Healthcare analytics (New York, N.Y.)最新文献_第5页

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 : 2025-08-30 DOI: 10.1016/j.health.2025.100414

Ahed Abugabah

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.

乳腺癌是全世界妇女中最常见的癌症，占新病例的很大比例。深度学习（DL）已经成为乳腺癌检测和诊断的强大工具，特别是通过对组织学图像的分析，这是直接影响患者管理的自动化诊断系统的关键组成部分。BreakHis数据集和威斯康星乳腺癌数据库（WBCD）是广泛使用的公共资源，用于跨学科医疗保健研究中基于深度学习的乳腺癌组织学图像分析。计算机辅助方法采用颜色归一化来减少乳腺组织病理学图像分布差异的影响。在本文中，在分割阶段利用注意力引导的深度阿鲁斯-残余U-Net对感兴趣的乳腺肿瘤区域进行分割。然后对patch进行处理，形成特征向量VGG19和ResNet50，从patch中提取深度特征。此外，为了进一步微调这些模型，我们使用了乳腺癌数据集，并使用Levy Flight-based Red Fox Optimisation从预先训练的模型中提取特征，而无需进一步训练。高效胶囊网络用于提高特征表示和分类能力。研究中提出的AGDATUNet-LFRFO-ECN模型在WBCD数据集上的测试结果优于其他模型，灵敏度为99.17%，特异性为99.08%，准确率为99.23%。此外，AGDATUNet-LFRFO-ECN的灵敏度为99.81%，特异性为99.79%，准确率为99.82%，优于BreakHis上现有的模型，达到了最先进的水平。

{"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}

引用次数: 0

A comparative analysis of generalized additive models for obesity risk prediction 肥胖风险预测的广义加性模型的比较分析

Healthcare analytics (New York, N.Y.)

Pub Date : 2025-08-27 DOI: 10.1016/j.health.2025.100410

Olushina Olawale Awe , Olawale Abiodun Olaniyan , Ayorinde Emmanuel Olatunde , Ronel SewPaul , Natisha Dukhi

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

=

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

=

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.

肥胖是一个日益严重的全球健康危机，传统的回归模型往往无法捕捉风险因素之间的复杂关系，从而限制了预测的准确性，阻碍了有效的公共卫生干预。传统方法忽略了人口统计、社会经济和行为预测因素之间的非线性关联和相互作用效应，这在具有不同肥胖决定因素的不同人群中尤为重要。为了解决这些局限性，我们应用了位置、规模和形状的广义加性模型（GAMLSS）来分析全国代表性青少年样本（N = 671）的肥胖预测因子。我们的框架包括人口统计学、社会经济、行为和临床领域的综合变量选择，与三种替代回归模型的比较，并使用广义赤池信息标准（gac）进行验证。二项逐步GAMLSS模型表现出较好的性能（GAIC = 624.98）。主要发现包括强烈的地理差异、显著的性别差异、社会经济梯度和重要的行为预测因素，如体重增加的尝试。GAMLSS框架通过建模传统方法经常忽略的复杂关系来改进肥胖风险预测，提供基于地理、性别和社会经济因素的有针对性的干预策略，并挑战有关饮食影响的假设。

{"title":"A comparative analysis of generalized additive models for obesity risk prediction","authors":"Olushina Olawale Awe , Olawale Abiodun Olaniyan , Ayorinde Emmanuel Olatunde , Ronel SewPaul , 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}

引用次数: 0

A comprehensive diagnostic framework for hepatitis C using structured data and predictive analytics 使用结构化数据和预测分析的丙型肝炎综合诊断框架

Healthcare analytics (New York, N.Y.)

Pub Date : 2025-08-20 DOI: 10.1016/j.health.2025.100412

Behnaz Motamedi, Balázs Villányi

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.

本研究假设结构化的预处理和特征选择方法可以大大提高机器学习（ML）模型在使用临床和人口统计数据预测丙型肝炎病毒（HCV）阶段的分类准确性和泛化性。HCV是一种慢性肝脏疾病，其特点是有许多阶段，需要精确和及时的分类以获得最佳治疗。尽管机器学习为阶段预测提供了机会，但类不平衡、数据缺失和特征冗余等问题限制了模型的有效性和泛化性。为了验证这一理论，我们建立了一个广泛的四阶段准备流程：基线使用特定类别的方法估算缺失值；细化通过类特定的中位数和标准化减轻异常值；平衡解决了五个阶段的阶级不平衡，采用局部随机仿射阴影采样；而Augmented则结合了基于聚类的特征，该特征来源于K-means和高斯混合模型的集合，并结合了主成分分析。采用ReliefF方法优化特征选择，采用随机搜索的随机森林分类器建立预测模型。该模型在训练集上的准确率为0.9983，精密度为0.9984，召回率为0.9983，f1得分为0.9984，马修斯相关系数（MCC）为0.9979。独立检验的准确度为0.9977，精密度为0.9976，召回率为0.9981，f1分数为0.9978，MCC为0.9973。整体聚类成分具有合理的效度，调整后的Rand指数为1.0，剪影系数为0.4702，Davies-Bouldin得分为1.1745，略优于单个聚类方法。研究结果支持了这一假设，并表明彻底的预处理、严格的特征选择和模型优化为预测HCV分期提供了高度准确和可推广的工具，从而改善了临床诊断和治疗策略。

{"title":"A comprehensive diagnostic framework for hepatitis C using structured data and predictive analytics","authors":"Behnaz Motamedi, 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}

引用次数: 0

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 : 2025-08-19 DOI: 10.1016/j.health.2025.100411

Yead Rahman , Prerna Dua

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.

医疗补助数据由于其庞大的规模和异质性，在预测建模和医疗保健分析方面提出了重大挑战。这项研究分析了路易斯安那州卫生部（LDH）的630多万份记录，以确定预测临床服务利用、COVID-19感染和烟草使用的最有效的机器学习模型。严格的预处理流程确保了数据的完整性，而探索性数据分析（EDA）指导了特征选择，最终保留了20个关键变量来捕获复杂的交互。基于预测性能、计算效率和特征重要性评估了7种监督模型，即逻辑回归、极端梯度增强（XGBoost）、自适应增强（AdaBoost）、随机森林、决策树、人工神经网络（ANN）和naïve贝叶斯。虽然集成方法（如XGBoost和随机森林）实现了卓越的准确性，但它们的高计算需求突出了大规模医疗保健分析中性能和效率之间的权衡。更简单的模型，如naïve贝叶斯和决策树，计算效率高，但准确性较低。主要预测因素包括医疗服务使用的住院时间、COVID-19风险的烟草使用以及烟草使用的慢性阻塞性肺疾病（COPD）。这些发现强调了合并症和人口统计学对医疗保健利用的影响，为医疗保健从业者和政策制定者提供了数据驱动的见解，以加强患者护理，优化成本，并完善政策决策。

{"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":"2025-08-19","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}

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 : 2025-08-11 DOI: 10.1016/j.health.2025.100409

Deblina Mazumder Setu

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.

有效和早期发现自闭症谱系障碍（ASD）是提高诊断和干预效果的关键目标。基于功能磁共振成像（fMRI）和问卷调查的各种方法已经被探索，其中眼动追踪是一种很有前途的方法。然而，依靠眼动追踪的现有方法往往将我们限制在受控环境中，使事情变得复杂和昂贵。本研究通过专注于ASD检测的眼动数据，消除了对特定参数的要求，因此引入了一种新颖且用户友好的技术。采用特征工程，包括预处理和提取相关凝视运动数据。这些特性被用于机器学习和深度学习模型训练，并通过超参数调整进行优化。使用Saliency4ASD数据集并超越其通常的凝视焦点，本研究建立了一个仅使用眼球运动来识别ASD的模型，准确率约为81%。这种安全、低成本的方法有可能提供简单的技术，使自闭症谱系障碍的早期检测成为可能，从而使每个人都能获得这种方法。

{"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}

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 : 2025-08-04 DOI: 10.1016/j.health.2025.100408

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

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.

卷积神经网络（cnn）因其强大的特征提取能力而被广泛应用，特别是在医学分类任务中。然而，他们不透明的决策过程在临床环境中提出了挑战，其中可解释性和信任是至关重要的。本研究研究了使用干咳谱图为Covid-19和非Covid-19分类开发的自定义CNN模型的可解释性，重点是解释过滤器级表示和决策途径。为了提高模型的透明度，我们应用了一套可解释的人工智能（XAI）技术，包括特征可视化、SmoothGrad、Grad-CAM和LIME，这些技术解释了光谱-时间特征在分类过程中的相关性。此外，我们使用Guided Grad-CAM和Integrated Gradients与预训练的MobileNetV2模型进行了比较分析。结果表明，虽然MobileNetV2产生了一定程度的视觉归因，但其解释，特别是对Covid-19的预测是分散和不一致的，限制了它们的可解释性。相比之下，自定义CNN模型显示出更连贯和特定类别的激活模式，提供了诊断相关特征的改进定位。

{"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":"2025-08-04","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}

引用次数: 0

A decision-theoretic method for analyzing crossing survival curves in healthcare 医疗保健交叉生存曲线分析的决策理论方法

Healthcare analytics (New York, N.Y.)

Pub Date : 2025-07-17 DOI: 10.1016/j.health.2025.100405

Elie Appelbaum , Moshe Leshno , Eitan Prisman , Eliezer Z. Prisman

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.

迄今为止，在医学研究文献中，卡普兰-迈耶曲线的交叉问题尚未得到解决。本文将生存曲线比较整合到决策理论中，为Kaplan-Meier曲线交叉问题提供了一个理论框架和解决方案。决策理论的应用使我们能够应用随机优势概念和风险偏好属性来比较处理，即使在标准Kaplan-Meier曲线交叉时也是如此。本文表明，通过引入额外的风险偏好属性，Kaplan-Meier曲线可以在较弱的约束下排序，即具有较高的随机优势阶数。因此，即使交叉的Kaplan-Meier曲线也可以排序。我们提出的方法允许我们从生存函数中提取所有可能的信息；因此，使用标准Kaplan-Meier曲线无法识别的优越治疗方法可能会被识别出来。我们的方法应用于已发表的实证医学研究的两个例子。我们表明，治疗被认为是不可比较的，因为它们的Kaplan-Meier曲线相交，可以使用我们的方法进行比较。

{"title":"A decision-theoretic method for analyzing crossing survival curves in healthcare","authors":"Elie Appelbaum , Moshe Leshno , Eitan Prisman , 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}

引用次数: 0

An integrated machine learning and fractional calculus approach to predicting diabetes risk in women 综合机器学习和分数微积分方法预测女性糖尿病风险

Healthcare analytics (New York, N.Y.)

Pub Date : 2025-07-15 DOI: 10.1016/j.health.2025.100402

David Amilo , Khadijeh Sadri , Evren Hincal , Muhammad Farman , Kottakkaran Sooppy Nisar , Mohamed Hafez

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.

本研究提出了一种新的双重方法来预测女性糖尿病风险，将机器学习分类与分数阶生理建模相结合。我们采用了七种机器学习算法：决策树、逻辑回归、支持向量机（SVM）、随机森林、袋装树、朴素贝叶斯和XGBoost来识别关键风险因素，其中XGBoost表现出更高的性能。在所有模型中，血糖水平、BMI、血压和糖尿病谱系函数是最重要的预测因子。为了补充这些数据驱动的见解，我们开发了一个Caputo分数阶模型，该模型捕获了葡萄糖-胰岛素调节、BMI和血压的时间动态。通过不动点定理分析，我们证明了解的存在唯一性，而使用拉格朗日多项式插值的数值实现揭示了不同分数阶对代谢响应模式的影响。这一数学框架为糖尿病的发展提供了独特的见解，特别是通过其模拟记忆效应和长期生理变化的能力。我们的研究的实际实施特点是一个直观的图形用户界面（GUI），集成了这两种方法，实现了实时风险评估和动态反馈。我们对皮马印第安人数据集的分析证实了重要的生理关系，包括年龄-怀孕和bmi -皮肤厚度的相关性。这种双方法框架结合了机器学习的即时预测能力和分数阶模型的纵向视角，为临床医生提供了糖尿病管理的综合工具。机器学习组件提供了准确的短期风险分层，而分数阶模型增强了对长期疾病进展的理解。总之，它们能够实现更加个性化和主动的护理策略，促进糖尿病风险评估的理论和实践。

{"title":"An integrated machine learning and fractional calculus approach to predicting diabetes risk in women","authors":"David Amilo , Khadijeh Sadri , Evren Hincal , Muhammad Farman , Kottakkaran Sooppy Nisar , 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}

引用次数: 0

An explainable analytical approach to heart attack detection using biomarkers and nature-inspired algorithms 一种可解释的分析方法来检测心脏病发作使用生物标志物和自然启发算法

Healthcare analytics (New York, N.Y.)

Pub Date : 2025-07-11 DOI: 10.1016/j.health.2025.100407

Maithri Bairy , Krishnaraj Chadaga , Niranjana Sampathila , R. Vijaya Arjunan , G. Muralidhar Bairy

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.

心脏病发作是全球死亡的主要原因之一，尽早发现高危患者对降低死亡率至关重要。先进的机器学习和深度学习算法已被有效地用于基于临床和实验室标记物预测心脏病发作的存在。本研究使用了五种可解释的人工智能技术（XAI）来确保模型做出的预测是可理解和可解释的，以促进临床决策。14种受自然启发的特征选择算法被应用于识别最具信息量的标记，同时优化预测模型以提高准确性和可靠性。互信息检测精度最高可达90%，最高可达94%。鲸鱼优化算法、Jaya算法、灰狼优化器和正弦余弦算法紧随其后。XAI结果显示，最重要的指标是ST斜率、Oldpeak、运动性心绞痛、胸痛类型和空腹血糖。这些模型可以在医疗机构中实施，以早期预测心脏病发作风险，及时干预，减少严重心血管疾病的可能性。通过为医疗保健专业人员提供计算机辅助诊断工具，这些系统可以增强针对患者的决策，同时减轻对医疗保健资源的压力。

{"title":"An explainable analytical approach to heart attack detection using biomarkers and nature-inspired algorithms","authors":"Maithri Bairy , Krishnaraj Chadaga , Niranjana Sampathila , R. Vijaya Arjunan , 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":"2025-07-11","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}

引用次数: 0

An analytical framework for enhancing cancer care efficiency in North London hospitals 提高北伦敦医院癌症护理效率的分析框架

Healthcare analytics (New York, N.Y.)

Pub Date : 2025-07-09 DOI: 10.1016/j.health.2025.100406

Elizabeth A. Cooke , Nadia A.S. Smith , Donna Chung , Ben Goretzki , Spencer A. Thomas , Adrienne Flanagan , Craig Gerrand , Neal Navani , Prabhakar Rajan , Ashoke Roy , Clare Schilling , Ellie Smyth , Paul Stimpson , Sandra J. Strauss , Derralynn Hughes

We use mathematical and statistical techniques on operational data to examine the impact of different factors on the time to treatment for cancer patients in North London hospitals. Understanding the factors which prolong the time between referral and treatment starting for cancer patients on pathways which cross healthcare providers is imperative to improved patient care. We analyse three tumour pathways which involve transfer of patients between hospitals: sarcoma, urological, and head and neck cancers. Several factors impact on the time to first treatment including demographic characteristics, day of the week first seen and method of communicating the cancer diagnosis. In particular, we found that head and neck patients from lower socioeconomic areas were more likely to have longer times from referral to treatment. Patients with sarcoma who were first seen on a Sunday are more likely to breach the 28-day faster diagnosis standard. This analysis is an important first step in highlighting where focus is needed to improve cancer care pathways. Understanding and mitigating the factors influencing the length of time between referral and treatment could enhance the efficiency of cancer care pathways and, consequently, patient outcomes.

我们在操作数据上使用数学和统计技术来检查不同因素对北伦敦医院癌症患者治疗时间的影响。了解延长转诊和治疗之间的时间的癌症患者的途径，交叉医疗保健提供者的因素是必要的，以改善患者护理。我们分析了三种肿瘤途径，涉及患者在医院之间的转移：肉瘤、泌尿科和头颈癌。有几个因素影响第一次治疗的时间，包括人口统计学特征、第一次就诊的星期几和沟通癌症诊断的方法。特别是，我们发现来自社会经济地位较低地区的头颈部患者从转诊到治疗的时间更可能更长。在周日首次发现的肉瘤患者更有可能违反28天快速诊断标准。这项分析是重要的第一步，突出了需要重点改善癌症治疗途径的地方。了解和减轻影响转诊和治疗之间时间长度的因素可以提高癌症治疗途径的效率，从而提高患者的预后。

{"title":"An analytical framework for enhancing cancer care efficiency in North London hospitals","authors":"Elizabeth A. Cooke , Nadia A.S. Smith , Donna Chung , Ben Goretzki , Spencer A. Thomas , Adrienne Flanagan , Craig Gerrand , Neal Navani , Prabhakar Rajan , Ashoke Roy , Clare Schilling , Ellie Smyth , Paul Stimpson , Sandra J. Strauss , Derralynn Hughes","doi":"10.1016/j.health.2025.100406","DOIUrl":"10.1016/j.health.2025.100406","url":null,"abstract":"<div><div>We use mathematical and statistical techniques on operational data to examine the impact of different factors on the time to treatment for cancer patients in North London hospitals. Understanding the factors which prolong the time between referral and treatment starting for cancer patients on pathways which cross healthcare providers is imperative to improved patient care. We analyse three tumour pathways which involve transfer of patients between hospitals: sarcoma, urological, and head and neck cancers. Several factors impact on the time to first treatment including demographic characteristics, day of the week first seen and method of communicating the cancer diagnosis. In particular, we found that head and neck patients from lower socioeconomic areas were more likely to have longer times from referral to treatment. Patients with sarcoma who were first seen on a Sunday are more likely to breach the 28-day faster diagnosis standard. This analysis is an important first step in highlighting where focus is needed to improve cancer care pathways. Understanding and mitigating the factors influencing the length of time between referral and treatment could enhance the efficiency of cancer care pathways and, consequently, patient outcomes.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100406"},"PeriodicalIF":0.0,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144632687","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