Healthcare analytics (New York, N.Y.)最新文献

An analytics-based framework for early detection of cervical cancer using predictive modeling 基于分析的宫颈癌早期检测框架的预测建模

Healthcare analytics (New York, N.Y.)

Pub Date : 2025-12-12 DOI: 10.1016/j.health.2025.100442

Wirapong Chansanam , Kittichai Nilubol , Pichayada Suphajaroonshab , Chunqiu Li

This study aims to develop and evaluate advanced machine learning (ML) models for accurate and scalable early detection of cervical cancer, addressing critical limitations in current diagnostic practices. In leveraging exploratory data analysis (EDA), rigorous data preprocessing, and multiple ML techniques—including Random Forest, ANN, SVM, XGBoost, and ensemble models—we systematically analyzed a comprehensive dataset from the UCI repository comprising demographic, clinical, and behavioral features. Results indicated that the Random Forest model achieved the highest performance, with an accuracy of 98.4 %, a sensitivity of 99.3 %, and a specificity of 97.6 %, substantially surpassing the other evaluated models. Despite limitations related to dataset homogeneity and potential biases introduced by synthetic oversampling methods, these findings represent significant methodological and practical advancements. By offering an interpretable and robust diagnostic tool, the study significantly contributes to the improvement of cervical cancer detection, particularly benefitting low-resource clinical environments where effective, scalable screening methods are urgently needed. The proposed framework—developed and evaluated solely on the UCI tabular cervical cancer dataset—achieved high discriminative performance with the Random Forest model (accuracy = 98.4 %, sensitivity = 99.3 %, specificity = 97.6 %). A previously published imaging-based ResNet-50 model (AUC = 0.97) is referenced for contextual comparison only and was not part of our experimental work. However, deployment in resource-constrained environments will require further optimization and cost-efficiency analyses to confirm feasibility.

本研究旨在开发和评估先进的机器学习（ML）模型，用于准确和可扩展的宫颈癌早期检测，解决当前诊断实践中的关键限制。利用探索性数据分析（EDA）、严格的数据预处理和多种机器学习技术（包括随机森林、人工神经网络、支持向量机、XGBoost和集成模型），我们系统地分析了来自UCI存储库的综合数据集，包括人口统计、临床和行为特征。结果表明，随机森林模型取得了最高的性能，准确率为98.4% %，灵敏度为99.3 %，特异性为97.6% %，大大超过了其他评估模型。尽管存在与数据集同质性和合成过采样方法引入的潜在偏差相关的局限性，但这些发现代表了方法和实践上的重大进步。通过提供一种可解释的、强大的诊断工具，该研究显著有助于提高宫颈癌的检测，特别是有利于资源匮乏的临床环境，这些环境迫切需要有效的、可扩展的筛查方法。拟议中的framework-developed和评估仅仅在UCI表格宫颈癌dataset-achieved高区别的性能与随机森林模型(精度 = 98.4  %,敏感性 = 99.3  %,特异性 = 97.6 %)。先前发表的基于成像的ResNet-50模型（AUC = 0.97）仅用于上下文比较，而不是我们实验工作的一部分。然而，在资源受限的环境中部署，需要进一步优化和成本效益分析，以确认可行性。

{"title":"An analytics-based framework for early detection of cervical cancer using predictive modeling","authors":"Wirapong Chansanam , Kittichai Nilubol , Pichayada Suphajaroonshab , Chunqiu Li","doi":"10.1016/j.health.2025.100442","DOIUrl":"10.1016/j.health.2025.100442","url":null,"abstract":"<div><div>This study aims to develop and evaluate advanced machine learning (ML) models for accurate and scalable early detection of cervical cancer, addressing critical limitations in current diagnostic practices. In leveraging exploratory data analysis (EDA), rigorous data preprocessing, and multiple ML techniques—including Random Forest, ANN, SVM, XGBoost, and ensemble models—we systematically analyzed a comprehensive dataset from the UCI repository comprising demographic, clinical, and behavioral features. Results indicated that the Random Forest model achieved the highest performance, with an accuracy of 98.4 %, a sensitivity of 99.3 %, and a specificity of 97.6 %, substantially surpassing the other evaluated models. Despite limitations related to dataset homogeneity and potential biases introduced by synthetic oversampling methods, these findings represent significant methodological and practical advancements. By offering an interpretable and robust diagnostic tool, the study significantly contributes to the improvement of cervical cancer detection, particularly benefitting low-resource clinical environments where effective, scalable screening methods are urgently needed. The proposed framework—developed and evaluated solely on the UCI tabular cervical cancer dataset—achieved high discriminative performance with the Random Forest model (accuracy = 98.4 %, sensitivity = 99.3 %, specificity = 97.6 %). A previously published imaging-based ResNet-50 model (AUC = 0.97) is referenced for contextual comparison only and was not part of our experimental work. However, deployment in resource-constrained environments will require further optimization and cost-efficiency analyses to confirm feasibility.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"9 ","pages":"Article 100442"},"PeriodicalIF":0.0,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An ensemble learning approach for predicting hospital stay in transplant patients 预测移植患者住院时间的集成学习方法

Healthcare analytics (New York, N.Y.)

Pub Date : 2025-12-12 DOI: 10.1016/j.health.2025.100444

Zahra Gharibi

The rising incidence of heart and lung failure has increased the demand for effective transplant management strategies. Predicting Hospital Length of Stay (HLOS) is essential for reducing cost variability, optimizing resource utilization, and supporting patient recovery. This study uses data from the United Network for Organ Sharing (UNOS) to develop and validate an Ensemble Meta Stacked (EMS) model for predicting hospitalization duration after heart and lung transplantation. Expert-informed feature engineering incorporates donor and recipient compatibility measures, while a hybrid two-stage feature selection process combines expert evaluation with the Boruta algorithm to identify key predictors across demographic, clinical, behavioral, and geographical domains. Twelve predictive models are developed, including five base learners for each organ type and an EMS model that integrates their outputs through a Random Forest (RF) meta learner. Among the base learners, RF achieves the highest accuracy, but the EMS consistently outperforms all individual models. Sensitivity analysis confirms the robustness of model performance under different feature sources and scaling procedures, while paired statistical tests confirm that the improvement in predictive accuracy of EMS compared to the base learners is not due to random variation. The study also links predictive metrics to stakeholder priorities: policymakers and payers benefit from stable forecasts that control financial variability, hospital administrators rely on consistent prediction accuracy for capacity planning and resource allocation, and clinicians depend on bias-related metrics to guide safer discharge decisions. The EMS framework advances data-driven management in transplantation, supporting more efficient, equitable, and clinically responsible care.

心脏和肺衰竭的发病率上升，增加了对有效的移植管理策略的需求。预测住院时间（HLOS）对于降低成本可变性、优化资源利用和支持患者康复至关重要。本研究使用来自联合器官共享网络（UNOS）的数据来开发和验证用于预测心肺移植后住院时间的集成Meta堆叠（EMS）模型。专家信息特征工程结合了供体和受体的兼容性措施，而混合两阶段特征选择过程结合了专家评估和Boruta算法，以确定跨越人口统计学、临床、行为和地理领域的关键预测因素。开发了12个预测模型，包括每个器官类型的5个基本学习器和一个EMS模型，该模型通过随机森林（RF）元学习器集成了它们的输出。在基础学习器中，RF达到最高的准确率，但EMS始终优于所有单个模型。敏感性分析证实了模型性能在不同特征源和缩放程序下的稳健性，而配对统计检验证实了EMS与基础学习器相比预测精度的提高不是由于随机变化。该研究还将预测指标与利益相关者的优先事项联系起来：政策制定者和支付方受益于控制财务变化的稳定预测，医院管理人员依赖于容量规划和资源分配的一致预测准确性，临床医生依赖于与偏差相关的指标来指导更安全的出院决策。EMS框架推进了数据驱动的移植管理，支持更有效、公平和临床负责任的护理。

{"title":"An ensemble learning approach for predicting hospital stay in transplant patients","authors":"Zahra Gharibi","doi":"10.1016/j.health.2025.100444","DOIUrl":"10.1016/j.health.2025.100444","url":null,"abstract":"<div><div>The rising incidence of heart and lung failure has increased the demand for effective transplant management strategies. Predicting Hospital Length of Stay (HLOS) is essential for reducing cost variability, optimizing resource utilization, and supporting patient recovery. This study uses data from the United Network for Organ Sharing (UNOS) to develop and validate an Ensemble Meta Stacked (EMS) model for predicting hospitalization duration after heart and lung transplantation. Expert-informed feature engineering incorporates donor and recipient compatibility measures, while a hybrid two-stage feature selection process combines expert evaluation with the Boruta algorithm to identify key predictors across demographic, clinical, behavioral, and geographical domains. Twelve predictive models are developed, including five base learners for each organ type and an EMS model that integrates their outputs through a Random Forest (RF) meta learner. Among the base learners, RF achieves the highest accuracy, but the EMS consistently outperforms all individual models. Sensitivity analysis confirms the robustness of model performance under different feature sources and scaling procedures, while paired statistical tests confirm that the improvement in predictive accuracy of EMS compared to the base learners is not due to random variation. The study also links predictive metrics to stakeholder priorities: policymakers and payers benefit from stable forecasts that control financial variability, hospital administrators rely on consistent prediction accuracy for capacity planning and resource allocation, and clinicians depend on bias-related metrics to guide safer discharge decisions. The EMS framework advances data-driven management in transplantation, supporting more efficient, equitable, and clinically responsible care.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"9 ","pages":"Article 100444"},"PeriodicalIF":0.0,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An unsupervised machine learning approach for defining surge levels in emergency medical services 用于定义紧急医疗服务中激增水平的无监督机器学习方法

Healthcare analytics (New York, N.Y.)

Pub Date : 2025-12-08 DOI: 10.1016/j.health.2025.100443

Qixuan Zhao , Adair Collins , Judah Goldstein , Onur Pakkanlilar , Peter Vanberkel

A surge period occurs when demand significantly exceeds available capacity, creating operational strain in emergency medical services (EMS) and leading to measurable declines in system performance. Although surge levels are a critical metric for EMS operations, no established method exists for their objective definition. This study introduces a genetic algorithm-based unsupervised clustering model designed to define surge levels using EMS operational data. Unlike the National Emergency Department Overcrowding Scale, which depends on subjective assessments, the proposed approach objectively categorizes surge levels and supports regional customization through hyperparameter tuning and feature selection. The model's adaptability allows healthcare leaders to determine the desired number of surge-level categories and tailor the feature set to local operational needs. A case study in Nova Scotia, Canada, demonstrates the model's effectiveness, accurately identifying 88.96 % of busy periods with recall and precision of 96.49 % and 78.57 %, respectively. These results indicate that the approach provides a robust and flexible tool for defining surge levels, enabling data-driven decision-making in EMS system management.

当需求大大超过可用容量时，会出现激增期，这会给紧急医疗服务（EMS）造成运营压力，并导致系统性能明显下降。虽然电涌水平是EMS操作的一个关键指标，但没有既定的方法来确定其客观定义。本研究引入一种基于遗传算法的无监督聚类模型，设计用于使用EMS运行数据定义浪涌水平。与依赖主观评估的国家急诊科过度拥挤量表不同，拟议的方法客观地对激增水平进行分类，并通过超参数调整和特征选择支持区域定制。该模型的适应性使医疗保健领导者能够确定所需的激增级别类别数量，并根据本地操作需求定制功能集。以加拿大新斯科舍省为例，验证了该模型的有效性，准确识别出88.96 %的繁忙时段，查全率和查准率分别为96.49 %和78.57 %。这些结果表明，该方法为定义浪涌水平提供了一个强大而灵活的工具，使EMS系统管理中的数据驱动决策成为可能。

{"title":"An unsupervised machine learning approach for defining surge levels in emergency medical services","authors":"Qixuan Zhao , Adair Collins , Judah Goldstein , Onur Pakkanlilar , Peter Vanberkel","doi":"10.1016/j.health.2025.100443","DOIUrl":"10.1016/j.health.2025.100443","url":null,"abstract":"<div><div>A surge period occurs when demand significantly exceeds available capacity, creating operational strain in emergency medical services (EMS) and leading to measurable declines in system performance. Although surge levels are a critical metric for EMS operations, no established method exists for their objective definition. This study introduces a genetic algorithm-based unsupervised clustering model designed to define surge levels using EMS operational data. Unlike the National Emergency Department Overcrowding Scale, which depends on subjective assessments, the proposed approach objectively categorizes surge levels and supports regional customization through hyperparameter tuning and feature selection. The model's adaptability allows healthcare leaders to determine the desired number of surge-level categories and tailor the feature set to local operational needs. A case study in Nova Scotia, Canada, demonstrates the model's effectiveness, accurately identifying 88.96 % of busy periods with recall and precision of 96.49 % and 78.57 %, respectively. These results indicate that the approach provides a robust and flexible tool for defining surge levels, enabling data-driven decision-making in EMS system management.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"9 ","pages":"Article 100443"},"PeriodicalIF":0.0,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An analytical modeling framework for breast cancer progression and treatment evaluation 乳腺癌进展和治疗评估的分析建模框架

Healthcare analytics (New York, N.Y.)

Pub Date : 2025-12-08 DOI: 10.1016/j.health.2025.100441

H. Gholami , M. Gachpazan , M. Erfanian , M. Hasanzadeh

This paper presents a mathematical model of breast cancer composed of six compartments: one representing tumor cells, two representing cytokine populations, and three representing immune cell types. The proposed framework is original in that it integrates cytokine-mediated (IL-2 and IFN-

γ

) feedback loops, immune effector dynamics, and chemotherapeutic drug kinetics within a unified six-compartment structure. This coupling of tumor-immune-drug interactions, calibrated specifically for breast cancer, distinguishes the model from existing mathematical tumor-immune systems. To maintain simplicity and avoid unnecessary complexity, the study initially considers the interaction between tumor cells and the two cytokine groups. The results show that cytokines alone are insufficient to eliminate tumor cells. The analysis then extends to the interaction between tumor cells and the three immune cell types. Graphical simulations demonstrate that tumor cells can still evade immune cell responses. A dynamical analysis is conducted, proving the uniqueness and nonnegativity of the model solutions and identifying two types of equilibrium points. The existence conditions for each equilibrium are discussed. A transcritical bifurcation analysis (TBA) indicates that the tumor-free equilibrium loses stability at a critical tumor growth rate of 0.25 per day, beyond which a stable positive tumor state emerges. Comparison with clinical tumor growth data shows that the model accurately captures tumor dynamics, achieving a goodness-of-fit of 98.46 percent using nonlinear least squares (NLS) fitting. The full model, which incorporates immune cells, tumor cells, and a chemotherapeutic agent, is then presented. Mathematical techniques are applied to reduce the system, and the Adomian Decomposition Method (ADM) is used for analysis. The convergence of ADM in the context of the model is established and proved. Graphical results indicate that tumor cells can be eliminated under this treatment strategy. Phase-plane (PP) and vector field (VF) analyses reveal oscillatory immune responses and regulatory feedback among immune cells, while surface plots highlight the sensitivity of tumor suppression to key parameters. The findings suggest that effective treatment requires both reducing tumor proliferation and enhancing immune-mediated lysis. A sensitivity analysis (SA) identifies the most influential parameters in tumor control.

本文提出了一个由六个区室组成的乳腺癌数学模型：一个代表肿瘤细胞，两个代表细胞因子种群，三个代表免疫细胞类型。提出的框架是原创的，因为它将细胞因子介导的（IL-2和IFN-γ）反馈回路、免疫效应动力学和化疗药物动力学整合在一个统一的六室结构中。这种肿瘤-免疫-药物相互作用的耦合，专门针对乳腺癌进行校准，将该模型与现有的数学肿瘤-免疫系统区分开来。为了保持简单，避免不必要的复杂性，本研究初步考虑了肿瘤细胞与两组细胞因子之间的相互作用。结果表明，仅靠细胞因子不足以消灭肿瘤细胞。然后，分析扩展到肿瘤细胞和三种免疫细胞类型之间的相互作用。图形模拟表明，肿瘤细胞仍然可以逃避免疫细胞反应。进行了动力学分析，证明了模型解的唯一性和非负性，并确定了两类平衡点。讨论了各平衡的存在条件。跨临界分岔分析（trans - critical bif岔analysis， TBA）表明，当肿瘤生长速率为0.25 / d时，无瘤平衡失去稳定性，超过该速率后，肿瘤出现稳定的阳性状态。与临床肿瘤生长数据的比较表明，该模型准确捕获了肿瘤动力学，使用非线性最小二乘（NLS）拟合的拟合优度达到98.46%。完整的模型，其中包括免疫细胞，肿瘤细胞和化疗药物，然后提出。采用数学方法对系统进行约简，并采用阿多米亚分解法（ADM）进行分析。建立并证明了ADM在模型背景下的收敛性。图形结果表明，在这种治疗策略下，肿瘤细胞可以被消除。相平面（PP）和矢量场（VF）分析揭示了免疫细胞之间的振荡免疫反应和调节反馈，而表面图则突出了肿瘤抑制对关键参数的敏感性。研究结果表明，有效的治疗需要减少肿瘤增殖和增强免疫介导的溶解。敏感性分析（SA）确定了肿瘤控制中最具影响力的参数。

{"title":"An analytical modeling framework for breast cancer progression and treatment evaluation","authors":"H. Gholami , M. Gachpazan , M. Erfanian , M. Hasanzadeh","doi":"10.1016/j.health.2025.100441","DOIUrl":"10.1016/j.health.2025.100441","url":null,"abstract":"<div><div>This paper presents a mathematical model of breast cancer composed of six compartments: one representing tumor cells, two representing cytokine populations, and three representing immune cell types. The proposed framework is original in that it integrates cytokine-mediated (IL-2 and IFN-<span><math><mi>γ</mi></math></span>) feedback loops, immune effector dynamics, and chemotherapeutic drug kinetics within a unified six-compartment structure. This coupling of tumor-immune-drug interactions, calibrated specifically for breast cancer, distinguishes the model from existing mathematical tumor-immune systems. To maintain simplicity and avoid unnecessary complexity, the study initially considers the interaction between tumor cells and the two cytokine groups. The results show that cytokines alone are insufficient to eliminate tumor cells. The analysis then extends to the interaction between tumor cells and the three immune cell types. Graphical simulations demonstrate that tumor cells can still evade immune cell responses. A dynamical analysis is conducted, proving the uniqueness and nonnegativity of the model solutions and identifying two types of equilibrium points. The existence conditions for each equilibrium are discussed. A transcritical bifurcation analysis (TBA) indicates that the tumor-free equilibrium loses stability at a critical tumor growth rate of 0.25 per day, beyond which a stable positive tumor state emerges. Comparison with clinical tumor growth data shows that the model accurately captures tumor dynamics, achieving a goodness-of-fit of 98.46 percent using nonlinear least squares (NLS) fitting. The full model, which incorporates immune cells, tumor cells, and a chemotherapeutic agent, is then presented. Mathematical techniques are applied to reduce the system, and the Adomian Decomposition Method (ADM) is used for analysis. The convergence of ADM in the context of the model is established and proved. Graphical results indicate that tumor cells can be eliminated under this treatment strategy. Phase-plane (PP) and vector field (VF) analyses reveal oscillatory immune responses and regulatory feedback among immune cells, while surface plots highlight the sensitivity of tumor suppression to key parameters. The findings suggest that effective treatment requires both reducing tumor proliferation and enhancing immune-mediated lysis. A sensitivity analysis (SA) identifies the most influential parameters in tumor control.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"9 ","pages":"Article 100441"},"PeriodicalIF":0.0,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145705822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A multi-agent reinforcement learning framework for public health decision analysis 公共卫生决策分析的多智能体强化学习框架

Healthcare analytics (New York, N.Y.)

Pub Date : 2025-12-01 DOI: 10.1016/j.health.2025.100436

Dinesh Sharma , Ankit Shah , Chaitra Gopalappa

Human immunodeficiency virus (HIV) is a major public health concern in the United States (U.S.), with about 1.2 million people living with it and about 35,000 newly infected each year. There are considerable geographical disparities in HIV burden and care access across the U.S. The ’Ending the HIV Epidemic (EHE)’ initiative by the U.S. Department of Health and Human Services aims to reduce new infections by 90% by 2030, by improving coverage of diagnoses, treatment, and prevention interventions and prioritizing jurisdictions with high HIV prevalence. One of the approaches towards achieving this objective includes developing intelligent decision-support systems that can help optimize resource allocation and intervention strategies. Existing decision analytic models either focus on individual cities or aggregate national data, failing to capture jurisdictional interactions critical for optimizing intervention strategies. To address this, we propose a multi-agent reinforcement learning (MARL) framework that enables jurisdiction-specific decision-making while accounting for cross-jurisdictional epidemiological interactions. Our framework functions as an intelligent resource optimization system, helping policymakers strategically allocate interventions based on dynamic, data-driven insights. Experimental results across jurisdictions in California and Florida demonstrate that MARL-driven policies outperform traditional single-agent reinforcement learning approaches by reducing new infections under fixed budget constraints. Our study highlights the importance of incorporating jurisdictional dependencies in decision-making frameworks for large-scale public initiatives. By integrating multi-agent intelligent systems, decision analytics, and reinforcement learning, this study advances expert systems for government resource planning and public health management, offering a scalable framework for broader applications in healthcare policy and epidemic management.

人类免疫缺陷病毒（艾滋病毒）是美国一个主要的公共卫生问题，约有120万人感染艾滋病毒，每年约有3.5万名新感染者。美国各地在艾滋病毒负担和护理机会方面存在相当大的地域差异。美国卫生与公众服务部的“终结艾滋病毒流行（EHE）”倡议旨在通过提高诊断、治疗和预防干预的覆盖率，并优先考虑艾滋病毒高流行的司法管辖区，到2030年将新感染人数减少90%。实现这一目标的方法之一包括开发智能决策支持系统，以帮助优化资源分配和干预策略。现有的决策分析模型要么关注单个城市，要么关注汇总的国家数据，未能捕捉到对优化干预策略至关重要的管辖权相互作用。为了解决这个问题，我们提出了一个多智能体强化学习（MARL）框架，该框架可以在考虑跨司法管辖区流行病学相互作用的同时，实现特定司法管辖区的决策。我们的框架作为一个智能资源优化系统，帮助决策者根据动态的、数据驱动的见解战略性地分配干预措施。加州和佛罗里达州的实验结果表明，在固定预算约束下，marl驱动的政策通过减少新感染，优于传统的单智能体强化学习方法。我们的研究强调了在大规模公共倡议的决策框架中纳入司法依赖关系的重要性。通过整合多智能体智能系统、决策分析和强化学习，本研究为政府资源规划和公共卫生管理提供了专家系统，为医疗政策和流行病管理提供了一个可扩展的框架。

{"title":"A multi-agent reinforcement learning framework for public health decision analysis","authors":"Dinesh Sharma , Ankit Shah , Chaitra Gopalappa","doi":"10.1016/j.health.2025.100436","DOIUrl":"10.1016/j.health.2025.100436","url":null,"abstract":"<div><div>Human immunodeficiency virus (HIV) is a major public health concern in the United States (U.S.), with about 1.2 million people living with it and about 35,000 newly infected each year. There are considerable geographical disparities in HIV burden and care access across the U.S. The ’Ending the HIV Epidemic (EHE)’ initiative by the U.S. Department of Health and Human Services aims to reduce new infections by 90% by 2030, by improving coverage of diagnoses, treatment, and prevention interventions and prioritizing jurisdictions with high HIV prevalence. One of the approaches towards achieving this objective includes developing intelligent decision-support systems that can help optimize resource allocation and intervention strategies. Existing decision analytic models either focus on individual cities or aggregate national data, failing to capture jurisdictional interactions critical for optimizing intervention strategies. To address this, we propose a multi-agent reinforcement learning (MARL) framework that enables jurisdiction-specific decision-making while accounting for cross-jurisdictional epidemiological interactions. Our framework functions as an intelligent resource optimization system, helping policymakers strategically allocate interventions based on dynamic, data-driven insights. Experimental results across jurisdictions in California and Florida demonstrate that MARL-driven policies outperform traditional single-agent reinforcement learning approaches by reducing new infections under fixed budget constraints. Our study highlights the importance of incorporating jurisdictional dependencies in decision-making frameworks for large-scale public initiatives. By integrating multi-agent intelligent systems, decision analytics, and reinforcement learning, this study advances expert systems for government resource planning and public health management, offering a scalable framework for broader applications in healthcare policy and epidemic management.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100436"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145617925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An image-based analytics framework for early autism detection using eye movements 一个基于图像的分析框架，用于使用眼球运动进行早期自闭症检测

Healthcare analytics (New York, N.Y.)

Pub Date : 2025-12-01 DOI: 10.1016/j.health.2025.100439

Roaa Soloh , Lara Abou Orm , Dana Dabdoub

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition whose early detection is crucial for improving social and cognitive outcomes. Current diagnostic tools are often costly, subjective, and inaccessible to many clinics. This work presents GazeScan, an image-based analytics framework that identifies ASD from eye-tracking behavior using only standard video input. The system non-invasively performs gaze estimation via a 16-point geometric calibration and transforms gaze trajectories into grayscale scanpath images. These images are classified using a lightweight convolutional neural network. GazeScan was evaluated on the Eye-Tracking Scan Path (ETSP) dataset with five-fold cross-validation, achieving 97.01% accuracy and an AUC of 0.98. The model’s compact architecture enables real-time inference and mobile deployment without specialized hardware. The results obtained highlight the potential of accessible, AI-enabled digital screening tools to support early ASD detection and broader behavioral healthcare delivery.

自闭症谱系障碍（ASD）是一种复杂的神经发育疾病，其早期发现对于改善社会和认知结果至关重要。目前的诊断工具往往是昂贵的、主观的，而且许多诊所无法获得。这项工作提出了GazeScan，这是一个基于图像的分析框架，仅使用标准视频输入就可以从眼动跟踪行为中识别ASD。该系统通过16点几何校准非侵入性地进行凝视估计，并将凝视轨迹转换为灰度扫描路径图像。使用轻量级卷积神经网络对这些图像进行分类。GazeScan在眼动扫描路径（Eye-Tracking Scan Path， ETSP）数据集上进行5倍交叉验证，准确率达到97.01%，AUC为0.98。该模型的紧凑架构使实时推理和移动部署无需专门的硬件。获得的结果突出了可获得的、支持人工智能的数字筛查工具的潜力，以支持早期ASD检测和更广泛的行为医疗保健服务。

引用次数: 0

An investigation of treatment barriers for End-Stage Kidney Disease patients using advanced analytics 终末期肾病患者治疗障碍的高级分析研究

Healthcare analytics (New York, N.Y.)

Pub Date : 2025-12-01 DOI: 10.1016/j.health.2025.100438

Olga Bountali , Sila Cetinkaya , Michael Hahsler , Farnaz Nourbakhsh , Zhenghang Xu , Henry Quinones

This study uses advanced analytics to investigate the treatment barriers faced by unfunded patients suffering from end-stage kidney disease at Parkland Hospital. Under the Emergency Medical Treatment and Labor Act (EMTALA) federal law, these patients can receive dialysis only under emergency conditions. This practice, commonly known as “emergent dialysis,” routes patients through the Emergency Room (ER) for a screening assessment to determine whether they will be accepted for treatment. Utilizing a data set from Parkland Hospital on patient ER visits seeking emergent dialysis, we leverage descriptive analytics and statistical methods to investigate (i) the impact of this accept/reject decision process on patient outcomes and (ii) the potential influence of operational, medical, and behavioral factors, such as the ER load, patient acuity level, and accept/reject patient history on it. Our research highlights an unanticipated burden caused by a subset of occasional dialysis patients with notably infrequent visits—the aspect that should not be overlooked. It also pinpoints discrepancies across patients, e.g., counterintuitively, patients accepted for treatment experienced shorter wait times before the decision was made than those rejected. More importantly, our work reveals that operational and behavioral factors influence the decision-making process substantially, much more than medical ones. The above findings underscore the critical role of analytics in our model. Our work further employs prescriptive analytics and simulation optimization approaches to provide recommendations on how policymakers can leverage the insights above to make more effective decisions that improve care delivery for this vulnerable population.

本研究使用先进的分析方法来调查在Parkland医院无资金支持的终末期肾病患者所面临的治疗障碍。根据《紧急医疗和劳工法》（EMTALA）联邦法律，这些患者只能在紧急情况下接受透析。这种做法，通常被称为“紧急透析”，将患者通过急诊室（ER）进行筛选评估，以确定他们是否将被接受治疗。利用来自帕克兰医院急诊室寻求紧急透析的患者就诊数据集，我们利用描述性分析和统计方法来调查(i)这种接受/拒绝决策过程对患者结果的影响，以及（ii）操作、医疗和行为因素的潜在影响，如急诊室负荷、患者的视力水平和接受/拒绝患者的病史。我们的研究强调了一个意想不到的负担，由偶尔透析患者的一个子集引起，特别是不频繁的访问，这方面不应该被忽视。它还指出了患者之间的差异，例如，与直觉相反，接受治疗的患者在做出决定之前的等待时间比拒绝治疗的患者短。更重要的是，我们的研究表明，操作和行为因素对决策过程的影响要比医疗因素大得多。上述发现强调了分析在我们的模型中的关键作用。我们的工作进一步采用规范性分析和模拟优化方法，为政策制定者如何利用上述见解做出更有效的决策提供建议，以改善对弱势群体的护理服务。

{"title":"An investigation of treatment barriers for End-Stage Kidney Disease patients using advanced analytics","authors":"Olga Bountali , Sila Cetinkaya , Michael Hahsler , Farnaz Nourbakhsh , Zhenghang Xu , Henry Quinones","doi":"10.1016/j.health.2025.100438","DOIUrl":"10.1016/j.health.2025.100438","url":null,"abstract":"<div><div>This study uses advanced analytics to investigate the treatment barriers faced by unfunded patients suffering from end-stage kidney disease at Parkland Hospital. Under the Emergency Medical Treatment and Labor Act (EMTALA) federal law, these patients can receive dialysis only under emergency conditions. This practice, commonly known as “emergent dialysis,” routes patients through the Emergency Room (ER) for a screening assessment to determine whether they will be accepted for treatment. Utilizing a data set from Parkland Hospital on patient ER visits seeking emergent dialysis, we leverage descriptive analytics and statistical methods to investigate (i) the impact of this accept/reject decision process on patient outcomes and (ii) the potential influence of operational, medical, and behavioral factors, such as the ER load, patient acuity level, and accept/reject patient history on it. Our research highlights an unanticipated burden caused by a subset of occasional dialysis patients with notably infrequent visits—the aspect that should not be overlooked. It also pinpoints discrepancies across patients, e.g., counterintuitively, patients accepted for treatment experienced shorter wait times before the decision was made than those rejected. More importantly, our work reveals that operational and behavioral factors influence the decision-making process substantially, much more than medical ones. The above findings underscore the critical role of analytics in our model. Our work further employs prescriptive analytics and simulation optimization approaches to provide recommendations on how policymakers can leverage the insights above to make more effective decisions that improve care delivery for this vulnerable population.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100438"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145617926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An analytics-based model for securing the healthcare drug distribution network with blockchain 使用b区块链保护医疗药品分销网络的基于分析的模型

Healthcare analytics (New York, N.Y.)

Pub Date : 2025-12-01 DOI: 10.1016/j.health.2025.100440

Herry Irawan , Adinda Amalia Putri Abidin , Andry Alamsyah

The surge of counterfeit pharmaceuticals and vaccines in Indonesia poses a significant public health threat, compromising treatment effectiveness and jeopardising patient safety. Current pharmaceutical supply chain systems are constrained in scalability, transparency, and real-time verification, impeding initiatives to guarantee medicine authenticity, including expiration and dose accuracy. This paper suggests a blockchain-based paradigm specifically designed for Indonesia's pharmaceutical supply chain, focusing on traceability, security, and regulatory compliance.

The research employs a qualitative methodology that incorporates literature review, stakeholder interviews, and interface prototyping. A smart contract simulation is executed to verify essential supply chain operations, including medicine serialization, batch approval, recall, and dispensing control. The experimental assessment indicates that the contract operates with minimal latency and deterministic enforcement, guaranteeing dependable real-time validation at the point of care.

The preliminary results indicate that the blockchain prototype augments traceability, mitigates counterfeit distribution, and facilitates coordination among stakeholders, including producers, regulators, healthcare professionals, and patients. The results highlight blockchain's capacity to facilitate policy reform and digital transformation in pharmaceutical governance, enhancing regulatory compliance and public health outcomes in Indonesia.

印度尼西亚假冒药品和疫苗的激增对公共卫生构成重大威胁，损害了治疗效果并危及患者安全。当前的药品供应链系统在可扩展性、透明度和实时验证方面受到限制，阻碍了保证药品真实性（包括有效期和剂量准确性）的举措。本文提出了一个专门为印度尼西亚制药供应链设计的基于区块链的范例，重点是可追溯性、安全性和合规性。该研究采用了一种定性方法，结合了文献回顾、利益相关者访谈和界面原型。执行智能合约模拟以验证必要的供应链操作，包括药品序列化、批批准、召回和配药控制。实验评估表明，该合约以最小的延迟和确定性执行运行，保证了在护理点的可靠实时验证。初步结果表明，区块链原型增强了可追溯性，减少了假冒产品的分销，并促进了利益相关者（包括生产商、监管机构、医疗保健专业人员和患者）之间的协调。结果表明，b区块链有能力促进印度尼西亚药品治理方面的政策改革和数字化转型，加强监管合规和公共卫生成果。

{"title":"An analytics-based model for securing the healthcare drug distribution network with blockchain","authors":"Herry Irawan , Adinda Amalia Putri Abidin , Andry Alamsyah","doi":"10.1016/j.health.2025.100440","DOIUrl":"10.1016/j.health.2025.100440","url":null,"abstract":"<div><div>The surge of counterfeit pharmaceuticals and vaccines in Indonesia poses a significant public health threat, compromising treatment effectiveness and jeopardising patient safety. Current pharmaceutical supply chain systems are constrained in scalability, transparency, and real-time verification, impeding initiatives to guarantee medicine authenticity, including expiration and dose accuracy. This paper suggests a blockchain-based paradigm specifically designed for Indonesia's pharmaceutical supply chain, focusing on traceability, security, and regulatory compliance.</div><div>The research employs a qualitative methodology that incorporates literature review, stakeholder interviews, and interface prototyping. A smart contract simulation is executed to verify essential supply chain operations, including medicine serialization, batch approval, recall, and dispensing control. The experimental assessment indicates that the contract operates with minimal latency and deterministic enforcement, guaranteeing dependable real-time validation at the point of care.</div><div>The preliminary results indicate that the blockchain prototype augments traceability, mitigates counterfeit distribution, and facilitates coordination among stakeholders, including producers, regulators, healthcare professionals, and patients. The results highlight blockchain's capacity to facilitate policy reform and digital transformation in pharmaceutical governance, enhancing regulatory compliance and public health outcomes in Indonesia.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100440"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145683793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An attention-guided graph spiking approach for seizure localization and detection in healthcare 在医疗保健中用于癫痫定位和检测的注意引导图尖峰方法

Healthcare analytics (New York, N.Y.)

Pub Date : 2025-11-14 DOI: 10.1016/j.health.2025.100437

Resmi Cherian , E. Grace Mary Kanaga

Epilepsy is a chronic neurological disorder with recurrent seizures, posing significant challenges for timely diagnosis and treatment. The manual identification of seizures from long-term EEG is labour-intensive, time-consuming, and heavily dependent on expertise, which underscores the need for automated seizure detection systems. This study proposes a patient-specific hybrid Graph Neural Network–Spiking Neural Network (GNN–SNN) framework, integrating attention-driven channel importance estimation with graph-based spiking dynamics for interpretable seizure detection. The EEG channels are represented as graph nodes with attention layers modeling their spatial relationships, and Adaptive Leaky Integrate-and-Fire neurons represent biologically inspired temporal dynamics. A key feature of the framework is its capacity to measure channel-wise contributions through attention weights, which yields human-interpretable information about which EEG channels contribute most to seizure detection. Each model is trained and optimized independently for every patient to learn their unique spatiotemporal EEG patterns, preserving the patient-specific design while maintaining a uniform architectural pattern for all subjects. The proposed model achieves robust performance on the CHB-MIT dataset, with 98.94 % accuracy, 95.01 % sensitivity, and 99.23 % specificity, while improving interpretability for EEG-based seizure detection. Although this model emphasizes dominant EEG channels, the model is not suitable for clinical localization and would require validation by intracranial EEG (iEEG) for translational applications. The integration of graph attention mechanisms with spiking computation provides better seizure detection with physiologically interpretable insights into EEG channel contributions. Future work will focus on validating these interpretability results on clinical gold standards, generalizing to patient-independent scenarios, and scaling up the framework for energy-efficient, real-time seizure monitoring.

癫痫是一种反复发作的慢性神经系统疾病，对及时诊断和治疗提出了重大挑战。从长期脑电图中手动识别癫痫发作是劳动密集型的，耗时的，并且严重依赖于专业知识，这强调了对自动癫痫发作检测系统的需求。本研究提出了一种针对患者的混合图神经网络-峰值神经网络（GNN-SNN）框架，将注意力驱动的通道重要性估计与基于图的峰值动态相结合，用于可解释的癫痫检测。脑电通道被表示为图节点，注意层建模了它们的空间关系，而自适应的Leaky - integre -and- fire神经元代表了生物学启发的时间动态。该框架的一个关键特征是它能够通过注意权重来衡量通道的贡献，从而产生关于哪些脑电图通道对癫痫检测贡献最大的人类可解释信息。每个模型都针对每个患者进行独立的训练和优化，以学习其独特的时空脑电图模式，在保留患者特定设计的同时保持所有受试者的统一架构模式。该模型在CHB-MIT数据集上实现了稳健的性能，准确率为98.94 %，灵敏度为95.01 %，特异性为99.23 %，同时提高了基于脑电图的癫痫发作检测的可解释性。虽然该模型强调脑电主导通道，但该模型不适合临床定位，需要颅内脑电图（iEEG）验证才能应用于翻译。图注意机制与尖峰计算的集成提供了更好的癫痫检测与生理上可解释的见解脑电图通道的贡献。未来的工作将侧重于在临床金标准上验证这些可解释性结果，推广到与患者无关的场景，并扩大节能、实时癫痫监测的框架。

{"title":"An attention-guided graph spiking approach for seizure localization and detection in healthcare","authors":"Resmi Cherian , E. Grace Mary Kanaga","doi":"10.1016/j.health.2025.100437","DOIUrl":"10.1016/j.health.2025.100437","url":null,"abstract":"<div><div>Epilepsy is a chronic neurological disorder with recurrent seizures, posing significant challenges for timely diagnosis and treatment. The manual identification of seizures from long-term EEG is labour-intensive, time-consuming, and heavily dependent on expertise, which underscores the need for automated seizure detection systems. This study proposes a patient-specific hybrid Graph Neural Network–Spiking Neural Network (GNN–SNN) framework, integrating attention-driven channel importance estimation with graph-based spiking dynamics for interpretable seizure detection. The EEG channels are represented as graph nodes with attention layers modeling their spatial relationships, and Adaptive Leaky Integrate-and-Fire neurons represent biologically inspired temporal dynamics. A key feature of the framework is its capacity to measure channel-wise contributions through attention weights, which yields human-interpretable information about which EEG channels contribute most to seizure detection. Each model is trained and optimized independently for every patient to learn their unique spatiotemporal EEG patterns, preserving the patient-specific design while maintaining a uniform architectural pattern for all subjects. The proposed model achieves robust performance on the CHB-MIT dataset, with 98.94 % accuracy, 95.01 % sensitivity, and 99.23 % specificity, while improving interpretability for EEG-based seizure detection. Although this model emphasizes dominant EEG channels, the model is not suitable for clinical localization and would require validation by intracranial EEG (iEEG) for translational applications. The integration of graph attention mechanisms with spiking computation provides better seizure detection with physiologically interpretable insights into EEG channel contributions. Future work will focus on validating these interpretability results on clinical gold standards, generalizing to patient-independent scenarios, and scaling up the framework for energy-efficient, real-time seizure monitoring.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100437"},"PeriodicalIF":0.0,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145578577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An analytical study of worker well-being and COVID-19 impact using Bayesian panel modeling 使用贝叶斯面板模型对工人幸福感和COVID-19影响的分析研究

Healthcare analytics (New York, N.Y.)

Pub Date : 2025-11-14 DOI: 10.1016/j.health.2025.100434

Makoto Nakakita , Tomoki Toyabe , Naoki Kubota , Wakuo Saito , Teruo Nakatsuma

This study investigates how the determinants of Japanese workers’ well-being shifted before and during the COVID-19 pandemic. We estimate a Bayesian hierarchical panel model and Markov chain Monte Carlo sampling is implemented with the ancillarity–sufficiency interweaving strategy to handle the high parameter-to-sample ratio efficiently. Consequently, we observed that positive drivers include marriage, good health, job satisfaction, and conversion from nonregular to regular employment, whereas male gender, turnover intention, reduced family contact, and pandemic-related financial concerns lower well-being. Age traces a U-shape, and weekday sleep shows an inverse-U pattern. Although the evidence is correlational and confined to self-reported data from one country, the analysis clarifies how socio-economic and workplace factors interact with a major external shock.

本研究调查了在COVID-19大流行之前和期间，日本工人福祉的决定因素是如何变化的。我们估计了一个贝叶斯层次面板模型，并采用辅助-充分交织策略实现马尔可夫链蒙特卡罗抽样，以有效地处理高参数样本比。因此，我们观察到，积极的驱动因素包括婚姻、身体健康、工作满意度和从非正规就业到正规就业的转变，而男性性别、离职意愿、家庭联系减少和与大流行相关的财务担忧会降低幸福感。年龄呈u型，工作日睡眠呈反u型。尽管证据是相关的，而且仅限于一个国家的自我报告数据，但该分析澄清了社会经济和工作场所因素如何与重大外部冲击相互作用。

引用次数: 0