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

{"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 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}

{"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":"2025-12-01","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":"A constrained optimization approach for ultrasound shear wave speed estimation with time-lateral plane cleaning in medical imaging","authors":"MD Jahin Alam,&nbsp;Md. Kamrul Hasan","doi":"10.1016/j.health.2025.100423","DOIUrl":"10.1016/j.health.2025.100423","url":null,"abstract":"<div><div>Ultrasound shear wave elastography (SWE) is a noninvasive tissue stiffness measurement technique for medical diagnosis. In SWE, an acoustic radiation force creates shear waves (SW) throughout a medium where the shear wave speed (SWS) is related to the medium stiffness. Traditional SWS estimation techniques are not noise-resilient in handling jitter and reflection artifacts. This paper proposes new techniques to estimate SWS in both time and frequency domains. These new methods utilize loss functions which are: (1) optimized by lateral signal shift between known locations, and (2) constrained by neighborhood displacement group shift determined from the time-lateral plane-denoised SW propagation. The proposed constrained optimization is formed by coupling neighboring particles’ losses with a Gaussian kernel, giving an optimum arrival time for the center particle to enforce local stiffness homogeneity and enable noise resilience. The explicit denoising scheme involves isolating SW profiles from time-lateral planes, creating parameterized masks. Additionally, lateral interpolation is performed to enhance reconstruction resolution and thereby improve the reliability of optimization. The proposed scheme is evaluated on a simulation (US-SWS-Digital-Phantoms) and three experimental phantom datasets: (i) Mayo Clinic CIRS049 model, (ii) RSNA-QIBA-US-SWS, (iii) Private data. The constrained optimization performance is compared with three time-of-flight (ToF) and two frequency-domain methods. The evaluations produced visually and quantitatively superior and noise-robust reconstructions compared to classical methods. Due to the quality and minimal error of SWS map formation, the proposed technique can find its application in tissue health inspection and cancer diagnosis.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100423"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219004","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 explainable analytical approach to heart attack detection using biomarkers and nature-inspired algorithms","authors":"Maithri Bairy ,&nbsp;Krishnaraj Chadaga ,&nbsp;Niranjana Sampathila ,&nbsp;R. Vijaya Arjunan ,&nbsp;G. Muralidhar Bairy","doi":"10.1016/j.health.2025.100407","DOIUrl":"10.1016/j.health.2025.100407","url":null,"abstract":"<div><div>Heart attacks are among the leading causes of death globally, and the earliest possible identification of at-risk patients is critical to lowering deaths. Advanced machine learning and deep learning algorithms have been effectively used to predict the presence of heart attack based on clinical and laboratory markers. This study used five explainable artificial intelligence techniques (XAI) to ensure that predictions made by the model are understandable and interpretable to facilitate clinical decisions. Fourteen nature-inspired feature selection algorithms were applied to identify the most informative markers while optimizing the predictive models for greater accuracy and reliability. Mutual information achieved a maximum testing accuracy of 90 % and highest precision of 94 %. The Whale Optimization Algorithm, Jaya Algorithm, Grey Wolf Optimizer and Sine Cosine Algorithm were the next best performing algorithms. The XAI results showed that the most important markers were ST slope, Oldpeak, exercise-induced angina, chest pain type, and fasting blood sugar. These models can be implemented in healthcare institutions to predict heart attack risks early, allowing timely interventions to reduce the likelihood of severe cardiovascular diseases. By supporting healthcare professionals with computer-aided diagnostic tools, these systems can enhance patient-specific decision-making while alleviating strain on healthcare resources.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100407"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144665926","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 bio-inspired approach to feature optimization for ischemic heart disease detection","authors":"D. Cenitta ,&nbsp;N. Arul ,&nbsp;T. Praveen Pai ,&nbsp;R. Vijaya Arjunan ,&nbsp;Tanuja Shailesh","doi":"10.1016/j.health.2025.100427","DOIUrl":"10.1016/j.health.2025.100427","url":null,"abstract":"<div><div>Ischemic Heart Disease (IHD) stands as one of the primary contributors to worldwide deaths, therefore requiring precise and efficient predictive models. Standard machine learning techniques encounter hurdles, including excessive feature dimensions and unbalanced data distribution together with inappropriate feature group choice that negatively affect model effectiveness. The research introduces an optimized feature selection method by employing an Improved Squirrel Search Algorithm (ISSA) to raise the predictive capacity for IHD classification. The ISSA implements adaptive search features to automatically optimize feature selection, through which it maintains important attributes while eliminating redundant information. The selected features are evaluated using a Random Forest classifier, known for its robustness and interpretability in medical prediction tasks. Experimental results on the University of California Irvine (UCI) Heart Disease dataset show that the Improved Squirrel Search Algorithm–Random Forest (ISSA-RF) model achieves a classification accuracy of 98.12 %, outperforming existing feature selection techniques while reducing computational overhead. Bio-inspired optimization proves effective in medical diagnostics through recent research findings that lead to more efficient predictive healthcare models with interpretable properties.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100427"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145415990","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}