Healthcare analytics (New York, N.Y.)最新文献_第4页

A machine learning framework for predicting healthcare utilization and risk factors 用于预测医疗保健利用和风险因素的机器学习框架

Healthcare analytics (New York, N.Y.) Pub Date : 2025-12-01 Epub Date: 2025-08-19 DOI: 10.1016/j.health.2025.100411

Yead Rahman , Prerna Dua

{"title":"A machine learning framework for predicting healthcare utilization and risk factors","authors":"Yead Rahman , 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":"2025-12-01","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}

引用次数: 0

EAGLE-Net: A hierarchical neural network for detecting anatomical landmarks in upper gastrointestinal endoscopy for clinical diagnosis EAGLE-Net：一种用于检测上消化道内镜解剖标志的分层神经网络，用于临床诊断

Healthcare analytics (New York, N.Y.) Pub Date : 2025-12-01 Epub Date: 2025-09-20 DOI: 10.1016/j.health.2025.100420

Thi Thu Huong Nguyen , Van Duy Truong , Xuan Huy Manh , Thanh Tung Nguyen , Hang Viet Dao , Hai Vu

{"title":"EAGLE-Net: A hierarchical neural network for detecting anatomical landmarks in upper gastrointestinal endoscopy for clinical diagnosis","authors":"Thi Thu Huong Nguyen , Van Duy Truong , Xuan Huy Manh , Thanh Tung Nguyen , Hang Viet Dao , Hai Vu","doi":"10.1016/j.health.2025.100420","DOIUrl":"10.1016/j.health.2025.100420","url":null,"abstract":"<div><div>This study proposes a hierarchical network architecture, named EAGLE-Net, for identifying anatomical landmarks in the upper gastrointestinal (GI) tract endoscopic videos. Unlike conventional techniques, which label anatomical landmarks for static endoscopic images, the proposed method aims to classify landmarks from videos of the upper GI tract. Video streams often suffer from many noises and contaminated objects, which requires a new approach to tackle this issue. The proposed technique utilizes hierarchical network architecture, which consists of two stages: endoscopic image quality assessment and anatomical landmark classification. In the first stage, high-quality frames are preserved from GI tract videos. These frames are then used to identify a specific location among ten anatomical landmarks. The proposed method increases the coherence between the hierarchical data levels. It integrates an attention module to strengthen feature connections and utilizes a new hierarchical cross-entropy loss function to optimize model performance. The experimental results demonstrated that the proposed system achieves a high accuracy of 93% on average in both classification stages. In clinical experiments, anatomical landmarks are automatically denoted to help physicians monitor the endoscopy process. In addition, the proposed method demonstrates a potential solution for the deployment of a computer-aided diagnostic application for the detection and treatment of upper GI tract lesions.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100420"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145157325","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}

引用次数: 0

A multi-agent reinforcement learning framework for public health decision analysis 公共卫生决策分析的多智能体强化学习框架

Healthcare analytics (New York, N.Y.) Pub Date : 2025-12-01 Epub Date: 2025-11-20 DOI: 10.1016/j.health.2025.100436

Dinesh Sharma , Ankit Shah , Chaitra Gopalappa

{"title":"A multi-agent reinforcement learning framework for public health decision analysis","authors":"Dinesh Sharma , Ankit Shah , Chaitra Gopalappa","doi":"10.1016/j.health.2025.100436","DOIUrl":"10.1016/j.health.2025.100436","url":null,"abstract":"<div><div>Human immunodeficiency virus (HIV) is a major public health concern in the United States (U.S.), with about 1.2 million people living with it and about 35,000 newly infected each year. There are considerable geographical disparities in HIV burden and care access across the U.S. The ’Ending the HIV Epidemic (EHE)’ initiative by the U.S. Department of Health and Human Services aims to reduce new infections by 90% by 2030, by improving coverage of diagnoses, treatment, and prevention interventions and prioritizing jurisdictions with high HIV prevalence. One of the approaches towards achieving this objective includes developing intelligent decision-support systems that can help optimize resource allocation and intervention strategies. Existing decision analytic models either focus on individual cities or aggregate national data, failing to capture jurisdictional interactions critical for optimizing intervention strategies. To address this, we propose a multi-agent reinforcement learning (MARL) framework that enables jurisdiction-specific decision-making while accounting for cross-jurisdictional epidemiological interactions. Our framework functions as an intelligent resource optimization system, helping policymakers strategically allocate interventions based on dynamic, data-driven insights. Experimental results across jurisdictions in California and Florida demonstrate that MARL-driven policies outperform traditional single-agent reinforcement learning approaches by reducing new infections under fixed budget constraints. Our study highlights the importance of incorporating jurisdictional dependencies in decision-making frameworks for large-scale public initiatives. By integrating multi-agent intelligent systems, decision analytics, and reinforcement learning, this study advances expert systems for government resource planning and public health management, offering a scalable framework for broader applications in healthcare policy and epidemic management.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100436"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145617925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A deep learning framework for automated breast cancer diagnosis using intelligent segmentation and classification 一种使用智能分割和分类的自动化乳腺癌诊断的深度学习框架

Healthcare analytics (New York, N.Y.) Pub Date : 2025-12-01 Epub Date: 2025-08-30 DOI: 10.1016/j.health.2025.100414

Ahed Abugabah

{"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":"2025-12-01","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}

引用次数: 0

An image-based analytics framework for early autism detection using eye movements 一个基于图像的分析框架，用于使用眼球运动进行早期自闭症检测

Healthcare analytics (New York, N.Y.) Pub Date : 2025-12-01 Epub Date: 2025-11-27 DOI: 10.1016/j.health.2025.100439

Roaa Soloh , Lara Abou Orm , Dana Dabdoub

引用次数: 0

An analytics-driven model for identifying autism spectrum disorder using eye tracking 用眼动追踪识别自闭症谱系障碍的分析驱动模型

Healthcare analytics (New York, N.Y.) Pub Date : 2025-12-01 Epub Date: 2025-08-11 DOI: 10.1016/j.health.2025.100409

Deblina Mazumder Setu

{"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":"2025-12-01","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}

引用次数: 0

An in-depth review and analysis of deep learning methods and applications in spinal cord imaging 深入回顾和分析深度学习方法及其在脊髓成像中的应用

Healthcare analytics (New York, N.Y.) Pub Date : 2025-12-01 Epub Date: 2025-10-28 DOI: 10.1016/j.health.2025.100429

Md Sabbir Hossain , Mostafijur Rahman , Mumtahina Ahmed , Ashifur Rahman , Md Mohsin Kabir , M.F. Mridha , Jungpil Shin

{"title":"An in-depth review and analysis of deep learning methods and applications in spinal cord imaging","authors":"Md Sabbir Hossain , Mostafijur Rahman , Mumtahina Ahmed , Ashifur Rahman , Md Mohsin Kabir , M.F. Mridha , Jungpil Shin","doi":"10.1016/j.health.2025.100429","DOIUrl":"10.1016/j.health.2025.100429","url":null,"abstract":"<div><div>This systematic review explores the advances, technologies, and applications of deep learning in spinal cord magnetic resonance imaging (MRI). The current state of deep-learning techniques used for injury detection, disease diagnosis, and treatment planning in spinal cord imaging is thoroughly examined. This review includes a systematic analysis of over 100 studies from 2018 to 2025, selected based on clinical relevance, model performance, and innovation. Through a comprehensive analysis of recent literature, this review highlights the evolution and effectiveness of various deep-learning models in enhancing the accuracy and reliability of spinal cord MRI interpretations. Significant contributions of this review include identifying the most effective and innovative deep-learning approaches, such as Convolutional Neural Networks (CNNs) for precise lesion segmentation and Generative Adversarial Networks (GANs) for data augmentation. Additionally, it synthesizes current applications, such as improved injury detection and multiple sclerosis diagnosis, and explores deep-learning’s role in treatment planning. The review also addresses the challenges and limitations faced in this domain, including data scarcity, model interpretability, and computational demands, and proposes potential solutions and directions for future research. By offering these insights, this review provides a unique perspective on integrating deep-learning models into clinical workflows and their impact on clinical outcomes and patient care.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100429"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145415989","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}

引用次数: 0

An interpretable deep learning framework for medical diagnosis using spectrogram analysis 一个可解释的深度学习框架，用于使用谱图分析的医学诊断

Healthcare analytics (New York, N.Y.) Pub Date : 2025-12-01 Epub Date: 2025-08-04 DOI: 10.1016/j.health.2025.100408

Shagufta Henna , Juan Miguel Lopez Alcaraz , Upaka Rathnayake , Mohamed Amjath

{"title":"An interpretable deep learning framework for medical diagnosis using spectrogram analysis","authors":"Shagufta Henna , Juan Miguel Lopez Alcaraz , Upaka Rathnayake , 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":"2025-12-01","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}

引用次数: 0

An explainable analytics framework for predicting diabetes in women using Convolutional Neural Networks 使用卷积神经网络预测女性糖尿病的可解释分析框架

Healthcare analytics (New York, N.Y.) Pub Date : 2025-12-01 Epub Date: 2025-10-10 DOI: 10.1016/j.health.2025.100422

Gazi Mohammad Imdadul Alam , Tapu Biswas , Sharia Arfin Tanim , M.F. Mridha

{"title":"An explainable analytics framework for predicting diabetes in women using Convolutional Neural Networks","authors":"Gazi Mohammad Imdadul Alam , Tapu Biswas , Sharia Arfin Tanim , M.F. Mridha","doi":"10.1016/j.health.2025.100422","DOIUrl":"10.1016/j.health.2025.100422","url":null,"abstract":"<div><div>Diabetes is a chronic metabolic disorder that heightens the risk of complications for women and presents diagnostic challenges owing to imbalanced datasets and the need for interpretable predictive models. In this study, we propose a 1D Convolutional Neural Network (1D CNN) model that achieves an accuracy of 98.61% on German Patient Dataset, comprising 2,000 samples, and 99.35% on the Bangladeshi Patient Dataset, which includes 465 samples. Our model effectively addresses class imbalance by integrating the Synthetic Minority Over-sampling Technique and Edited Nearest Neighbor (SMOTE-ENN), which significantly enhances performance. Additionally, we conducted a statistical comparison with Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM) models, demonstrating our CNN’s superior accuracy while maintaining reduced complexity and enhanced transparency through the integration of SHapley Additive exPlanations (SHAP). Our SHAP analysis revealed significant variations in feature importance between the two populations, offering culturally relevant insights into the risk factors for diabetes. The SHAP analysis not only facilitates interpretability by allowing healthcare professionals to understand the influence of individual features but also emphasizes the cultural context of diabetes risk. Overall, our findings surpass existing methodologies in terms of accuracy and complexity while underscoring the critical need for demographic diversity in predictive healthcare models, paving the way for more effective diabetes prediction strategies.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100422"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145320222","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}

引用次数: 0

An analytical study of external factors influencing emergency occurrences in healthcare 影响医疗卫生突发事件发生的外部因素分析研究

Healthcare analytics (New York, N.Y.) Pub Date : 2025-12-01 Epub Date: 2025-10-19 DOI: 10.1016/j.health.2025.100426

Félicien Hêche , Philipp Schiller , Oussama Barakat , Thibaut Desmettre , Stephan Robert-Nicoud

{"title":"An analytical study of external factors influencing emergency occurrences in healthcare","authors":"Félicien Hêche , Philipp Schiller , Oussama Barakat , Thibaut Desmettre , Stephan Robert-Nicoud","doi":"10.1016/j.health.2025.100426","DOIUrl":"10.1016/j.health.2025.100426","url":null,"abstract":"<div><div>This study investigates the impact of 19 external factors, related to weather, road traffic conditions, air quality, and time, on the hourly occurrence of emergencies. The analysis relies on six years of dispatch records (2015–2021) from the Centre Hospitalier Universitaire Vaudois (CHUV), which oversees 18 ambulance stations across the French-speaking region of Switzerland. First, classical statistical methods, including Chi-squared test, Student’s <span><math><mi>t</mi></math></span>-test, and information value, are employed to identify dependencies between the occurrence of emergencies and the considered parameters. Additionally, SHapley Additive exPlanations (SHAP) values and permutation importance are computed using eXtreme Gradient Boosting (XGBoost) and Multilayer Perceptron (MLP) models. Training and hyperparameter optimization were performed on data from 2015–2020, while the 2021 data were held out for evaluation and for computing model interpretation metrics. Results indicate that temporal features – particularly the hour of the day – are the dominant drivers of emergency occurrences, whereas other external factors contribute minimally once temporal effects are accounted for. Subsequently, performance comparisons with a simplified model that considers only the hour of the day suggest that more complex machine learning approaches offer limited added value in this context. Operationally, this result supports the use of simple time-dependent demand curves for EMS planning. Such models can effectively guide staffing schedules and relocations without the overhead of integrating external data or maintaining complex pipelines. By highlighting the limited utility of external predictors, this study provides practical guidance for EMS organizations seeking efficient, data-driven resource allocation methods.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100426"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145361876","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}

引用次数: 0