Healthcare analytics (New York, N.Y.)最新文献_第8页

A data-driven approach to pricing models for balanced public–private healthcare systems 以数据为导向的公私平衡医疗保健系统定价模式

Healthcare analytics (New York, N.Y.) Pub Date : 2025-06-01 Epub Date: 2025-02-17 DOI: 10.1016/j.health.2025.100385

Aydin Teymourifar , Onur Kaya , Gurkan Ozturk

{"title":"A data-driven approach to pricing models for balanced public–private healthcare systems","authors":"Aydin Teymourifar , Onur Kaya , Gurkan Ozturk","doi":"10.1016/j.health.2025.100385","DOIUrl":"10.1016/j.health.2025.100385","url":null,"abstract":"<div><div>This study focuses on a real-world healthcare system with coexisting public and private hospitals with distinct characteristics. While public hospitals have lower costs, they also suffer from long waiting times and diminishing patients’ perceived quality of care. Conversely, despite their higher fees, private hospitals offer shorter waiting times, leading to a more favorable perception of quality. A balanced healthcare system could provide societal benefits. Pricing strategies greatly influence a patient’s hospital selection. For instance, reduced fees in private hospitals attract more patients, consequently reducing overcrowding in public facilities and elevating the overall quality of services provided. This study aims to develop pricing models to foster a balanced and socially advantageous healthcare system. This system determines private hospital pricing through contract mechanisms with the government. Thus, we delve into the ramifications of various contract models between the government and private hospitals on social utility. Our findings underscore the communal advantages of contract mechanisms. Furthermore, we generalize the proposed models to apply to similar systems.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"7 ","pages":"Article 100385"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An exploration of machine learning approaches for early Autism Spectrum Disorder detection 机器学习方法在早期自闭症谱系障碍检测中的探索

Healthcare analytics (New York, N.Y.) Pub Date : 2025-06-01 Epub Date: 2025-01-06 DOI: 10.1016/j.health.2024.100379

Nawshin Haque, Tania Islam, Md Erfan

{"title":"An exploration of machine learning approaches for early Autism Spectrum Disorder detection","authors":"Nawshin Haque, Tania Islam, Md Erfan","doi":"10.1016/j.health.2024.100379","DOIUrl":"10.1016/j.health.2024.100379","url":null,"abstract":"<div><div>Autism Spectrum Disorder is a neurodevelopmental condition impacting an individual’s repetitive behaviours, social skills, verbal and nonverbal communication abilities, and capacity for acquiring new knowledge. Manifesting typically in early childhood, specifically between 6 months and 5 years, the symptoms of autism exhibit a progressive nature over time. This study explores the application of Logistic Regression, Support Vector Classifier, K-Nearest Neighbour, Decision Tree, and Random Forest for predicting Autism in children and toddlers by leveraging advancements in machine learning. The efficacy of these techniques is evaluated using publicly accessible datasets specific to both age groups. The findings indicate remarkable performance, with the toddler dataset achieving a mean Intersection over Union (mIoU) of 100<span><math><mtext>%</mtext></math></span> for Support Vector Classifier and 99.80<span><math><mtext>%</mtext></math></span> for Logistic Regression. Similarly, the children dataset demonstrates outstanding results, achieving an mIoU of 100<span><math><mtext>%</mtext></math></span> for Support Vector Classifier and 99.96<span><math><mtext>%</mtext></math></span> for Logistic Regression. Furthermore, all algorithms achieved 100<span><math><mtext>%</mtext></math></span> accuracy on the children (age 4–11) dataset collected from real-world sources. Logistic Regression, Random Forest, Support Vector Classifier, and Decision Tree attained 100<span><math><mtext>%</mtext></math></span> accuracy and mIoU with the real-world dataset. These results underscore the potential of machine learning in aiding the early detection of ASD in children and toddlers, offering promising avenues for future research and clinical applications.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"7 ","pages":"Article 100379"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143169862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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 : 2025-06-01 Epub 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":"2025-06-01","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 automated information extraction model for unstructured discharge letters using large language models and GPT-4 基于大语言模型和GPT-4的非结构化离职信自动信息提取模型

Healthcare analytics (New York, N.Y.) Pub Date : 2025-06-01 Epub Date: 2025-01-10 DOI: 10.1016/j.health.2024.100378

Robert M. Siepmann , Giulia Baldini , Cynthia S. Schmidt , Daniel Truhn , Gustav Anton Müller-Franzes , Amin Dada , Jens Kleesiek , Felix Nensa , René Hosch

{"title":"An automated information extraction model for unstructured discharge letters using large language models and GPT-4","authors":"Robert M. Siepmann , Giulia Baldini , Cynthia S. Schmidt , Daniel Truhn , Gustav Anton Müller-Franzes , Amin Dada , Jens Kleesiek , Felix Nensa , René Hosch","doi":"10.1016/j.health.2024.100378","DOIUrl":"10.1016/j.health.2024.100378","url":null,"abstract":"<div><div>The administrative burden of manually extracting clinical information from discharge letters is a common challenge in healthcare. This study aims to explore the use of Large Language Models (LLMs), specifically Generative Pretrained Transformer 4 (GPT-4) by OpenAI, for automated extraction of diagnoses, medications, and allergies from discharge letters. Data for this study were sourced from two healthcare institutions in Germany, comprising discharge letters for ten patients from each institution. The first experiment is conducted using a standardized prompt for information extraction. However, challenges were encountered, and the prompt was fine-tuned in a second experiment to improve the results. We further tested whether open-source LLMs can achieve similar results. In the first experiment, primary diagnoses were identified with 85% accuracy and secondary diagnoses with 55.8%. Medications and allergies were extracted with 85.9% and 100% accuracy, respectively. The International Classification of Diseases, 10th revision (ICD-10) codes for the identified diagnoses achieved an accuracy of 85% for primary diagnoses and 60.7% for secondary diagnoses. Anatomical Therapeutic Chemical (ATC) codes were identified with an accuracy of 78.8%. On the other hand, open-source LLMs did not provide similar levels of accuracy and could not consistently fill the template. With prompt fine-tuning in the second experiment, the primary diagnoses, secondary diagnoses, and medications could be predicted with 95%, 88.9%, and 92.2% accuracy, respectively. GPT-4 shows excellent potential for automated extraction of crucial diagnostic and medication information from discharge letters, presumably lowering the administrative burden for healthcare professionals and improving patient outcomes.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"7 ","pages":"Article 100378"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143172049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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 : 2025-06-01 Epub 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":"2025-06-01","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

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 : 2025-06-01 Epub 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":"2025-06-01","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

An application of natural language processing for hypoglycemic event identification in patients with diabetes mellitus 自然语言处理在糖尿病患者低血糖事件识别中的应用

Healthcare analytics (New York, N.Y.) Pub Date : 2025-06-01 Epub Date: 2025-01-21 DOI: 10.1016/j.health.2024.100381

J.E. Camacho-Cogollo , Cristhian Felipe Patiño Zambrano , Christian Lochmuller , Claudia C. Colmenares-Mejia , Nicolas Rozo , Mario A. Isaza-Ruget , Paul Rodriguez , Andrés García

{"title":"An application of natural language processing for hypoglycemic event identification in patients with diabetes mellitus","authors":"J.E. Camacho-Cogollo , Cristhian Felipe Patiño Zambrano , Christian Lochmuller , Claudia C. Colmenares-Mejia , Nicolas Rozo , Mario A. Isaza-Ruget , Paul Rodriguez , Andrés García","doi":"10.1016/j.health.2024.100381","DOIUrl":"10.1016/j.health.2024.100381","url":null,"abstract":"<div><div>The therapeutic goal for diabetes mellitus is to maintain normal blood glucose levels, but in some cases, hypoglycemia may occur as a consequence of treatment. Identifying patients with hypoglycemia is critical to preventing adverse events and mortality. However, hypoglycemic events are often not accurately documented in electronic health records (EHRs). This study presents a retrospective analysis of the EHRs of patients with diabetes mellitus. We hypothesize that text analytics and machine learning can identify possible hypoglycemic incidents from unstructured physician notes in electronic health records. Our analysis applies these techniques using the Python programming language as a tool. It also considers words that describe symptoms related to hypoglycemia. The analysis involves searching physicians' notes for keywords and applying supervised classification methods to 146,542 records. Natural language processing (NLP) and machine learning algorithms are used to identify possible hypoglycemic events and related symptoms in physicians’ notes. A multi-layer perceptron (MLP) model produces the best classification performance among all the models tested in this study, with an obtained accuracy of 0.87. We show that the NLP approach can effectively identify and automate the text-based detection process of potential hypoglycemic events, and can subsequently be used to make informed decisions about potential patient risks.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"7 ","pages":"Article 100381"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143172047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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 Epub Date: 2025-05-14 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 exploration of the interplay between treatment and vaccination in an Age-Structured Malaria Model using non-linear ordinary differential equations 利用非线性常微分方程探索年龄结构疟疾模型中治疗和疫苗接种之间的相互作用

Healthcare analytics (New York, N.Y.) Pub Date : 2025-06-01 Epub Date: 2025-02-22 DOI: 10.1016/j.health.2025.100386

Mahmudul Bari Hridoy, Angela Peace

{"title":"An exploration of the interplay between treatment and vaccination in an Age-Structured Malaria Model using non-linear ordinary differential equations","authors":"Mahmudul Bari Hridoy, Angela Peace","doi":"10.1016/j.health.2025.100386","DOIUrl":"10.1016/j.health.2025.100386","url":null,"abstract":"<div><div>Malaria continues to be a significant global health challenge, particularly in tropical regions. Resistance to key antimalarial drugs is spreading, complicating treatment efforts. While progress toward eradication has been slow, the development and introduction of novel malaria vaccines offer hope for reducing the disease burden in endemic areas. To address these challenges, we develop an extended Susceptible–Exposed–Infected–Recovered (SEIR) age-structured model incorporating malaria vaccination for children, drug-sensitive and drug-resistant strains, and interactions between human hosts and mosquitoes. Our research evaluates how malaria vaccination coverage influences disease prevalence and transmission dynamics. We derive both strains’ basic, intervention, and invasion reproduction numbers and conduct sensitivity analysis to identify key parameters affecting infection prevalence. Our findings reveal that model outcomes are primarily influenced by scale factors that reduce transmission and natural recovery rates for the resistant strain, as well as by drug treatment and vaccination efficacies and mosquito death rates. Numerical simulations indicate that while treatment reduces the malaria disease burden, it also increases the proportion of drug-resistant cases. Conversely, higher vaccination efficacy correlates with lower infection cases for both strains. These results suggest that a synergistic approach involving vaccination and treatment could effectively decrease the overall proportion of the infected population.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"7 ","pages":"Article 100386"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A clustering-based federated deep learning approach for enhancing diabetes management with privacy-preserving edge artificial intelligence 一种基于聚类的联合深度学习方法，用于增强糖尿病管理与隐私保护边缘人工智能

Healthcare analytics (New York, N.Y.) Pub Date : 2025-06-01 Epub Date: 2025-04-01 DOI: 10.1016/j.health.2025.100392

Xinyi Yang, Juan Li

{"title":"A clustering-based federated deep learning approach for enhancing diabetes management with privacy-preserving edge artificial intelligence","authors":"Xinyi Yang, Juan Li","doi":"10.1016/j.health.2025.100392","DOIUrl":"10.1016/j.health.2025.100392","url":null,"abstract":"<div><div>The increasing prevalence of diabetes necessitates innovative glucose prediction methods that prioritize patient privacy. While edge artificial intelligence (AI) offers potential, its limitations in resource-constrained devices can be mitigated through federated learning (FL). However, challenges remain in accounting for patient variability and optimizing FL for glucose prediction. This research introduces a novel personalized clustering-based federated deep learning (Clu-FDL) model to address these challenges. We develop tailored models that enhance prediction accuracy by clustering patients based on carbohydrate (CHO) intake patterns. Utilizing Simple Recurrent Neural Network (SimpleRNN) and Gated Recurrent Unit (GRU) methods, the study evaluates the performance of local patients who contribute to training the cluster and global (non-cluster) models. The results show that the Clu-FDL approach achieves high precision (0.93), recall (0.96), and F1 scores (0.95), along with low Root Mean Square Error (RMSE) values (11.08 ± 1.77 mg/dL). Additionally, for new patients with different data durations, analysis based on 0.25–3 days of data indicates that Clu-FDL models exhibit greater stability, with smaller RMSE and higher precision, recall, and F1 scores compared to non-clustering models. The study identifies that SimpleRNN and GRU models are most effective for new patients with 9 and 6 days of data. This privacy-preserving, clustering-based personalized approach empowers patients to manage their diabetes effectively.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"7 ","pages":"Article 100392"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0