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

{"title":"A log-linear analytics approach to cost model regularization for inpatient stays through diagnostic code merging","authors":"Chi-Ken Lu,&nbsp;David Alonge,&nbsp;Nicole Richardson,&nbsp;Bruno Richard","doi":"10.1016/j.health.2025.100431","DOIUrl":"10.1016/j.health.2025.100431","url":null,"abstract":"<div><div>Healthcare cost models that use a great number of detailed ICD-10 diagnostic codes produce unstable results, yet the underlying causes of this instability have not been well understood. This study provides a mathematical framework linking the variability of model coefficients to the uneven, power-law distribution of diagnostic codes and the structure of the regression model. We propose a transparent approach that improves coefficient stability by merging similar codes through hierarchical truncation. Using Medicare data, we demonstrate how this method clarifies the trade-off between code detail and model reliability, offering analysts and policymakers a practical and interpretable tool for diagnosis-based cost modeling.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100431"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145464881","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 3D brain tumor segmentation and survival prediction","authors":"Ashfak Yeafi,&nbsp;Monira Islam,&nbsp;Md. Salah Uddin Yusuf","doi":"10.1016/j.health.2025.100418","DOIUrl":"10.1016/j.health.2025.100418","url":null,"abstract":"<div><div>Accurate and efficient segmentation of brain tumors is crucial for early diagnosis, personalized treatment planning, and improved survival rates. Brain tumors exhibit complex spatial and morphological variations, making automated segmentation a challenging task. This study introduces a dynamic segmentation network (DSNet), a novel 3D brain tumor segmentation framework that integrates adversarial learning, dynamic convolutional neural network (DCNN), and attention mechanisms to enhance precision and robustness. DSNet processes 3D magnetic resonance imaging (MRI) volumes, including T1-weighted (T1), T1-weighted with contrast enhancement (T1ce), T2-weighted (T2), and fluid-attenuated inversion recovery (FLAIR) modalities, capturing rich spatial and contextual features. Leveraging adversarial training, DSNet refines boundary definitions, while dynamic filters adapt to tumor-specific heterogeneities, ensuring accurate segmentation across diverse cases. The attention mechanism further emphasizes tumor-relevant regions, enhancing feature extraction and boundary delineation. The model was trained and validated on the BraTS 2020 dataset, achieving dice similarity coefficients of 0.959, 0.975, and 0.947 for whole tumors (WT), tumor cores (TC), and enhancing tumor (ET) regions, respectively. Its generalizability was further confirmed through evaluations on the BraTS 2019 and BraTS 2018 datasets. Additionally, volumetric features derived from segmented images were used to predict patients’ overall survival rates via a Random Forest (RF) classifier. To enhance accessibility, we integrated the segmentation and prediction processes into a user-friendly web application. DSNet outperforms state-of-the-art methods, providing a robust and accurate solution for 3D brain tumor segmentation with strong clinical potential.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100418"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095141","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":"2025-12-01","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}

{"title":"A focal loss and sequential analytics approach for liver disease classification and detection","authors":"Musa Mustapha ,&nbsp;Oluwadamilare Harazeem Abdulganiyu ,&nbsp;Isah Ndakara Abubakar ,&nbsp;Kaloma Usman Majikumna ,&nbsp;Garba Suleiman ,&nbsp;Mehdi Ech-chariy ,&nbsp;Mekila Mbayam Olivier","doi":"10.1016/j.health.2025.100424","DOIUrl":"10.1016/j.health.2025.100424","url":null,"abstract":"<div><div>Liver disease poses a significant global health challenge requiring accurate and timely diagnosis. This research develops a novel deep learning model, named AFLID-Liver, to improve the classification of liver diseases from medical data. The AFLID-Liver model integrates three key techniques: an Attention Mechanism to focus on the most relevant data features, Long Short-Term Memory (LSTM) networks to process potential sequential information, and Focal Loss to effectively handle imbalances between different disease classes in the dataset. This combination enhances the model's ability to learn complex patterns and make robust predictions. We evaluated AFLID-Liver using a dataset of various patient records, including biomarkers and demographics. Our proposed model achieved superior performance, with 99.9 % accuracy, 99.9 % precision, and a 99.9 % F-score, significantly outperforming a baseline Gated Recurrent Unit (GRU) model (99.7 % accuracy, 97.9 % F-score) and existing state-of-the-art approaches. These results demonstrate AFLID-Liver's potential for highly accurate liver disease detection. To validate the generalizability of the proposed model, we performed cross validation using an external dataset which also yielded a good performance depicting the potential of the proposed model. The novelty lies in the synergistic integration of these techniques, offering a robust approach for clinical decision support and improved patient outcomes. Future research will aim to enhance the computational efficiency, paving the way for its adoption in real-time clinical applications.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100424"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145264752","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":"2025-12-01","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}

{"title":"An integrated deep learning approach for enhancing brain tumor diagnosis","authors":"Rabeya Bashri Sumona ,&nbsp;John Pritom Biswas ,&nbsp;Ahmed Shafkat ,&nbsp;Md Mahbubur Rahman ,&nbsp;Md Omor Faruk ,&nbsp;Yaqoob Majeed","doi":"10.1016/j.health.2025.100421","DOIUrl":"10.1016/j.health.2025.100421","url":null,"abstract":"<div><div>The diagnosis of a brain tumor poses a significant challenge due to the varied manifestations of tumors and their impact on patient health. Traditional Magnetic Resonance Imaging (MRI) based methods are time-consuming, expensive, and highly reliant on radiologists’ expertise. Automated and reliable classification techniques are crucial to enhancing diagnostic accuracy, improving patient outcomes, and ensuring timely detection. This study introduces RDXNet, a hybrid deep learning model that integrates ResNet50, DenseNet121, and Xception to improve the classification of multiclass brain tumors. We utilized three publicly available datasets which are Br35H :: Brain Tumor Detection 2020, Figshare Brain Tumor Dataset, and Radiopaedia MRI Scans, combining 7,023 MRI images in four categories: glioma, meningioma, no tumor, and pituitary tumor. After evaluating individual models, we integrated them into RDXNet using feature fusion and transfer learning. Our model achieves an accuracy of 94%, exceeding the performance of individual models and mitigating overfitting. To validate robustness, K-Fold Cross-Validation was conducted across multiple data splits. Additionally, Grad-CAM-based visualizations were employed to enhance interpretability, enabling clinicians to understand the model’s decision-making. Using hybrid deep learning techniques, RDXNet significantly improves classification performance and reliability. This study demonstrates the potential of Artificial Intelligence (AI)-driven computer-aided diagnosis (CAD) systems to support radiologists, enabling faster and more accurate brain tumor identification, ultimately improving patient outcomes. Our proposed hybrid model, RDXNet outperforms individual architectures in multiclass brain tumor classification, achieving state-of-the-art performance and contributing towards faster, more reliable automated diagnosis.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100421"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219005","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 model for automated contact tracing during disease outbreaks","authors":"Zeyad Aklah ,&nbsp;Amean Al-Safi ,&nbsp;Marwa H. Abdali ,&nbsp;Khalid Al-jabery","doi":"10.1016/j.health.2025.100389","DOIUrl":"10.1016/j.health.2025.100389","url":null,"abstract":"<div><div>This study aims to develop and evaluate a conceptual model for assessing the Risk of Infection (ROI) within the context of automated digital contact tracing during pandemics. The proposed model incorporates five input parameters: distance, overlap time, contamination interval, incubation time, and contact facility size. These parameters capture various aspects of disease transmission dynamics. The model employs logistic functions to quantify the influence of each parameter on the overall ROI. The evaluation of the model involves two methods: a partial evaluation to observe the impact of parameter pairs on ROI, and a full evaluation, which is trained on a dataset of 24,000 simulated scenarios to identify central clusters for high, medium, and low-risk categories using K-means and the Hidden Markov Model. Additionally, the model is tested on another 16,000 simulated scenarios to assess its overall performance. Results indicate that the Hidden Markov Model categorizes 63.8% of the testing dataset as low risk, 20.7% as medium risk, and 15.5% as high risk. In contrast, K-means classifies 44.3% as low risk, 30.7% as medium risk, and 25% as high risk. The evaluation metrics favor the Hidden Markov Model, which demonstrates higher performance in terms of Log-Likelihood, with a value of 50,688, as well as in the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), with values of -101,365.6430 and -101,319.5609, respectively. In both evaluations, the results validate the model’s ability to automate digital contact tracing based on the input parameters. Future studies could explore classification accuracy using real contact tracing datasets. The proposed approach enhances the efficiency of public health authorities by directing their efforts toward individuals with the highest risk of infection, rather than applying the same level of intervention indiscriminately to everyone.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"7 ","pages":"Article 100389"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143619509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An interpretable machine learning study for developing a binary classifier for predicting rehospitalization from skilled nursing facilities","authors":"Zhouyang Lou ,&nbsp;Zachary Hass ,&nbsp;Nan Kong","doi":"10.1016/j.health.2025.100387","DOIUrl":"10.1016/j.health.2025.100387","url":null,"abstract":"<div><div>Reducing hospital readmissions for older adults discharged to a skilled nursing facility (SNF) is important to the Unites States (U.S.) both from financial and care quality perspectives. To identify potential risk factors, researchers have used data from claims, national surveys, and administrative databases to train models that predict hospital readmissions that occur within 30 days of discharge. Machine learning techniques hold promise for this binary classification task. However, analysis pipelines are underdeveloped in data balancing, feature selection, and model interpretability. In this paper, we utilized individual resident-level data from the Long-Term Care Minimum Data Set (MDS) collected from SNFs in a midwestern U.S. state (n = 93,058). We further triangulated this data with publicly available facility quality and staffing data from the Nursing Home Compares tool of the Medicare.gov and facility neighborhood data from the National Neighborhood Data Archive. We compared several machine learning models, data balancing techniques, and feature selection methods, for the prediction task. We found that XGBoost, with Synthetic Minority Oversampling Edited Nearest Neighbor (SMOTE-ENN) to balance the data, and hierarchical clustering based on spearman correlation to select the features that produces the best prediction performance. We then used SHapley Additive exPlanations (SHAP) values to identify features that contribute most to the performance and used partial dependence plots to examine curvilinear and moderating relationships between features and the risk of 30-day rehospitalization.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"7 ","pages":"Article 100387"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An integrated stacked convolutional neural network and the levy flight-based grasshopper optimization algorithm for predicting heart disease","authors":"Syed Muhammad Salman Bukhari ,&nbsp;Muhammad Hamza Zafar ,&nbsp;Syed Kumayl Raza Moosavi ,&nbsp;Majad Mansoor ,&nbsp;Filippo Sanfilippo","doi":"10.1016/j.health.2024.100374","DOIUrl":"10.1016/j.health.2024.100374","url":null,"abstract":"<div><div>Cardiovascular disease is the leading cause of death worldwide, including critical conditions such as blood vessel blockage, heart failure, and stroke. Accurate and early prediction of heart disease remains a significant challenge due to the complexity of symptoms and the variability of contributing factors. This study proposes a novel hybrid model integrating a Stacked Convolutional Neural Network (SCNN) with the Levy Flight-based Grasshopper Optimization Algorithm (LFGOA) to address this challenge. The SCNN provides robust feature extraction, while LFGOA enhances the model by optimizing hyperparameters, improving classification accuracy, and reducing overfitting. The proposed approach is evaluated using four publicly available heart disease datasets, each representing diverse clinical and demographic features. Compared to traditional classifiers, including Regression Trees, Support Vector Machine, Logistic Regression, K-Nearest Neighbors, and standard Neural Networks, the SCNN-LFGOA consistently outperforms these methods. The results highlight that the SCNN-LFGOA achieves an average accuracy of 99%, with significant improvements in specificity, sensitivity, and F1-Score, showcasing its adaptability and robustness across datasets. This study highlights the SCNN-LFGOA's potential as a transformative tool for early and accurate heart disease prediction, contributing to improved patient outcomes and more efficient healthcare resource utilization. By combining deep learning with an advanced optimization technique, this research introduces a scalable and effective solution to a critical healthcare problem.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"7 ","pages":"Article 100374"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143171471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comparative assessment of machine learning models and algorithms for osteosarcoma cancer detection and classification","authors":"Amoakoh Gyasi-Agyei","doi":"10.1016/j.health.2024.100380","DOIUrl":"10.1016/j.health.2024.100380","url":null,"abstract":"<div><div>Osteosarcoma is a bone-forming tumor that is more common in children and young adults than in adults. Timely detection and classification of its type is crucial to its proper treatment and possible survival. Machine learning (ML) models trained on disease datasets are more effective in detection and classification than the conventional methods with hand-crafted features highly dependent on pathologists’ expertise. A publicly available raw osteosarcoma dataset was explored and then preprocessed using different combinations of data denoising techniques (including principal component analysis, mutual information gain, analysis of variance and Kendall’s rank correlation analysis) and data augmentation to <em>derive</em> seven different datasets. Using the seven derived datasets and eight ML algorithms, this study designed and performed an extensive comparative analysis of seven sets of ML models (altogether over 160 models) with their hyperparameters optimized using grid search. The performance differences between the learned ML models were then validated using repeated stratified 10-fold cross-validation and 5x2 cross-validation paired <em>t</em>-tests to select the best model for our task. The empirical model based on the extra trees algorithm and fitted to class-balanced dataset via random oversampling and multicollinearity removed via principal component analysis proved to be the best, as it detected and classified osteosarcoma cancer in 10 ms with 97.8% area under the receiver operating characteristics curve and acceptably low false alarm and misdetection. Thus, the proposed models can be cutting-edge techniques for automated detection and classification of osteosarcoma tumors to aid timely diagnosis, prognosis, and treatment.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"7 ","pages":"Article 100380"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143169863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}