The methodology involves spatial distribution analysis using Average Nearest Neighbor (ANN), Moran's I for spatial autocorrelation, Kernel Density Analysis (KDA), Thiessen polygons for catchment areas, and Network analysis for determining service areas and accessibility by walking and driving distances. Findings indicate a clustered distribution of pharmacies, with higher concentrations in densely populated northern Wilayats like Muttrah, AS Seeb, and Bawshar. Muttrah exhibits the highest accessibility, with 99 % coverage within a 2.5 km radius, whereas Muscat Wilaya lacks pharmacy services entirely. These findings highlight significant disparities in the spatial distribution of pharmacies, underscoring the need for policy interventions to ensure equitable access. Policymakers should consider geographic and demographic factors in health service planning to ensure fair distribution and accessibility across the governorate. Implementing these recommendations can help improve healthcare access and equity in Muscat, contributing to overall social and health development.

保健服务对全球社会经济和健康发展具有重大影响。在阿曼，自1970年代以来的迅速发展已导致把重点放在公平分配公共服务上。本研究旨在利用地理信息系统（GIS）和空间分析技术对阿曼马斯喀特省药店的空间可达性和分布进行评价。主要目标是衡量马斯喀特省药房空间分布的公平性。该研究利用了空间数据集，包括行政区域、药房位置、居民点位置、交通网络，以及非空间数据集，如人口数据。该方法包括使用平均最近邻居（ANN）进行空间分布分析，Moran's I用于空间自相关，核密度分析（KDA）用于集水区的Thiessen多边形，以及通过步行和开车距离确定服务区和可达性的网络分析。研究结果表明，药店呈集群分布，在人口稠密的北维拉亚特（如Muttrah、AS Seeb和bashar）集中度较高。穆特拉的可达性最高，在2.5公里半径内覆盖率达到99%，而马斯喀特维拉亚完全缺乏药房服务。这些发现突出了药店空间分布的显著差异，强调了采取政策干预措施以确保公平获取的必要性。决策者应在卫生服务规划中考虑地理和人口因素，以确保全省的公平分配和可及性。实施这些建议有助于改善马斯喀特的医疗保健机会和公平性，从而促进整体社会和卫生发展。

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引用次数: 0

A Deep Learning Framework for Chronic Kidney Disease stage classification 慢性肾脏疾病分期分类的深度学习框架

Healthcare analytics (New York, N.Y.)

Pub Date : 2025-05-20 DOI: 10.1016/j.health.2025.100398

Gayathri Hegde M , P Deepa Shenoy , Venugopal KR , Arvind Canchi

Chronic Kidney Disease (CKD) has become more prevalent, leading to a gradual decline in kidney function and, ultimately, in renal failure. Timely detection of the CKD stage is essential for enhancing healthcare services and decreasing morbidity and mortality. Hence, this study proposes a Metaheuristic-Hybrid Metaheuritstic eXplainable Artificial Intelligence (MHMXAI) driven Feature Selection (FS) approach and Deep Learning (DL) models for CKD stage prediction. MHMXAI approach selects the features with the highest scores from the Metaheuristic algorithm-Eagle Search Strategy, Hybrid Metaheuristic algorithm-Great Salmon Run-Thermal Exchange Optimization and eXplainable AI (XAI) tools like Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive exPlanations (SHAP) for their effectiveness. To evaluate the proposed method, eight DL models — Feedforward Neural Network, Recurrent Neural Network, Deep Neural Network, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Bidirectional LSTM, Gated Recurrent Unit (GRU) and Bidirectional GRU were trained on selected features using different FS methods, as well as complete dataset. The models were assessed using performance metrics such as accuracy, precision, recall, F1-Score, Loss, Validation Loss and computation time. The CNN model outperformed others, achieving an accuracy between 98%-99.5% for all FS methods. Statistical tests, including the Friedman and Nemenyi post-hoc test, identified the CNN model trained with MHMXAI-selected features as the most robust choice for CKD stage prediction. These findings demonstrate that the proposed MHMXAI method effectively integrates metaheuristic algorithms and XAI tools, improving CKD stage prediction accuracy and clinical interpretability.

慢性肾脏疾病（CKD）变得越来越普遍，导致肾功能逐渐下降，最终导致肾功能衰竭。及时发现CKD阶段对于提高医疗服务和降低发病率和死亡率至关重要。因此，本研究提出了一种元启发式-混合元启发式可解释人工智能（MHMXAI）驱动的特征选择（FS）方法和深度学习（DL）模型用于CKD阶段预测。MHMXAI方法从元启发式算法-鹰搜索策略，混合元启发式算法-大鲑鱼运行-热交换优化和可解释AI （XAI）工具（如局部可解释模型不可知解释（LIME）和Shapley加性解释（SHAP））中选择得分最高的特征。为了评估所提出的方法，使用不同的FS方法和完整的数据集对8个深度学习模型-前馈神经网络、循环神经网络、深度神经网络、卷积神经网络（CNN）、长短期记忆（LSTM）、双向LSTM、门控循环单元（GRU）和双向GRU进行了训练。使用准确性、精密度、召回率、F1-Score、损失、验证损失和计算时间等性能指标对模型进行评估。CNN模型优于其他模型，所有FS方法的准确率在98%-99.5%之间。统计检验，包括Friedman和Nemenyi事后检验，确定了用mhmxai选择的特征训练的CNN模型是CKD阶段预测的最稳健选择。这些发现表明，所提出的MHMXAI方法有效地整合了元启发式算法和XAI工具，提高了CKD分期预测的准确性和临床可解释性。

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引用次数: 0

A hybrid deep learning framework for early detection of Mpox using image data 基于图像数据的Mpox早期检测混合深度学习框架

Healthcare analytics (New York, N.Y.)

Pub Date : 2025-05-14 DOI: 10.1016/j.health.2025.100396

Sajal Chakroborty

Infectious diseases pose significant global threats to public health and economic stability by causing pandemics. Early detection of infectious diseases is crucial to prevent global outbreaks. Mpox, a contagious viral disease first detected in humans in 1970, has experienced multiple epidemics in recent decades, emphasizing the development of tools for its early detection. In this paper, we propose a hybrid deep learning framework for Mpox detection. This framework allows us to construct hybrid deep learning models combining deep learning architectures as a feature extraction tool with machine learning classifiers and perform a comprehensive analysis of Mpox detection from image data. Our best-performing model consists of MobileNetV2 with LightGBM classifier, which achieves an accuracy of 91.49%, precision of 86.96%, weighted precision of 91.87%, recall of 95.24%, weighted recall of 91.49%, F1 score of 90.91%, weighted F1-score of 91.51% and Matthews Correlation Coefficient score of 0.83.

传染病通过引起大流行，对公共卫生和经济稳定构成重大的全球威胁。早期发现传染病对预防全球疫情至关重要。m痘是1970年首次在人类中发现的一种传染性病毒疾病，近几十年来经历了多次流行，强调了早期发现工具的开发。在本文中，我们提出了一种用于Mpox检测的混合深度学习框架。该框架允许我们构建混合深度学习模型，将深度学习架构作为特征提取工具与机器学习分类器相结合，并从图像数据中执行Mpox检测的综合分析。我们表现最好的模型由带有LightGBM分类器的MobileNetV2组成，其准确率为91.49%，精度为86.96%，加权精度为91.87%，召回率为95.24%，加权召回率为91.49%，F1得分为90.91%，加权F1得分为91.51%，马修斯相关系数得分为0.83。

引用次数: 0

A predictive healthcare model using machine learning and psychological factors for medication adherence 使用机器学习和药物依从性心理因素的预测性医疗保健模型

Healthcare analytics (New York, N.Y.)

Pub Date : 2025-05-03 DOI: 10.1016/j.health.2025.100397

Junwu Dong , Minyi Chu , Yirou Xu

Ensuring effective medication adherence is vital for managing chronic diseases, yet global patient adherence remains suboptimal. This study aims to develop a predictive model for medication adherence behaviour (MAB) employing machine learning techniques, addressing the limitations of traditional correlation-based approaches. Based on the Meta-Theoretic Model of Motivation and Personality (3M Model), data from 428 chronic disease patients, included dark triad traits (narcissism, Machiavellianism, psychopathy), general self-efficacy, doctor-patient trust, and demographic variables. Five machine learning algorithms – multiple logistic regression, decision tree, adaptive boosting, random forest and support vector machine (SVM) – were utilized to identify MAB levels and assess feature importance. Among these, the random forest model achieved the highest performance, with an accuracy of 0.637, recall of 0.538, precision of 0.556, and an F1 score of 0.544. Feature ranking revealed that narcissism, Machiavellianism, doctor-patient trust, psychopathy, and general self-efficacy were the most influential predictors. These findings demonstrate that integrating psychological and demographic factors into machine learning models can enhance the prediction of medication adherence. This study presents a novel interdisciplinary framework that integrates behavioural health analytics and data science to inform clinical decision-making. It provides valuable insights into the severity and temporal progression of medication adherence behaviours, offering clinicians a practical reference for developing more effective intervention strategies.

确保有效的药物依从性对于管理慢性病至关重要，但全球患者依从性仍然不理想。本研究旨在利用机器学习技术开发药物依从行为（MAB）的预测模型，解决传统基于相关性方法的局限性。基于动机与人格元理论模型（3M模型），研究了428例慢性疾病患者的黑暗三合一特征（自恋、马基雅维利主义、精神病）、一般自我效能、医患信任和人口统计学变量。五种机器学习算法-多元逻辑回归，决策树，自适应增强，随机森林和支持向量机(SVM) -用于识别MAB水平和评估特征重要性。其中，随机森林模型的准确率为0.637，召回率为0.538，精度为0.556，F1得分为0.544。特征排序显示，自恋、马基雅维利主义、医患信任、精神病和一般自我效能是最具影响力的预测因子。这些发现表明，将心理和人口因素整合到机器学习模型中可以增强对药物依从性的预测。本研究提出了一个新的跨学科框架，将行为健康分析和数据科学相结合，为临床决策提供信息。它为药物依从性行为的严重程度和时间进展提供了有价值的见解，为临床医生制定更有效的干预策略提供了实用参考。

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引用次数: 0

An analytical transmission model for evaluating pneumonia vaccination and control strategies 评估肺炎疫苗接种和控制策略的分析传播模型

Healthcare analytics (New York, N.Y.)

Pub Date : 2025-04-27 DOI: 10.1016/j.health.2025.100394

Dipo Aldila , Abdullah Hasan Hassan , Mohamad Hifzhudin Noor Aziz , Putri Zahra Kamalia

Pneumonia is an infectious disease caused by various agents, such as viruses, bacteria, or fungi. This study proposes an analytical pneumonia model to assess the impact of vaccine interventions. The proposed mathematical model reveals that pneumonia will be eradicated from the population if the basic reproduction number is less than one. Furthermore, our bifurcation analysis indicates the absence of a backward bifurcation, meaning that the basic reproduction number is the sole threshold for determining the endemicity of a disease. In other words, pneumonia will be extinct if the basic reproduction number is less than one and will exist if it is larger than one. We estimate our model parameter values using incidence data from five districts in Jakarta, Indonesia. The dataset consists of weekly incidence data from 2023 until mid-2024. Our analysis shows North Jakarta has the highest case incidence per 100,000 individuals compared to the other districts. A global sensitivity analysis, using the partial rank correlation coefficient and Latin hypercube sampling, was conducted to identify the most impactful parameters on the basic reproduction number for each district in Jakarta. An optimal control problem was formulated to determine the most effective strategies for controlling pneumonia in the field. We found that adult vaccination has a greater impact on reducing the spread of pneumonia than a newborn vaccination strategy. However, combining both newborn and adult vaccinations is essential to ensure long-lasting immunity in children.

肺炎是一种传染病，由多种病原体引起，如病毒、细菌或真菌。本研究提出了一个分析性肺炎模型来评估疫苗干预的影响。提出的数学模型表明，如果基本繁殖数小于1，肺炎将从种群中被根除。此外，我们的分岔分析表明不存在向后分岔，这意味着基本繁殖数是确定疾病地方性的唯一阈值。也就是说，如果基本繁殖数小于1，肺炎就会灭绝，如果基本繁殖数大于1，肺炎就会存在。我们使用印度尼西亚雅加达五个地区的发病率数据估计模型参数值。该数据集包括从2023年到2024年中期的每周发病率数据。我们的分析显示，与其他地区相比，雅加达北部每10万人的发病率最高。利用偏秩相关系数和拉丁超立方抽样进行了全球敏感性分析，以确定对雅加达各区基本再生产数影响最大的参数。制定了一个最优控制问题，以确定在现场控制肺炎的最有效策略。我们发现，与新生儿疫苗接种策略相比，成人疫苗接种对减少肺炎传播的影响更大。然而，结合新生儿和成人疫苗接种对于确保儿童长期免疫至关重要。

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引用次数: 0

An integrated machine learning and hyperparameter optimization framework for noninvasive creatinine estimation using photoplethysmography signals 一个集成的机器学习和超参数优化框架，用于利用光容积脉搏波信号进行无创肌酐估计

Healthcare analytics (New York, N.Y.)

Pub Date : 2025-04-22 DOI: 10.1016/j.health.2025.100395

Parama Sridevi, Zawad Arefin, Sheikh Iqbal Ahamed

Frequent measurement of creatinine levels is vital for patients with chronic kidney disease. Traditional creatinine level measurement requires invasive blood test which has several disadvantages like discomfort, anxiety, panic, pain, risk of infection, etc. To address the issue, we propose a noninvasive machine learning (ML) model-based method to estimate creatinine level using photoplethysmography (PPG) signal. We obtained the PPG signal and gold-standard serum creatinine level of 404 patients from the Medical News Mart for Concentrated Care III (MIMIC III) database. In data preprocessing, we analyzed the PPG signal following several steps and created PPG feature set. We used multiple feature engineering methods to identify the most important features. We integrated Optuna, a hyperparameter optimization framework, with every ML model to get the optimal hyperparameters. We developed five ML models and compared their performance both with and without the application of Optuna. We found that Optuna significantly improves every model's performance. With Optuna, extreme gradient boosting (XGBoost) performed best among all five models. This XGBoost model had an accuracy of 85.2 %, an average k-fold cross validation score (k = 10) of 0.70, and a “receiver operating characteristic area under the curve” (ROC-AUC) score of 0.80. With the high performance exhibited by our developed model, the study can play a crucial role in the field of noninvasive creatinine estimation and diagnosis of chronic kidney disease.

经常测量肌酐水平对慢性肾病患者至关重要。传统的肌酐水平检测需要进行有创性血液检测，存在不适、焦虑、恐慌、疼痛、感染风险等缺点。为了解决这个问题，我们提出了一种基于无创机器学习（ML）模型的方法，利用光容积脉搏波（PPG）信号来估计肌酐水平。我们从医学新闻市场集中护理III （MIMIC III）数据库中获得404例患者的PPG信号和金标准血清肌酐水平。在数据预处理中，我们按照几个步骤分析了PPG信号，并创建了PPG特征集。我们使用多种特征工程方法来识别最重要的特征。我们将超参数优化框架Optuna与每个ML模型集成，以获得最优的超参数。我们开发了五个ML模型，并比较了它们在使用和不使用Optuna的情况下的性能。我们发现Optuna显著提高了每个模型的性能。对于Optuna，极端梯度增强（XGBoost）在所有五种模型中表现最好。该XGBoost模型准确率为85.2%，平均k-fold交叉验证分数（k = 10）为0.70，“曲线下受试者工作特征面积”（ROC-AUC）分数为0.80。该模型具有良好的性能，在无创肌酸酐评估和慢性肾脏疾病诊断领域具有重要意义。

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