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

{"title":"An analytics framework for secure and intelligent telemedicine in sustainable healthcare","authors":"Luis Vives ,&nbsp;Abraham Dávila ,&nbsp;Iván Cabezas ,&nbsp;Antonio Sánchez ,&nbsp;Víctor Gómez ,&nbsp;Jorge Eduardo Luján López","doi":"10.1016/j.health.2026.100472","DOIUrl":"10.1016/j.health.2026.100472","url":null,"abstract":"<div><div>Telemedicine has significantly transformed healthcare delivery by expanding access to medical services and enabling more personalized care. Despite these advances, healthcare organizations continue to face challenges related to digital literacy, uneven access to technology, data privacy, platform interoperability, and the integration of advanced digital solutions into clinical and operational workflows. This paper proposes an analytics-driven telemedicine framework that emphasizes secure data governance, interoperability, and decision support in healthcare organizations. The framework integrates permissioned blockchain services to provide immutable logging, identity and access management, and end-to-end traceability of clinical and pharmaceutical activities, while federated learning supports decentralized model training without centralizing sensitive patient data. In addition, large language models are incorporated to enhance clinical text understanding and enable multimodal decision making. The framework is evaluated through controlled experiments across two complementary dimensions. An operational simulation of healthcare service indicators demonstrates meaningful performance gains under controlled conditions, reducing consultation response times from 24 to 72 h to 1 to 4 h, shortening diagnostic delays from 3 to 7 days to 2 to 12 h, and decreasing medication traceability errors from 2 to 8 percent annually to below 0.5 percent. A multimodal analytics assessment using 15,000 paired medical images and clinical reports related to breast, liver, and lung cancer shows strong predictive performance, with the best configuration achieving 93.5% accuracy, 94.5% precision, and 91.5% recall, along with low cross entropy values that indicate reliable classification and coherent report generation. Overall, the results indicate that the integration of advanced analytics with secure and auditable data infrastructure can improve efficiency, data integrity, and clinical decision support in telemedicine systems. The framework is particularly relevant for healthcare environments where institutional fragmentation and infrastructure limitations require scalable, privacy-preserving, and interoperable digital solutions.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"9 ","pages":"Article 100472"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148183850","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 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":"2026-06-01","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}

{"title":"An intelligent machine learning approach for predicting and explaining brain injury severity","authors":"Hoang Bach Nguyen ,&nbsp;Quang Tung Pham ,&nbsp;Sinh Huy Nguyen ,&nbsp;Chi Thanh Nguyen ,&nbsp;Thanh Hai Tran ,&nbsp;Hai Vu","doi":"10.1016/j.health.2025.100445","DOIUrl":"10.1016/j.health.2025.100445","url":null,"abstract":"<div><div>Traumatic brain injury (TBI) requires timely and reliable severity assessment to support critical clinical decision-making. This study proposes an interpretable machine learning framework for TBI severity prediction using two datasets: the public HPTBI dataset and a newly developed 103_TBI dataset comprising 504 patients. After data preprocessing and feature selection, ensemble learning models-particularly Random Forest and XGBoost-achieved accuracies exceeding 94%. To enhance transparency and clinical trust, we introduce a dual-layer interpretability strategy that integrates post-hoc explanation techniques (SHAP, LIME, PFI, PDP, and counterfactual analysis) with a knowledge-graph-based evaluation of feature interactions. The attribution methods show high agreement (<span><math><mrow><mi>c</mi><mi>o</mi><mi>r</mi><mi>r</mi><mi>e</mi><mi>l</mi><mi>a</mi><mi>t</mi><mi>i</mi><mi>o</mi><mi>n</mi><mo>&gt;</mo><mn>0</mn><mo>.</mo><mn>91</mn></mrow></math></span>) and consistently identify key clinical predictors such as the Glasgow Coma Scale (GCS), midline shift, and pulse rate. These insights align closely with expert judgment, supporting the clinical credibility of the model explanations. Additionally, the knowledge graph reveals multivariate relationships critical to outcome determination. By integrating predictive models with clinical interpretability techniques, the proposed framework offers reliable clinical support to assist neurotrauma triage and expert validation. This work therefore demonstrates the potential of integrating explainable AI with domain knowledge to advance TBI severity prediction.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"9 ","pages":"Article 100445"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926223","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 framework for graph-based anomaly detection in healthcare time series","authors":"Emerson Yoshiaki Okano ,&nbsp;Daniel Aloise ,&nbsp;Mariá C.V. Nascimento","doi":"10.1016/j.health.2026.100447","DOIUrl":"10.1016/j.health.2026.100447","url":null,"abstract":"<div><div>Anomaly detection in time series plays a vital role in diverse domains such as healthcare, finance, and industrial monitoring, where identifying deviations from normal behavior can signal critical events. While traditional methods often focus on univariate time series and assume fixed temporal dynamics, real-world systems are typically multivariate and characterized by complex interdependencies. Ignoring these relationships can lead to suboptimal detection of system-level anomalies. This paper proposes a novel graph-based framework for multivariate time series anomaly detection that explicitly captures temporal patterns and structural relationships among variables. Individual univariate time series are first transformed into Horizontal Visibility Graphs (HVGs), which are then combined into multiplex networks to preserve inter-layer interactions. Additionally, we construct feature-based similarity graphs derived from statistical properties of the series to model inter-series relations. Anomalies are identified by comparing the neighborhood structure of each series against a historical reference set, enabling the detection of subtle and coordinated deviations. Computational experiments on real-world healthcare data illustrate the behavior and practical relevance of the proposed approach in capturing complex anomalies, offering a robust and interpretable alternative to traditional techniques.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"9 ","pages":"Article 100447"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023162","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 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":"2026-06-01","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 data-integrated systems engineering method for proactive inpatient fall prevention","authors":"Khaled Toffaha ,&nbsp;Mecit Can Emre Simsekler ,&nbsp;Michael A. Kortt ,&nbsp;Andrei Sleptchenko ,&nbsp;Jeffrey Chapman","doi":"10.1016/j.health.2026.100470","DOIUrl":"10.1016/j.health.2026.100470","url":null,"abstract":"<div><div>Inpatient falls remain a persistent patient safety problem, increasing morbidity, mortality, and costs. This study integrates machine learning (ML), Bayesian Belief Networks (BBNs), and Chain Event Graphs (CEGs) with the Systems Engineering Initiative for Patient Safety (SEIPS) to enhance fall-risk prediction and support prevention. A retrospective cohort of 6661 inpatient admissions from an academic hospital was analyzed, with 1.8% experiencing at least one fall. In line with recent guideline recommendations that discourage the use of stand-alone fall risk scores for screening, this work uses routinely collected electronic health record (EHR) data to support locally defined multifactorial fall prevention rather than proposing a new point-of-care score. The selected SEIPS-based Autoencoder, evaluated under nested stratified cross-validation, showed more balanced performance than purely data-driven models and achieved a recall of 0.74 for fall events. Feature analysis identified clinically plausible contributors spanning patients, tasks, tools, and technology, organizational conditions, and physical environment. BBNs captured probabilistic dependencies among these predictors, and CEGs summarized high-risk pathways involving combinations of impaired mobility, lower cognitive status, intermediate neurological responsiveness, and higher potassium values. Uplift modeling suggested that patients who received fall education had an estimated 7.5% lower probability of falling, under standard causal assumptions for observational data. Cost analyses using published per-fall estimates indicated that, if similar effects were achieved in practice, implementation could meaningfully reduce fall-related expenditures for hospitals adopting systems-oriented, data-driven fall prevention programs.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"9 ","pages":"Article 100470"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148183852","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 framework for inverse cascade classification in cancer risk assessment","authors":"Markian Jaworsky ,&nbsp;Xiaohui Tao ,&nbsp;Thanveer Shaik ,&nbsp;Shiva Raj Pokhrel","doi":"10.1016/j.health.2026.100471","DOIUrl":"10.1016/j.health.2026.100471","url":null,"abstract":"<div><div>Understanding chronic disease risk factors helps people make informed lifestyle choices and live longer; however, patients often struggle to stay healthy because health information can be overwhelming, and diseases have multiple causes. We map survey-derived predictors to specific organ systems using the WHO International Classification of Diseases (ICD) to pinpoint cancer subtypes and find a third-dimensional relationship with mental health. Using our framework, we identify high-impact risk factors, highlight preventive measures, and estimate the likelihood of age-related cancer to guide lifestyle interventions. Using Knowledge Graph-driven feature selection, we also introduce an inverse cascading deep learning (DL) multilabel classifier, a feedforward neural network (NN), for benign majority class filtering and machine learning (ML) classifiers trained solely on minority classes, with minority class-suited hyperparameters and cross-validation. This framework correlates risk factors with the ICD chapters, revealing the interconnected relationship among chronic illness, risk factors, and mental health.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"9 ","pages":"Article 100471"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148183851","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 decision-theoretic method for analyzing crossing survival curves in healthcare","authors":"Elie Appelbaum ,&nbsp;Moshe Leshno ,&nbsp;Eitan Prisman ,&nbsp;Eliezer Z. Prisman","doi":"10.1016/j.health.2025.100405","DOIUrl":"10.1016/j.health.2025.100405","url":null,"abstract":"<div><div>The problem of crossing Kaplan–Meier curves has not been solved in the medical research literature to date. This paper integrates survival curve comparisons into decision theory, providing a theoretical framework and a solution to the problem of crossing Kaplan–Meier curves. The application of decision theory allows us to apply stochastic dominance concepts and risk preference attributes to compare treatments even when standard Kaplan–Meier curves cross. The paper shows that as additional risk preference attributes are adopted, Kaplan–Meier curves can be ranked under weaker restrictions, namely with higher orders of stochastic dominance. Consequently, even Kaplan–Meier curves that cross may be ranked. The method we present allows us to extract all possible information from survival functions; hence, superior treatments that cannot be identified using standard Kaplan–Meier curves may become identifiable. Our methodology is applied to two examples of published empirical medical studies. We show that treatments deemed non-comparable because their Kaplan–Meier curves intersect can be compared using our method.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100405"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144685878","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 approach to modeling conjunctival viral disease using fuzzy logic and time-delay dynamics","authors":"Muhammad Tashfeen ,&nbsp;Hothefa Shaker Jassim ,&nbsp;Fazal Dayan ,&nbsp;Muhammad Azizur Rehman ,&nbsp;Alwahab Dhulfiqar Zoltán ,&nbsp;Husam A. Neamah","doi":"10.1016/j.health.2025.100404","DOIUrl":"10.1016/j.health.2025.100404","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100404"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144298568","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 improving healthcare data management and organizational performance","authors":"Yeneneh Tamirat Negash ,&nbsp;Faradilah Hanum","doi":"10.1016/j.health.2025.100415","DOIUrl":"10.1016/j.health.2025.100415","url":null,"abstract":"<div><div>Digital healthcare relies on accurate, connected data to deliver safe and efficient patient care. Yet, fragmented management systems create data silos, limit interoperability, and delay clinical and administrative decisions. These conditions impede the promise of personalized, coordinated, and efficient care. Smart Product Service Systems (Smart PSS) integrate intelligent products, digital platforms, and value-added services, thereby providing a pathway to enhanced data management and improved patient care. Prior studies seldom identify or link the specific Smart PSS attributes that shape healthcare data management and organizational performance, particularly from a causal perspective. This study fills that gap by developing an analytical framework for improving healthcare data management and organizational performance. A literature review produced 47 candidate attributes. Thirty-three healthcare experts validated 27 attributes through the Fuzzy Delphi Method. Fuzzy Decision-Making Trial and Evaluation Laboratory then mapped the causal structure among the validated attributes and their associated aspects. Intelligent products, stakeholder collaboration, and service realization emerged as core causal aspects that influence data management and organizational performance. Smart repair, monitoring and early warning, synchronized transactions, information integration, data quality, and organizational readiness ranked as the most influential criteria for practice. By prioritizing these criteria, healthcare managers reduce data fragmentation and improve service outcomes. The study provides a hierarchical Smart PSS framework and managerial guidance for institutions advancing digital healthcare.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100415"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145010070","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}