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

{"title":"An analytical modeling framework for breast cancer progression and treatment evaluation","authors":"H. Gholami ,&nbsp;M. Gachpazan ,&nbsp;M. Erfanian ,&nbsp;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}

{"title":"A multi-agent reinforcement learning framework for public health decision analysis","authors":"Dinesh Sharma ,&nbsp;Ankit Shah ,&nbsp;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}

{"title":"An image-based analytics framework for early autism detection using eye movements","authors":"Roaa Soloh ,&nbsp;Lara Abou Orm ,&nbsp;Dana Dabdoub","doi":"10.1016/j.health.2025.100439","DOIUrl":"10.1016/j.health.2025.100439","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100439"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145617927","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}

{"title":"An investigation of treatment barriers for End-Stage Kidney Disease patients using advanced analytics","authors":"Olga Bountali ,&nbsp;Sila Cetinkaya ,&nbsp;Michael Hahsler ,&nbsp;Farnaz Nourbakhsh ,&nbsp;Zhenghang Xu ,&nbsp;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}

{"title":"An analytics-based model for securing the healthcare drug distribution network with blockchain","authors":"Herry Irawan ,&nbsp;Adinda Amalia Putri Abidin ,&nbsp;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}

{"title":"An attention-guided graph spiking approach for seizure localization and detection in healthcare","authors":"Resmi Cherian ,&nbsp;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}

{"title":"An analytical study of worker well-being and COVID-19 impact using Bayesian panel modeling","authors":"Makoto Nakakita ,&nbsp;Tomoki Toyabe ,&nbsp;Naoki Kubota ,&nbsp;Wakuo Saito ,&nbsp;Teruo Nakatsuma","doi":"10.1016/j.health.2025.100434","DOIUrl":"10.1016/j.health.2025.100434","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100434"},"PeriodicalIF":0.0,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145578579","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}

{"title":"An artificial intelligence-based approach for human parasite egg segmentation and classification","authors":"Sohag Kumar Mondal ,&nbsp;Monira Islam ,&nbsp;Md. Salah Uddin Yusuf","doi":"10.1016/j.health.2025.100432","DOIUrl":"10.1016/j.health.2025.100432","url":null,"abstract":"<div><div>Intestinal parasitic infections are a significant global health issue, recognized by the World Health Organization (WHO) as a major cause of disease. Current diagnostic methods rely on labor-intensive manual examination of fecal samples under a microscope. This study aims to overcome these challenges by leveraging artificial intelligence (AI) to automate the identification of parasitic eggs in laboratory settings. To enhance image clarity and remove noise from microscopic fecal images, we employed the Block-Matching and 3D Filtering (BM3D) technique, which effectively addresses Gaussian, Salt and Pepper, Speckle, and Fog Noise. Contrast enhancement between subjects and the background was achieved using Contrast-Limited Adaptive Histogram Equalization (CLAHE). A U-Net model was utilized for image segmentation, followed by a watershed algorithm to extract Regions of Interest (ROI) from the segmented images. Finally, a Convolutional Neural Network (CNN) was developed for classification through automatic feature learning in the spatial domain. The U-Net model, optimized using the Adam optimizer, demonstrated excellent performance with 96.47% accuracy, 97.85% precision, and 98.05% sensitivity at the pixel level. At the object level, it achieved 96% Intersection over Union (IoU) and a 94% Dice Coefficient. The CNN classifier achieved 97.38% accuracy, with macro average F1 scores of 97.67%. This study presents an innovative AI-based approach for diagnosing intestinal parasitic infections. By integrating advanced image filtering, segmentation, and classification techniques, the proposed method shows promise in improving diagnostic efficiency and reducing reliance on manual processes.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100432"},"PeriodicalIF":0.0,"publicationDate":"2025-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145519213","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}

{"title":"An analytical framework for enhancing brain signal classification through hybrid filtering and dimensionality reduction","authors":"Rajani Rai B ,&nbsp;Karunakara Rai B ,&nbsp;Mamatha A S ,&nbsp;Nikshitha","doi":"10.1016/j.health.2025.100435","DOIUrl":"10.1016/j.health.2025.100435","url":null,"abstract":"<div><div>Accurate classification of focal and non-focal epilepsy is a critical healthcare analytics challenge that requires robust data preprocessing and feature optimization. This work develops an integrated analytics framework that combines hybrid filtering with hybrid dimensionality reduction to improve both signal quality and predictive performance. A multi-criteria ranking strategy based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is employed, incorporating conventional signal measures alongside distance and divergence metrics to identify optimal preprocessing pipelines. Statistical validation is performed using the Friedman test with Nemenyi post-hoc analysis to establish the significance of competing filter–dimensionality reduction combinations. The validated framework is benchmarked across conventional, hybrid, and deep learning classifiers, with the most effective configuration—Butterworth.</div><div>Wavelet Packet Decomposition (BW + WPD) filtering followed by Principal Component Analysis–Linear Discriminant Analysis (PCA + LDA)—achieving 95.63% accuracy using an Adaboost classifier on the Bern–Barcelona dataset. Evaluation on the independent Bonn dataset confirms robustness and cross-subject generalizability. These findings demonstrate the value of a multi-metric, statistically validated analytics strategy for reliable epilepsy detection, with potential applicability to broader healthcare signal classification tasks.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100435"},"PeriodicalIF":0.0,"publicationDate":"2025-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145578578","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}

{"title":"An analytical review of blood supply chain management literature through science mapping and strategic diagrams","authors":"Nurhadi Siswanto ,&nbsp;Ivan Darma Wangsa ,&nbsp;Ahmed Raecky Baihaqy ,&nbsp;Patdono Suwignjo ,&nbsp;Vincent F. Yu","doi":"10.1016/j.health.2025.100433","DOIUrl":"10.1016/j.health.2025.100433","url":null,"abstract":"<div><div>The increasing complexity of healthcare systems and the critical role of blood supply chain (BSC) management in ensuring patient safety have motivated the need for a systematic synthesis of research in this domain. This study reviews the literature and presents a bibliometric and systematic literature review of BSC management studies. This study aimed to investigate the evolution of BSC management over twelve years (2014–2025). One hundred ninety-four published articles were retrieved from the Scopus database based on the inclusion criteria. Using bibliometric techniques, descriptive analysis was conducted to examine publication trends, citations, leading journals, influential authors, and contributing countries. Science mapping and strategic diagram analysis were employed to identify and visualize keyword networks, enabling the recognition of thematic clusters and their evolution. The results highlight five dominant research streams: donor engagement, demand forecasting, inventory and logistics optimization, resilience to disruptions, and the application of digital technologies such as artificial intelligence, machine learning, and blockchain. The analysis also reveals emerging sustainability and circular economy themes that remain underexplored, pointing to significant research gaps. This study contributes to theory by providing a structured knowledge map of BSC research and advancing understanding of its evolution. It offers practical insights for policymakers, blood banks, and healthcare managers to enhance the resilience, sustainability, and efficiency of BSC operations.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100433"},"PeriodicalIF":0.0,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145519214","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}