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

{"title":"A scalable methodology for optimizing hospital surgical schedules considering efficiency, flexibility, and improved patient outcomes","authors":"Jiaqi Suo ,&nbsp;Claudio Martani ,&nbsp;Timothy B. Lescun ,&nbsp;Cherri A. Krug","doi":"10.1016/j.health.2025.100413","DOIUrl":"10.1016/j.health.2025.100413","url":null,"abstract":"<div><div>Hospitals face challenges in efficiently adapting treatment delivery to growing and changing demands. The main challenge arises from accommodating diverse patients requiring specific surgical resources and attention. Traditional scheduling methods often fail to address the dynamic nature of these environments, which are characterized by numerous uncertainties and stakeholders’ complex and changing needs. This study presents a novel methodology designed to enhance hospital operational efficiency while considering the interests of all stakeholders, including hospital administrators, medical staff (doctors, nurses, technicians), and patients. This requires a nuanced approach to effectively handle unpredictable treatment demands, resource availability, and patient requirements. The methodology systematically progresses from defining constraints and resources to modeling uncertainties generating and evaluating optimal schedules through iterative processes. This study develops and applies a 12-step method to optimize the surgery scheduling for the farm animal section of the Purdue Veterinary Hospital over a defined period. The application shows the practical benefits of the proposed approach by modeling dynamic surgical demands and exploring various scheduling possibilities within resource constraints. The results reveal that the proposed method effectively accommodates increased operational demands while managing delays, accidents, and illness costs.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100413"},"PeriodicalIF":0.0,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145047823","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":"2025-09-01","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":"A deep learning framework for automated breast cancer diagnosis using intelligent segmentation and classification","authors":"Ahed Abugabah","doi":"10.1016/j.health.2025.100414","DOIUrl":"10.1016/j.health.2025.100414","url":null,"abstract":"<div><div>Breast cancer is the most commonly diagnosed cancer among women worldwide, accounting for a significant proportion of new cases. Deep learning (DL) has emerged as a powerful tool for the detection and diagnosis of breast cancer, particularly through the analysis of histological images, a critical component of automated diagnostic systems that directly impact patient management. The BreakHis dataset and the Wisconsin Breast Cancer Database (WBCD) are widely used publicly available resources for deep learning–based analyses of breast cancer histological images in cross-disciplinary healthcare research. A computer-assisted approach employs colour normalisation to reduce the effects of the differences in the distribution of breast histopathology images. In this paper, breast tumour areas of interest are segmented utilising Attention-Guided Deep Atrous-Residual U-Net at the segmentation stage. Subsequently, patches are processed to form feature vectors VGG19 and ResNet50 for the extraction of deep features from the patches. Also, to fine-tune these models even further, the breast cancer datasets are employed, and Levy Flight-based Red Fox Optimisation is used to extract features from the pre-trained models without further training. The Efficient Capsule Network is used to improve the feature representation and classification capabilities. AGDATUNet-LFRFO-ECN, which was suggested in the study, performed better than other models when tested on the WBCD dataset, with a sensitivity of 99.17 %, specificity of 99.08 %, and accuracy of 99.23 %. What's more, the AGDATUNet-LFRFO-ECN outperformed the available models on BreakHis with a sensitivity of 99.81 %, a specificity of 99.79 %, and an accuracy of 99.82 %, which are the state-of-the-art.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100414"},"PeriodicalIF":0.0,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145010072","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 comparative analysis of generalized additive models for obesity risk prediction","authors":"Olushina Olawale Awe ,&nbsp;Olawale Abiodun Olaniyan ,&nbsp;Ayorinde Emmanuel Olatunde ,&nbsp;Ronel SewPaul ,&nbsp;Natisha Dukhi","doi":"10.1016/j.health.2025.100410","DOIUrl":"10.1016/j.health.2025.100410","url":null,"abstract":"<div><div>Obesity is a growing global health crisis, and traditional regression models often fail to capture the complex relationships between risk factors, limiting predictive accuracy and hindering effective public health interventions. Conventional methods overlook non-linear associations and interaction effects across demographic, socioeconomic, and behavioral predictors, which are particularly important in diverse populations with varying obesity determinants. To address these limitations, we applied Generalized Additive Models for Location, Scale, and Shape (GAMLSS) to analyze obesity predictors in a nationally representative adolescent sample (N <span><math><mo>=</mo></math></span> 671). Our framework included comprehensive variable selection across demographic, socioeconomic, behavioral, and clinical domains, comparison with three alternative regression models, and validation using the Generalized Akaike Information Criterion (GAIC). The binomial stepwise GAMLSS model demonstrated superior performance (GAIC <span><math><mo>=</mo></math></span> 624.98). Key findings included strong geographic variation, significant gender disparity, a socioeconomic gradient, and important behavioral predictors such as weight gain attempts. The GAMLSS framework improves obesity risk prediction by modeling complex relationships often missed by traditional methods, offering targeted intervention strategies based on geographic, gender, and socioeconomic factors, and challenging assumptions about dietary influences.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100410"},"PeriodicalIF":0.0,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908269","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 comprehensive diagnostic framework for hepatitis C using structured data and predictive analytics","authors":"Behnaz Motamedi,&nbsp;Balázs Villányi","doi":"10.1016/j.health.2025.100412","DOIUrl":"10.1016/j.health.2025.100412","url":null,"abstract":"<div><div>This study posits that a structured preprocessing and feature selection methodology might substantially improve the classification accuracy and generalizability of machine learning (ML) models in predicting stages of hepatitis C virus (HCV) using clinical and demographic data. The HCV is a chronic liver ailment characterized by many phases, necessitating precise and prompt categorization for optimal therapy. Although ML presents opportunities for stage prediction, issues such as class imbalance, missing data, and feature redundancy limit model efficacy and generalizability. To test this theory, we established an extensive four-phase preparation pipeline: Baseline imputes missing values using class-specific means; Refine mitigates outliers through class-specific medians and normalization; Balanced addresses class imbalance across five stages employing localized random affine shadow-sampling; and Augmented incorporates a clustering-based feature derived from an ensemble of K-means and Gaussian mixture models, combined with principal component analysis. The prediction model was developed by optimizing feature selection with the ReliefF approach and a random forest classifier employing random search. The resultant model exhibited outstanding performance, attaining an accuracy of 0.9983, precision of 0.9984, recall of 0.9983, F1-score of 0.9984, and Matthews correlation coefficient (MCC) of 0.9979 on the training set. It achieved an accuracy of 0.9977, precision of 0.9976, recall of 0.9981, F1-score of 0.9978, and MCC of 0.9973 on the independent test. The ensemble clustering component demonstrated reasonable validity, shown by an adjusted Rand index of 1.0, a moderate silhouette coefficient of 0.4702, and a Davies–Bouldin score of 1.1745, modestly outperforming individual clustering methods. The findings support the hypothesis and demonstrate that thorough preprocessing, stringent feature selection, and model optimization provide a highly accurate and generalizable tool for predicting HCV stages, hence improving clinical diagnosis and treatment strategies.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100412"},"PeriodicalIF":0.0,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144879452","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 machine learning framework for predicting healthcare utilization and risk factors","authors":"Yead Rahman ,&nbsp;Prerna Dua","doi":"10.1016/j.health.2025.100411","DOIUrl":"10.1016/j.health.2025.100411","url":null,"abstract":"<div><div>Medicaid data, with its vast scale and heterogeneity, presents significant challenges in predictive modeling and healthcare analytics. This study analyzes over 6.3 million records from the Louisiana Department of Health (LDH) to identify the most effective machine learning models for predicting clinical service utilization, COVID-19 infections, and tobacco use. A rigorous preprocessing pipeline ensured data integrity, while exploratory data analysis (EDA) guided feature selection, ultimately retaining 20 key variables to capture complex interactions. Seven supervised models, i.e., logistic regression, extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), random forest, decision tree, artificial neural networks (ANN), and naïve bayes, were evaluated based on predictive performance, computational efficiency, and feature importance. While ensemble methods such as XGBoost and random forest achieved superior accuracy, their high computational demands highlight the trade-off between performance and efficiency in large-scale healthcare analytics. Simpler models like naïve bayes and decision trees were computationally efficient but less accurate. Key predictors included hospital stay duration for healthcare service utilization, tobacco use for COVID-19 risk, and chronic obstructive pulmonary disease (COPD) for tobacco use. These findings emphasize the impact of comorbidities and demographics on healthcare utilization, offering data-driven insights for healthcare practitioners and policymakers to enhance patient care, optimize costs, and refine policy decisions.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100411"},"PeriodicalIF":0.0,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144885885","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-driven model for identifying autism spectrum disorder using eye tracking","authors":"Deblina Mazumder Setu","doi":"10.1016/j.health.2025.100409","DOIUrl":"10.1016/j.health.2025.100409","url":null,"abstract":"<div><div>The efficient and early detection of Autism Spectrum Disorder (ASD) is a critical objective in improving diagnosis and intervention outcomes. Various methods based on functional Magnetic Resonance Imaging (fMRI) and questionnaires have been explored, among which eye tracking is a promising approach. However, existing methods relying on eye tracking often restrict us to controlled environments, making things complicated and expensive. This study eliminates the requirement for specific parameters by concentrating just on eye movement data for ASD detection, therefore introducing a novel and user-friendly technique. Feature engineering is employed, encompassing preprocessing and extracting relevant gaze movement data. These properties are utilized in machine learning and deep learning model training with hyperparameter adjusting for optimization. Using the Saliency4ASD dataset and looking beyond its usual gaze focus, this study built a model that uses eye movement alone to identify ASD with about 81% accuracy. This safe, low-cost approach has the potential to provide simple technologies that enable early detection of ASD, hence allowing its accessibility to everyone.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100409"},"PeriodicalIF":0.0,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144827735","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 interpretable deep learning framework for medical diagnosis using spectrogram analysis","authors":"Shagufta Henna ,&nbsp;Juan Miguel Lopez Alcaraz ,&nbsp;Upaka Rathnayake ,&nbsp;Mohamed Amjath","doi":"10.1016/j.health.2025.100408","DOIUrl":"10.1016/j.health.2025.100408","url":null,"abstract":"<div><div>Convolutional Neural Networks (CNNs) are widely utilized for their robust feature extraction capabilities, particularly in medical classification tasks. However, their opaque decision-making process presents challenges in clinical settings, where interpretability and trust are paramount. This study investigates the explainability of a custom CNN model developed for Covid-19 and non-Covid-19 classification using dry cough spectrograms, with a focus on interpreting filter-level representations and decision pathways. To improve model transparency, we apply a suite of explainable artificial intelligence (XAI) techniques, including feature visualizations, SmoothGrad, Grad-CAM, and LIME, which explain the relevance of spectro-temporal features in the classification process. Furthermore, we conduct a comparative analysis with a pre-trained MobileNetV2 model using Guided Grad-CAM and Integrated Gradients. The results indicate that while MobileNetV2 yields some degree of visual attribution, its explanations, particularly for Covid-19 predictions are diffuse and inconsistent, limiting their interpretability. In contrast, the custom CNN model exhibits more coherent and class-specific activation patterns, offering improved localization of diagnostically relevant features.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100408"},"PeriodicalIF":0.0,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144766925","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":"2025-07-17","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 integrated machine learning and fractional calculus approach to predicting diabetes risk in women","authors":"David Amilo ,&nbsp;Khadijeh Sadri ,&nbsp;Evren Hincal ,&nbsp;Muhammad Farman ,&nbsp;Kottakkaran Sooppy Nisar ,&nbsp;Mohamed Hafez","doi":"10.1016/j.health.2025.100402","DOIUrl":"10.1016/j.health.2025.100402","url":null,"abstract":"<div><div>This study presents a novel dual approach for diabetes risk prediction in women, combining machine learning classification with fractional-order physiological modeling. We employ seven machine learning algorithms: Decision Tree, Logistic Regression, Support Vector Machine (SVM), Random Forest, Bagged Trees, Naive Bayes, and XGBoost, to identify key risk factors, with XGBoost demonstrating higher performance. Glucose levels, BMI, blood pressure, and Diabetes Pedigree Function emerged as the most significant predictors across all models. Complementing these data-driven insights, we develop a Caputo fractional-order model that captures the temporal dynamics of glucose-insulin regulation, BMI, and blood pressure. Through fixed-point theorem analysis, we prove the existence and uniqueness of solutions, while numerical implementations using Lagrange polynomial interpolation reveal how varying fractional orders affect metabolic response patterns. This mathematical framework provides unique insights into the progression of diabetes, particularly through its ability to model memory effects and long-term physiological changes. The practical implementation of our research features an intuitive graphical user interface (GUI) that integrates both approaches, enabling real-time risk assessment with dynamic feedback. Our analysis of the Pima Indians dataset confirms important physiological relationships, including age-pregnancy and BMI-skin thickness correlations. This dual-method framework offers clinicians a comprehensive tool for diabetes management, combining the immediate predictive power of machine learning with the longitudinal perspective of fractional-order modeling. The machine learning component provides accurate short-term risk stratification, while the fractional-order model enhances understanding of long-term disease progression. Together, they enable more personalized and proactive care strategies, advancing both the theory and practice of diabetes risk assessment.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100402"},"PeriodicalIF":0.0,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144634355","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}