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

An attention-based loss function and synthetic minority oversampling technique for alleviating class imbalance in predicting diabetes 基于注意力的损失函数和合成少数派过采样技术在糖尿病预测中的应用

Healthcare analytics (New York, N.Y.) Pub Date : 2025-06-01 DOI: 10.1016/j.health.2025.100399

Santanu Roy , Reshma Rachel Cherish , Gifty Roy

{"title":"An attention-based loss function and synthetic minority oversampling technique for alleviating class imbalance in predicting diabetes","authors":"Santanu Roy , Reshma Rachel Cherish , Gifty Roy","doi":"10.1016/j.health.2025.100399","DOIUrl":"10.1016/j.health.2025.100399","url":null,"abstract":"<div><div>Diabetes is a chronic disease due to higher blood sugar (or Glucose) levels in the blood. This study proposes a novel attention-based loss function and a lightweight artificial neural network (ANN) called Diabetic Lite (DB-Lite) for diabetes prediction in the Pima Indian Diabetes Dataset (PIDD). We show that the Pima dataset has many challenges. It is a small and imbalanced dataset; moreover, many features are non-linearly correlated in this dataset. The novelties of this research work are as follows: (i) A novel loss function of attention-based binary cross entropy (ABCE) is proposed for the first time to alleviate the statistical imbalance present within the Pima dataset. This ABCE loss function is incorporated in the DB-Lite model, which is trained from scratch. (ii) A Swish activation function is deployed in the hidden layer of DB-Lite instead of Rectified Linear Unit (ReLU) to deal with the non-linear dependency of features with the final outcome. (iii) The synthetic minority oversampling technique (SMOTE) is used as a pre-processing technique to mitigate the class imbalance problem from the Pima dataset. (iv) An adaptive learning rate is utilized while training the model to speed up the convergence of the DB-Lite model. Our final proposed framework has achieved 99.7% accuracy, 99.4% precision, 99.8% recall, and 99.6% F1 score in testing, which is the best result on this Pima dataset. The Welch t-testing (as a statistical hypothesis testing) and 10-fold cross-validation are utilized to prove the validity of the proposed loss function.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"7 ","pages":"Article 100399"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144185724","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 approach to assessing the spatial equity and allocation of healthcare resources in Shanghai 上海市卫生资源空间公平与配置分析方法

Healthcare analytics (New York, N.Y.) Pub Date : 2025-06-01 DOI: 10.1016/j.health.2025.100400

Hong-Yan Li , Jing Guo, Chuang-Hao Yang

{"title":"An analytical approach to assessing the spatial equity and allocation of healthcare resources in Shanghai","authors":"Hong-Yan Li , Jing Guo, Chuang-Hao Yang","doi":"10.1016/j.health.2025.100400","DOIUrl":"10.1016/j.health.2025.100400","url":null,"abstract":"<div><div>The rational allocation of healthcare resources is vital for establishing a healthcare system that aligns with the levels of economic and social development. As a comprehensive discipline integrating geography, cartography, remote sensing, and computer science, Geographic Information System (GIS) can visualize and analyze spatial information through mapping. By utilizing GIS's statistical analysis and data visualization functions, this study provides a more efficient and intuitive analysis of Shanghai's spatial healthcare resource allocation and a more comprehensive assessment of its current allocation status. To examine the spatial correlation and spatial proximity, we apply the Global Moran Index (Moran's I), the Local Indicators of Spatial Association (LISA) test, and Hot Spot Analysis (Getis-Ord Gi∗) for assessment. Furthermore, by utilizing the Lorenz curve and Gini coefficient, this study provides a new perspective by expanding the measurement dimensions for assessing healthcare resource allocation in Shanghai. The results show that: From the global spatial correlation perspective, the allocation of healthcare resources in Shanghai exhibits spatial clustering. From the local spatial correlation perspective, healthcare resources in Shanghai show significant regional disparities, with resources concentrated in central urban areas. And from a multidimensional perspective, the equity of allocation of healthcare resources in Shanghai in 2022 was higher when measured by population (0.298 ± 0.063) and economy (0.292 ± 0.027) than by geographic area (0.612 ± 0.100) and green spaces (0.590 ± 0.110) of the Gini coefficient. These findings offer valuable insights for promoting the structural optimization and spatial distribution of healthcare resources in Shanghai.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"7 ","pages":"Article 100400"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144185725","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 equity-based spatial analytics framework for evaluating pharmacy accessibility using geographical information systems 利用地理信息系统评价药房可及性的基于公平的空间分析框架

Healthcare analytics (New York, N.Y.) Pub Date : 2025-05-21 DOI: 10.1016/j.health.2025.100401

Sara Al-Naabi , Noura Al Nasiri , Talal Al-Awadhi , Meshal Abdullah , Ammar Abulibdeh

{"title":"An equity-based spatial analytics framework for evaluating pharmacy accessibility using geographical information systems","authors":"Sara Al-Naabi , Noura Al Nasiri , Talal Al-Awadhi , Meshal Abdullah , Ammar Abulibdeh","doi":"10.1016/j.health.2025.100401","DOIUrl":"10.1016/j.health.2025.100401","url":null,"abstract":"<div><div>Healthcare services have a significant impact on socioeconomic and health development globally. In Oman, rapid development since the 1970s has led to a focus on the equitable distribution of public services. This research aims to evaluate the spatial accessibility and distribution of pharmacies in Muscat Governorate, Oman, using Geographical Information Systems (GIS) and spatial analysis techniques. The primary objective is to measure the equity in the spatial distribution of pharmacies within Muscat Governorate. The study utilizes spatial datasets, including administrative areas, pharmacy locations, settlement locations, transportation networks, and non-spatial datasets such as demographic data. The methodology involves spatial distribution analysis using Average Nearest Neighbor (ANN), Moran's I for spatial autocorrelation, Kernel Density Analysis (KDA), Thiessen polygons for catchment areas, and Network analysis for determining service areas and accessibility by walking and driving distances. Findings indicate a clustered distribution of pharmacies, with higher concentrations in densely populated northern Wilayats like Muttrah, AS Seeb, and Bawshar. Muttrah exhibits the highest accessibility, with 99 % coverage within a 2.5 km radius, whereas Muscat Wilaya lacks pharmacy services entirely. These findings highlight significant disparities in the spatial distribution of pharmacies, underscoring the need for policy interventions to ensure equitable access. Policymakers should consider geographic and demographic factors in health service planning to ensure fair distribution and accessibility across the governorate. Implementing these recommendations can help improve healthcare access and equity in Muscat, contributing to overall social and health development.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"7 ","pages":"Article 100401"},"PeriodicalIF":0.0,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147169","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 Chronic Kidney Disease stage classification 慢性肾脏疾病分期分类的深度学习框架

Healthcare analytics (New York, N.Y.) Pub Date : 2025-05-20 DOI: 10.1016/j.health.2025.100398

Gayathri Hegde M , P Deepa Shenoy , Venugopal KR , Arvind Canchi

{"title":"A Deep Learning Framework for Chronic Kidney Disease stage classification","authors":"Gayathri Hegde M , P Deepa Shenoy , Venugopal KR , Arvind Canchi","doi":"10.1016/j.health.2025.100398","DOIUrl":"10.1016/j.health.2025.100398","url":null,"abstract":"<div><div>Chronic Kidney Disease (CKD) has become more prevalent, leading to a gradual decline in kidney function and, ultimately, in renal failure. Timely detection of the CKD stage is essential for enhancing healthcare services and decreasing morbidity and mortality. Hence, this study proposes a Metaheuristic-Hybrid Metaheuritstic eXplainable Artificial Intelligence (MHMXAI) driven Feature Selection (FS) approach and Deep Learning (DL) models for CKD stage prediction. MHMXAI approach selects the features with the highest scores from the Metaheuristic algorithm-Eagle Search Strategy, Hybrid Metaheuristic algorithm-Great Salmon Run-Thermal Exchange Optimization and eXplainable AI (XAI) tools like Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive exPlanations (SHAP) for their effectiveness. To evaluate the proposed method, eight DL models — Feedforward Neural Network, Recurrent Neural Network, Deep Neural Network, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Bidirectional LSTM, Gated Recurrent Unit (GRU) and Bidirectional GRU were trained on selected features using different FS methods, as well as complete dataset. The models were assessed using performance metrics such as accuracy, precision, recall, F1-Score, Loss, Validation Loss and computation time. The CNN model outperformed others, achieving an accuracy between 98%-99.5% for all FS methods. Statistical tests, including the Friedman and Nemenyi post-hoc test, identified the CNN model trained with MHMXAI-selected features as the most robust choice for CKD stage prediction. These findings demonstrate that the proposed MHMXAI method effectively integrates metaheuristic algorithms and XAI tools, improving CKD stage prediction accuracy and clinical interpretability.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"7 ","pages":"Article 100398"},"PeriodicalIF":0.0,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144115378","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 hybrid deep learning framework for early detection of Mpox using image data 基于图像数据的Mpox早期检测混合深度学习框架

Healthcare analytics (New York, N.Y.) Pub Date : 2025-05-14 DOI: 10.1016/j.health.2025.100396

Sajal Chakroborty

引用次数: 0

A predictive healthcare model using machine learning and psychological factors for medication adherence 使用机器学习和药物依从性心理因素的预测性医疗保健模型

Healthcare analytics (New York, N.Y.) Pub Date : 2025-05-03 DOI: 10.1016/j.health.2025.100397

Junwu Dong , Minyi Chu , Yirou Xu

{"title":"A predictive healthcare model using machine learning and psychological factors for medication adherence","authors":"Junwu Dong , Minyi Chu , Yirou Xu","doi":"10.1016/j.health.2025.100397","DOIUrl":"10.1016/j.health.2025.100397","url":null,"abstract":"<div><div>Ensuring effective medication adherence is vital for managing chronic diseases, yet global patient adherence remains suboptimal. This study aims to develop a predictive model for medication adherence behaviour (MAB) employing machine learning techniques, addressing the limitations of traditional correlation-based approaches. Based on the Meta-Theoretic Model of Motivation and Personality (3M Model), data from 428 chronic disease patients, included dark triad traits (narcissism, Machiavellianism, psychopathy), general self-efficacy, doctor-patient trust, and demographic variables. Five machine learning algorithms – multiple logistic regression, decision tree, adaptive boosting, random forest and support vector machine (SVM) – were utilized to identify MAB levels and assess feature importance. Among these, the random forest model achieved the highest performance, with an accuracy of 0.637, recall of 0.538, precision of 0.556, and an F1 score of 0.544. Feature ranking revealed that narcissism, Machiavellianism, doctor-patient trust, psychopathy, and general self-efficacy were the most influential predictors. These findings demonstrate that integrating psychological and demographic factors into machine learning models can enhance the prediction of medication adherence. This study presents a novel interdisciplinary framework that integrates behavioural health analytics and data science to inform clinical decision-making. It provides valuable insights into the severity and temporal progression of medication adherence behaviours, offering clinicians a practical reference for developing more effective intervention strategies.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"7 ","pages":"Article 100397"},"PeriodicalIF":0.0,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143922291","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 transmission model for evaluating pneumonia vaccination and control strategies 评估肺炎疫苗接种和控制策略的分析传播模型

Healthcare analytics (New York, N.Y.) Pub Date : 2025-04-27 DOI: 10.1016/j.health.2025.100394

Dipo Aldila , Abdullah Hasan Hassan , Mohamad Hifzhudin Noor Aziz , Putri Zahra Kamalia

{"title":"An analytical transmission model for evaluating pneumonia vaccination and control strategies","authors":"Dipo Aldila , Abdullah Hasan Hassan , Mohamad Hifzhudin Noor Aziz , Putri Zahra Kamalia","doi":"10.1016/j.health.2025.100394","DOIUrl":"10.1016/j.health.2025.100394","url":null,"abstract":"<div><div>Pneumonia is an infectious disease caused by various agents, such as viruses, bacteria, or fungi. This study proposes an analytical pneumonia model to assess the impact of vaccine interventions. The proposed mathematical model reveals that pneumonia will be eradicated from the population if the basic reproduction number is less than one. Furthermore, our bifurcation analysis indicates the absence of a backward bifurcation, meaning that the basic reproduction number is the sole threshold for determining the endemicity of a disease. In other words, pneumonia will be extinct if the basic reproduction number is less than one and will exist if it is larger than one. We estimate our model parameter values using incidence data from five districts in Jakarta, Indonesia. The dataset consists of weekly incidence data from 2023 until mid-2024. Our analysis shows North Jakarta has the highest case incidence per 100,000 individuals compared to the other districts. A global sensitivity analysis, using the partial rank correlation coefficient and Latin hypercube sampling, was conducted to identify the most impactful parameters on the basic reproduction number for each district in Jakarta. An optimal control problem was formulated to determine the most effective strategies for controlling pneumonia in the field. We found that adult vaccination has a greater impact on reducing the spread of pneumonia than a newborn vaccination strategy. However, combining both newborn and adult vaccinations is essential to ensure long-lasting immunity in children.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"7 ","pages":"Article 100394"},"PeriodicalIF":0.0,"publicationDate":"2025-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143879067","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 hyperparameter optimization framework for noninvasive creatinine estimation using photoplethysmography signals 一个集成的机器学习和超参数优化框架，用于利用光容积脉搏波信号进行无创肌酐估计

Healthcare analytics (New York, N.Y.) Pub Date : 2025-04-22 DOI: 10.1016/j.health.2025.100395

Parama Sridevi, Zawad Arefin, Sheikh Iqbal Ahamed

{"title":"An integrated machine learning and hyperparameter optimization framework for noninvasive creatinine estimation using photoplethysmography signals","authors":"Parama Sridevi, Zawad Arefin, Sheikh Iqbal Ahamed","doi":"10.1016/j.health.2025.100395","DOIUrl":"10.1016/j.health.2025.100395","url":null,"abstract":"<div><div>Frequent measurement of creatinine levels is vital for patients with chronic kidney disease. Traditional creatinine level measurement requires invasive blood test which has several disadvantages like discomfort, anxiety, panic, pain, risk of infection, etc. To address the issue, we propose a noninvasive machine learning (ML) model-based method to estimate creatinine level using photoplethysmography (PPG) signal. We obtained the PPG signal and gold-standard serum creatinine level of 404 patients from the Medical News Mart for Concentrated Care III (MIMIC III) database. In data preprocessing, we analyzed the PPG signal following several steps and created PPG feature set. We used multiple feature engineering methods to identify the most important features. We integrated Optuna, a hyperparameter optimization framework, with every ML model to get the optimal hyperparameters. We developed five ML models and compared their performance both with and without the application of Optuna. We found that Optuna significantly improves every model's performance. With Optuna, extreme gradient boosting (XGBoost) performed best among all five models. This XGBoost model had an accuracy of 85.2 %, an average k-fold cross validation score (k = 10) of 0.70, and a “receiver operating characteristic area under the curve” (ROC-AUC) score of 0.80. With the high performance exhibited by our developed model, the study can play a crucial role in the field of noninvasive creatinine estimation and diagnosis of chronic kidney disease.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"7 ","pages":"Article 100395"},"PeriodicalIF":0.0,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143887787","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 robust transfer learning approach with histopathological images for lung and colon cancer detection using EfficientNetB3 使用EfficientNetB3进行肺癌和结肠癌组织病理学图像检测的稳健迁移学习方法

Healthcare analytics (New York, N.Y.) Pub Date : 2025-04-09 DOI: 10.1016/j.health.2025.100391

Raquel Ochoa-Ornelas , Alberto Gudiño-Ochoa , Julio Alberto García-Rodríguez , Sofia Uribe-Toscano

{"title":"A robust transfer learning approach with histopathological images for lung and colon cancer detection using EfficientNetB3","authors":"Raquel Ochoa-Ornelas , Alberto Gudiño-Ochoa , Julio Alberto García-Rodríguez , Sofia Uribe-Toscano","doi":"10.1016/j.health.2025.100391","DOIUrl":"10.1016/j.health.2025.100391","url":null,"abstract":"<div><div>Lung and colon cancers are among the deadliest diseases worldwide, necessitating early and accurate detection to improve patient outcomes. This study utilizes the EfficientNetB3 model, a state-of-the-art transfer learning approach, to enhance the detection of colon and lung cancers from histopathological images. The research leverages the LC25000 dataset, comprising 25,000 histopathological images evenly distributed across five classes: colon adenocarcinoma, benign colon tissue, lung adenocarcinoma, lung squamous cell carcinoma, and benign lung tissue. The EfficientNetB3 model initially achieved an impressive accuracy of 99.39% across all classes. To further validate and enhance the model’s robustness and generalizability, we augmented the dataset by replacing 1,000 cancerous class images with new Genomic Data Commons (GDC) Data Portal - National Cancer Institute images, simulating more diverse clinical scenarios. This modification resulted in an accuracy of 99.39%, with equally high performance across other metrics, including precision, recall, and F1-Score, all reaching 99.39%, and a Matthew’s Correlation Coefficient (MCC) of 99.24%. The Gradient-weighted Class Activation Mapping (Grad-CAM) technique was utilized to visually interpret the model’s decisions, enhancing its transparency and reliability. These findings demonstrate that EfficientNetB3 is an effective and generalizable end-to-end framework for histopathological image analysis with minimal preprocessing. The promising results underscore the potential of EfficientNetB3 to advance automated cancer detection, thereby contributing to earlier diagnosis and more effective treatment strategies.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"7 ","pages":"Article 100391"},"PeriodicalIF":0.0,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143806834","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 investigation of the impact of organizational big data analytics capabilities on healthcare supply chain resiliency 组织大数据分析能力对医疗保健供应链弹性影响的调查

Healthcare analytics (New York, N.Y.) Pub Date : 2025-04-08 DOI: 10.1016/j.health.2025.100393

Detcharat Sumrit

{"title":"An investigation of the impact of organizational big data analytics capabilities on healthcare supply chain resiliency","authors":"Detcharat Sumrit","doi":"10.1016/j.health.2025.100393","DOIUrl":"10.1016/j.health.2025.100393","url":null,"abstract":"<div><div>Evaluating organizational big data analytics capabilities (BDAC) is crucial for strengthening resilience in healthcare supply chains (HSCs). This study employs an integrated multi-criteria decision-making (MCDM) approach, combining the Decision-making Trial and Evaluation Laboratory (DANP) and Multi-Attributive Border Approximation Area Comparison (MABAC) methods in a fuzzy environment. The goal is to assess the interdependence of BDAC and its impact on resilience within the HSC. The research draws on organizational information processing (OIP) and knowledge-based view (KBV) theoretical lenses to identify relevant BDAC components. The study yields context-specific insights into the role of big data analytics in fortifying the HSC Using a case study in a public hospital. The findings contribute to the understanding of supply chain resilience, emphasizing the pivotal role of BDAC in organizational preparedness. This knowledge can guide healthcare sector managers in making informed decisions to enhance overall resilience, allowing organizations to navigate uncertainties and challenges proactively. Ultimately, leveraging insights from this study can foster a more adaptive and resilient HSC, benefiting both patients and stakeholders.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"7 ","pages":"Article 100393"},"PeriodicalIF":0.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852051","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