Clinical eHealth最新文献_第2页

Acceptance of telehealth in the Kingdom of Saudi Arabia: an application of the UTAUT model 沙特阿拉伯王国接受远程保健：UTAUT模式的应用

Clinical eHealth

Pub Date : 2025-08-18 DOI: 10.1016/j.ceh.2025.08.002

Abdullah A. Almojaibel , Assim M. AlAbdulKader , Mohammad A. Al-Bsheish , Abdulelah M. Aldhahir , Saeed M. Alghamdi , Fatma I. Almaghlouth , Abdullah S. Alqahtani , Jaber S. Alqahtani , Yousef D. Alqurashi , Mohammed E. Alsubaiei , Abdulrahman M. Jabour , Mu’taman K. Jarrar , Jithin K. Sreedharan , Shoug Y. Al Humoud

Introduction

Understanding telehealth users’ acceptance is essential for ensuring effective implementation and may lead to successful, higher quality, and safer telehealth programs. Therefore, this study aimed to measure telehealth acceptance in the population of Saudi Arabia and to explore the associations between sociodemographic variables and intention to use telehealth.

Materials and methods

This study was conducted online from May 1, 2024, to June 30, 2024. Part 1 of the questionnaire collected sociodemographic data. Part 2 employed the Unified Theory of Acceptance and Use of Technology (UTAUT), which includes performance expectancy (PE), effort expectancy (EE), social influence (SF), and facilitating conditions (FC) in addition to the Behavioral Intention (BI) subscale to examine factors influencing telehealth acceptance. The associations between the sociodemographic variables and each construct of the UTAUT and the associations between the sociodemographic variables of participants who agreed for each construct and BI to use telehealth were analyzed using bivariate logistic regression to evaluate predictors.

Results

A total of 2234 participants completed the survey. 95.7 % of the participants were positive about using telehealth. PE was a significant predictor of the intention to use telehealth (p < 0.01). EE was also a significant predictor of the positive intention to use telehealth (p < 0.01). SI significantly predicted telehealth usage (p < 0.01), as did the FC construct (p < 0.01).

Conclusion

Telehealth was highly accepted by the population in KSA. User acceptance of telehealth was influenced by their perception of its benefits, ease of use, social pressure, and the availability of facilitating logistics such as a computer and the internet.

了解远程医疗用户的接受程度对于确保有效实施至关重要，并可能导致成功，更高质量和更安全的远程医疗计划。因此，本研究旨在衡量沙特阿拉伯人口对远程医疗的接受程度，并探讨社会人口统计学变量与使用远程医疗的意愿之间的关系。材料与方法本研究于2024年5月1日至2024年6月30日在线进行。问卷的第一部分收集了社会人口统计数据。第二部分采用技术接受与使用统一理论（UTAUT），其中包括绩效期望（PE）、努力期望（EE）、社会影响（SF）和促进条件（FC）以及行为意向（BI）子量表来研究影响远程医疗接受的因素。使用双变量逻辑回归来评估预测因子，分析了社会人口变量与UTAUT每个结构之间的关联，以及同意每个结构和BI使用远程医疗的参与者的社会人口变量之间的关联。结果共2234人完成调查。95.7%的参与者对使用远程医疗持积极态度。PE是使用远程医疗意愿的显著预测因子（p < 0.01）。情感表达也是使用远程医疗的积极意向的显著预测因子（p < 0.01）。SI显著地预测了远程医疗的使用（p < 0.01）， FC结构也是如此（p < 0.01）。结论远程医疗在沙特阿拉伯地区的接受程度较高。用户对远程保健的好处、易用性、社会压力以及计算机和互联网等便利物流的可用性的看法影响了他们对远程保健的接受程度。

{"title":"Acceptance of telehealth in the Kingdom of Saudi Arabia: an application of the UTAUT model","authors":"Abdullah A. Almojaibel , Assim M. AlAbdulKader , Mohammad A. Al-Bsheish , Abdulelah M. Aldhahir , Saeed M. Alghamdi , Fatma I. Almaghlouth , Abdullah S. Alqahtani , Jaber S. Alqahtani , Yousef D. Alqurashi , Mohammed E. Alsubaiei , Abdulrahman M. Jabour , Mu’taman K. Jarrar , Jithin K. Sreedharan , Shoug Y. Al Humoud","doi":"10.1016/j.ceh.2025.08.002","DOIUrl":"10.1016/j.ceh.2025.08.002","url":null,"abstract":"<div><h3>Introduction</h3><div>Understanding telehealth users’ acceptance is essential for ensuring effective implementation and may lead to successful, higher quality, and safer telehealth programs. Therefore, this study aimed to measure telehealth acceptance in the population of Saudi Arabia and to explore the associations between sociodemographic variables and intention to use telehealth.</div></div><div><h3>Materials and methods</h3><div>This study was conducted online from May 1, 2024, to June 30, 2024. Part 1 of the questionnaire collected sociodemographic data. Part 2 employed the Unified Theory of Acceptance and Use of Technology (UTAUT), which includes performance expectancy (PE), effort expectancy (EE), social influence (SF), and facilitating conditions (FC) in addition to the Behavioral Intention (BI) subscale to examine factors influencing telehealth acceptance. The associations between the sociodemographic variables and each construct of the UTAUT and the associations between the sociodemographic variables of participants who agreed for each construct and BI to use telehealth were analyzed using bivariate logistic regression to evaluate predictors.</div></div><div><h3>Results</h3><div>A total of 2234 participants completed the survey. 95.7 % of the participants were positive about using telehealth. PE was a significant predictor of the intention to use telehealth (p < 0.01). EE was also a significant predictor of the positive intention to use telehealth (p < 0.01). SI significantly predicted telehealth usage (p < 0.01), as did the FC construct (p < 0.01).</div></div><div><h3>Conclusion</h3><div>Telehealth was highly accepted by the population in KSA. User acceptance of telehealth was influenced by their perception of its benefits, ease of use, social pressure, and the availability of facilitating logistics such as a computer and the internet.</div></div>","PeriodicalId":100268,"journal":{"name":"Clinical eHealth","volume":"8 ","pages":"Pages 162-174"},"PeriodicalIF":0.0,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144890251","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

Integrated feature selection-based stacking ensemble model using optimized hyperparameters to predict breast cancer with smart web application 基于优化超参数的基于特征选择的叠加集成模型与智能web应用预测乳腺癌

Clinical eHealth

Pub Date : 2025-08-07 DOI: 10.1016/j.ceh.2025.08.001

Rajib Kumar Halder, Marzana Akter Lima, Mohammed Nasir Uddin, Md.Aminul Islam, Adri Saha

Breast cancer is a leading cause of morbidity and mortality among women worldwide, arising from malignant cell transformations in breast tissue. Early detection is paramount as it significantly improves survival rates and reduces the complexity and cost of treatment. Machine learning has revolutionized this field, providing more precise, efficient, and personalized diagnostic methods. Our research aims to develop a robust predictive model for breast cancer classification through rigorous preprocessing, diverse feature selection techniques, and advanced ensemble learning strategies. A central component of our methodology is the employment of a Stacking Classifier integrated with multiple base classifiers, optimized using RandomizedSearchCV to fine-tune hyperparameters. This process enhances the model’s accuracy, reliability, and generalizability. Significantly, our feature selection process involves three methodologies: filter, wrapper, and embedded methods. By applying these techniques, we identify the most critical features that are consistently selected across all methods. These features are then used to train the model, ensuring that our approach focuses on the most relevant data points for breast cancer classification. Utilizing the Wisconsin Breast Cancer Dataset from the UCI repository, which comprises 569 patient records, our model demonstrates exceptional performance. It achieves a perfect accuracy of 100% and an AUC-ROC of 1.00, indicating flawless sensitivity and specificity. The proposed framework was evaluated using two distinct datasets: the Wisconsin Prognostic Breast Cancer (WPBC) dataset and the Wisconsin Original Breast Cancer (WOBC) dataset. This model stands out for its potential to significantly enhance early detection and treatment strategies, marking a significant advance in applying machine learning to improve healthcare outcomes. Additionally, we have developed a user-friendly web app for breast cancer detection using our predictive model.

乳腺癌是全世界妇女发病和死亡的主要原因，由乳腺组织中的恶性细胞转化引起。早期发现是至关重要的，因为它可以显著提高生存率，降低治疗的复杂性和成本。机器学习彻底改变了这一领域，提供了更精确、高效和个性化的诊断方法。我们的研究旨在通过严格的预处理、多样化的特征选择技术和先进的集成学习策略，建立一个强大的乳腺癌分类预测模型。我们方法的一个核心组成部分是使用与多个基本分类器集成的堆叠分类器，使用RandomizedSearchCV进行优化以微调超参数。这一过程提高了模型的准确性、可靠性和通用性。值得注意的是，我们的特征选择过程涉及三种方法：过滤器、包装器和嵌入方法。通过应用这些技术，我们确定了在所有方法中一致选择的最关键的特征。然后使用这些特征来训练模型，确保我们的方法专注于与乳腺癌分类最相关的数据点。利用UCI存储库中的威斯康星乳腺癌数据集，其中包括569例患者记录，我们的模型展示了卓越的性能。它达到100%的完美准确度和1.00的AUC-ROC，表明完美的灵敏度和特异性。该框架使用两个不同的数据集进行评估：威斯康星州预后乳腺癌（WPBC）数据集和威斯康星州原始乳腺癌（WOBC）数据集。该模型因其显著增强早期检测和治疗策略的潜力而脱颖而出，标志着应用机器学习改善医疗保健结果的重大进步。此外，我们还开发了一个用户友好的web应用程序，用于使用我们的预测模型进行乳腺癌检测。

{"title":"Integrated feature selection-based stacking ensemble model using optimized hyperparameters to predict breast cancer with smart web application","authors":"Rajib Kumar Halder, Marzana Akter Lima, Mohammed Nasir Uddin, Md.Aminul Islam, Adri Saha","doi":"10.1016/j.ceh.2025.08.001","DOIUrl":"10.1016/j.ceh.2025.08.001","url":null,"abstract":"<div><div>Breast cancer is a leading cause of morbidity and mortality among women worldwide, arising from malignant cell transformations in breast tissue. Early detection is paramount as it significantly improves survival rates and reduces the complexity and cost of treatment. Machine learning has revolutionized this field, providing more precise, efficient, and personalized diagnostic methods. Our research aims to develop a robust predictive model for breast cancer classification through rigorous preprocessing, diverse feature selection techniques, and advanced ensemble learning strategies. A central component of our methodology is the employment of a Stacking Classifier integrated with multiple base classifiers, optimized using RandomizedSearchCV to fine-tune hyperparameters. This process enhances the model’s accuracy, reliability, and generalizability. Significantly, our feature selection process involves three methodologies: filter, wrapper, and embedded methods. By applying these techniques, we identify the most critical features that are consistently selected across all methods. These features are then used to train the model, ensuring that our approach focuses on the most relevant data points for breast cancer classification. Utilizing the Wisconsin Breast Cancer Dataset from the UCI repository, which comprises 569 patient records, our model demonstrates exceptional performance. It achieves a perfect accuracy of 100% and an AUC-ROC of 1.00, indicating flawless sensitivity and specificity. The proposed framework was evaluated using two distinct datasets: the Wisconsin Prognostic Breast Cancer (WPBC) dataset and the Wisconsin Original Breast Cancer (WOBC) dataset. This model stands out for its potential to significantly enhance early detection and treatment strategies, marking a significant advance in applying machine learning to improve healthcare outcomes. Additionally, we have developed a user-friendly web app for breast cancer detection using our predictive model.</div></div>","PeriodicalId":100268,"journal":{"name":"Clinical eHealth","volume":"8 ","pages":"Pages 146-161"},"PeriodicalIF":0.0,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144828913","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

Enhanced epilepsy detection using discrete wavelet transform and bandpass filtering on EEG data: integration of ART-based and LVQ models 基于脑电数据的离散小波变换和带通滤波增强癫痫检测：基于art和LVQ模型的集成

Clinical eHealth

Pub Date : 2025-07-18 DOI: 10.1016/j.ceh.2025.07.001

Seyed Matin Malakouti

Accurate detection of epileptic seizures from EEG signals is vital for early diagnosis and treatment of epilepsy. However, EEG signals are inherently nonstationary and noisy, posing significant challenges for classification. This study presents a lightweight and interpretable framework for epileptic seizure detection, combining Discrete Wavelet Transform (DWT) and bandpass filtering for robust time–frequency feature extraction. We evaluate multiple adaptive classifiers, including Adaptive Resonance Theory (ART1, ARTMAP) and Learning Vector Quantization (LVQ), enhanced through grid search and ensemble learning.

Experiments were conducted using the Bonn EEG dataset, focusing on classifying interictal and ictal EEG signals. Among the evaluated models, ARTMAP with preprocessing and ensemble techniques achieved the best performance (AUC = 0.85), followed by ART1 with grid search and ensemble (AUC = 0.81). These results demonstrate that interpretable, adaptive models can offer competitive performance in EEG classification, while maintaining computational efficiency compared to deep learning methods.

The proposed method provides a practical and transparent solution for EEG-based seizure detection, especially suited for real-time clinical applications and resource-constrained environments.

从脑电图信号中准确检测癫痫发作对于癫痫的早期诊断和治疗至关重要。然而，脑电信号具有固有的非平稳和噪声，给分类带来了重大挑战。本研究提出了一种轻量级且可解释的癫痫发作检测框架，结合离散小波变换（DWT）和带通滤波进行鲁棒时频特征提取。我们评估了多种自适应分类器，包括自适应共振理论（ART1， ARTMAP）和学习向量量化（LVQ），通过网格搜索和集成学习增强。使用波恩EEG数据集进行实验，重点对间歇期和间歇期EEG信号进行分类。在评价的模型中，采用预处理和集成技术的ARTMAP模型表现最佳（AUC = 0.85），其次是采用网格搜索和集成技术的ART1模型（AUC = 0.81）。这些结果表明，与深度学习方法相比，可解释的、自适应的模型在保持计算效率的同时，可以在EEG分类中提供有竞争力的性能。该方法为基于脑电图的癫痫发作检测提供了一种实用、透明的解决方案，特别适合于实时临床应用和资源受限的环境。

{"title":"Enhanced epilepsy detection using discrete wavelet transform and bandpass filtering on EEG data: integration of ART-based and LVQ models","authors":"Seyed Matin Malakouti","doi":"10.1016/j.ceh.2025.07.001","DOIUrl":"10.1016/j.ceh.2025.07.001","url":null,"abstract":"<div><div>Accurate detection of epileptic seizures from EEG signals is vital for early diagnosis and treatment of epilepsy. However, EEG signals are inherently nonstationary and noisy, posing significant challenges for classification. This study presents a lightweight and interpretable framework for epileptic seizure detection, combining Discrete Wavelet Transform (DWT) and bandpass filtering for robust time–frequency feature extraction. We evaluate multiple adaptive classifiers, including Adaptive Resonance Theory (ART1, ARTMAP) and Learning Vector Quantization (LVQ), enhanced through grid search and ensemble learning.</div><div>Experiments were conducted using the Bonn EEG dataset, focusing on classifying interictal and ictal EEG signals. Among the evaluated models, ARTMAP with preprocessing and ensemble techniques achieved the best performance (AUC = 0.85), followed by ART1 with grid search and ensemble (AUC = 0.81). These results demonstrate that interpretable, adaptive models can offer competitive performance in EEG classification, while maintaining computational efficiency compared to deep learning methods.</div><div>The proposed method provides a practical and transparent solution for EEG-based seizure detection, especially suited for real-time clinical applications and resource-constrained environments.</div></div>","PeriodicalId":100268,"journal":{"name":"Clinical eHealth","volume":"8 ","pages":"Pages 134-145"},"PeriodicalIF":0.0,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144827592","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

Usability evaluation of wearable technology: A pilot study on a smart diabetic shoe for foot care 可穿戴技术的可用性评估：用于足部护理的智能糖尿病鞋的试点研究

Clinical eHealth

Pub Date : 2025-04-28 DOI: 10.1016/j.ceh.2025.04.004

Khadijeh Moulaei , Abbas Sheikhtaheri

Introduction

Smart diabetic shoes can be essential in preventing and monitoring foot ulcers. We developed a smart diabetic shoe to monitor pressure, temperature, and humidity and send the data to patients’ phones via Bluetooth for foot care. This study aimed to evaluate the usability of this smart diabetic shoe.

Methods

Seven patients were interviewed using a semi-structured interview. They were asked to use the shoes and application in different positions and then express their opinions.

Results

We identified a total number of 35 unique usability problems and recommendations. Hardware and software were responsible for 8 and 27 of them, respectively. The majority of the issues concerned the application. The most common software-related complaints raised by the participants were warning presentation, application appearance, and customization. Participants highlighted foot comfort as the most important concern among hardware-related issues.

Conclusion

By addressing various hardware and software issues—such as foot comfort, shoe design and layout, system performance, data collection, remote monitoring, and communication with healthcare providers—we can enhance the usability and overall experience of wearable devices for users.

智能糖尿病鞋在预防和监测足部溃疡方面是必不可少的。我们开发了一款智能糖尿病鞋，可以监测压力、温度和湿度，并通过蓝牙将数据发送到患者的手机，用于足部护理。本研究旨在评估这种智能糖尿病鞋的可用性。方法采用半结构化访谈法对7例患者进行访谈。他们被要求在不同的位置使用鞋子和应用程序，然后表达他们的意见。结果我们确定了35个独特的可用性问题和建议。硬件和软件分别负责其中的8个和27个。大多数问题与应用程序有关。参与者提出的最常见的与软件相关的抱怨是警告表示、应用程序外观和定制。与会者强调，在硬件相关问题中，脚的舒适度是最重要的问题。结论通过解决足部舒适度、鞋型设计与布局、系统性能、数据采集、远程监控以及与医疗服务提供者的沟通等软硬件问题，可以提高可穿戴设备的易用性和用户的整体体验。

{"title":"Usability evaluation of wearable technology: A pilot study on a smart diabetic shoe for foot care","authors":"Khadijeh Moulaei , Abbas Sheikhtaheri","doi":"10.1016/j.ceh.2025.04.004","DOIUrl":"10.1016/j.ceh.2025.04.004","url":null,"abstract":"<div><h3>Introduction</h3><div>Smart diabetic shoes can be essential in preventing and monitoring foot ulcers. We developed a smart diabetic shoe to monitor pressure, temperature, and humidity and send the data to patients’ phones via Bluetooth for foot care. This study aimed to evaluate the usability of this smart diabetic shoe.</div></div><div><h3>Methods</h3><div>Seven patients were interviewed using a semi-structured interview. They were asked to use the shoes and application in different positions and then express their opinions.</div></div><div><h3>Results</h3><div>We identified a total number of 35 unique usability problems and recommendations. Hardware and software were responsible for 8 and 27 of them, respectively. The majority of the issues concerned the application. The most common software-related complaints raised by the participants were warning presentation, application appearance, and customization. Participants highlighted foot comfort as the most important concern among hardware-related issues.</div></div><div><h3>Conclusion</h3><div>By addressing various hardware and software issues—such as foot comfort, shoe design and layout, system performance, data collection, remote monitoring, and communication with healthcare providers—we can enhance the usability and overall experience of wearable devices for users.</div></div>","PeriodicalId":100268,"journal":{"name":"Clinical eHealth","volume":"8 ","pages":"Pages 94-102"},"PeriodicalIF":0.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143885978","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

The smart and healthy city business model Canvas—A post Covid-19 resilience for smart city business modeling framework 智慧健康城市商业模式canvas—后疫情韧性智慧城市商业建模框架

Clinical eHealth

Pub Date : 2025-04-19 DOI: 10.1016/j.ceh.2025.04.003

Siti Jahroh , Dikky Indrawan , Z.B. Junaid , Asaduddin Abdullah , Idqan Fahmi , Muhammad Siddique

Cities must adopt clever solutions to address the health issues brought on by the COVID-19 pandemic and challenging population growth, as well as to meet the economic, social, and environmental concerns brought on by continued urbanization. This study aimed to analyze the city operational design and create a framework for a smart city, considering the health issue. It would be a useful instrument as well as to assist the government in the cities in analyzing the changing aspects of the Business Model Canvas (BMC) and altering the existing BMC elements that operationalize the new dimensions of the smart and healthy city. A framework in order to create and disseminate a further comprehensive and cohesive picture of a smart and healthy city operational design is offered by a modified BMC proposal, the so-called BMC of smart and healthy city (SHC). The new elements proposed in this paper are preventive measures and therapeutic urbanism as well as health cost and benefits based on the smart city BMC. Furthermore, it encourages innovative creation and more lasting value. With regard to SDG11, the smart and healthy city BMC provides a platform for connecting sustainable value creation in developing a city operational design and innovation in the smart and healthy cities. This article provides guidelines for modernization of urbanization as per directives of SDG11 of the United Nations.

城市必须采取明智的解决方案，应对COVID-19大流行和具有挑战性的人口增长带来的健康问题，并应对持续城市化带来的经济、社会和环境问题。本研究旨在分析城市运营设计，并在考虑健康问题的情况下创建一个智慧城市的框架。这将是一个有用的工具，也有助于城市政府分析商业模式画布（BMC）的变化方面，并改变现有的BMC元素，以实现智能和健康城市的新维度。通过修改BMC提案，即所谓的智慧健康城市BMC (SHC)，提供了一个框架，以创建和传播一个更全面、更有凝聚力的智慧健康城市运营设计。本文提出的新要素是基于智慧城市BMC的预防措施和治疗性城市主义以及健康成本和效益。此外，它鼓励创新创造和更持久的价值。在可持续发展目标11方面，智慧健康城市BMC提供了一个平台，将可持续价值创造与智慧健康城市的运营设计和创新联系起来。本文根据联合国可持续发展目标11的指示，提供了城市化现代化的指导方针。

{"title":"The smart and healthy city business model Canvas—A post Covid-19 resilience for smart city business modeling framework","authors":"Siti Jahroh , Dikky Indrawan , Z.B. Junaid , Asaduddin Abdullah , Idqan Fahmi , Muhammad Siddique","doi":"10.1016/j.ceh.2025.04.003","DOIUrl":"10.1016/j.ceh.2025.04.003","url":null,"abstract":"<div><div>Cities must adopt clever solutions to address the health issues brought on by the COVID-19 pandemic and challenging population growth, as well as to meet the economic, social, and environmental concerns brought on by continued urbanization. This study aimed to analyze the city operational design and create a framework for a smart city, considering the health issue. It would be a useful instrument as well as to assist the government in the cities in analyzing the changing aspects of the Business Model Canvas (BMC) and altering the existing BMC elements that operationalize the new dimensions of the smart and healthy city. A framework in order to create and disseminate a further comprehensive and cohesive picture of a smart and healthy city operational design is offered by a modified BMC proposal, the so-called BMC of smart and healthy city (SHC). The new elements proposed in this paper are preventive measures and therapeutic urbanism as well as health cost and benefits based on the smart city BMC. Furthermore, it encourages innovative creation and more lasting value. With regard to SDG11, the smart and healthy city BMC provides a platform for connecting sustainable value creation in developing a city operational design and innovation in the smart and healthy cities. This article provides guidelines for modernization of urbanization as per directives of SDG11 of the United Nations.</div></div>","PeriodicalId":100268,"journal":{"name":"Clinical eHealth","volume":"8 ","pages":"Pages 78-93"},"PeriodicalIF":0.0,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143882422","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

Securing electronic health records using blockchain-enabled federated learning for IoT-based smart healthcare 为基于物联网的智能医疗保健使用支持区块链的联邦学习来保护电子健康记录

Clinical eHealth

Pub Date : 2025-04-17 DOI: 10.1016/j.ceh.2025.04.002

A. Althaf Ali , M.A. Gunavathie , V. Srinivasan , M. Aruna , R. Chennappan , M. Matheena

The integration of smart city applications with healthcare has revolutionized patient monitoring and medical data management. However, ensuring the privacy and security of Electronic Health Records (EHR) remains a critical challenge, especially in IoT-based environments with resource-constrained devices. This paper proposes a novel Blockchain-Enabled Federated Learning (BFL) framework to enhance privacy preservation in EHR processing. The proposed framework leverages zero-knowledge proofs (ZKP) for authentication and homomorphic encryption for secure computation, ensuring robust data security without exposing raw patient data. Federated Learning (FL) enables decentralized model training across IoT devices, reducing privacy risks while maintaining data utility. Additionally, blockchain technology enhances the integrity and transparency of EHR transactions by creating a tamper-proof ledger. The performance of the proposed BFL framework is evaluated based on data utility, model accuracy, execution time, and scalability across varying sizes of EHR datasets. Results demonstrate improved privacy preservation, reduced computational overhead, and enhanced model efficiency, making it a promising approach for secure and privacy-aware IoT-based smart healthcare systems.

智能城市应用程序与医疗保健的集成彻底改变了患者监控和医疗数据管理。然而，确保电子健康记录（EHR）的隐私性和安全性仍然是一个严峻的挑战，特别是在基于物联网的环境中，设备资源受限。本文提出了一种新的基于区块链的联邦学习（BFL）框架，以增强电子病历处理中的隐私保护。该框架利用零知识证明（ZKP）进行身份验证，利用同态加密进行安全计算，在不暴露原始患者数据的情况下确保强大的数据安全性。联邦学习（FL）支持跨物联网设备的分散模型训练，在保持数据效用的同时降低隐私风险。此外，区块链技术通过创建防篡改分类帐来增强EHR交易的完整性和透明度。所提出的BFL框架的性能基于数据效用、模型准确性、执行时间和跨不同大小的EHR数据集的可扩展性进行评估。结果表明，该方法改善了隐私保护，减少了计算开销，提高了模型效率，使其成为一种有前景的安全且具有隐私意识的基于物联网的智能医疗保健系统方法。

{"title":"Securing electronic health records using blockchain-enabled federated learning for IoT-based smart healthcare","authors":"A. Althaf Ali , M.A. Gunavathie , V. Srinivasan , M. Aruna , R. Chennappan , M. Matheena","doi":"10.1016/j.ceh.2025.04.002","DOIUrl":"10.1016/j.ceh.2025.04.002","url":null,"abstract":"<div><div>The integration of smart city applications with healthcare has revolutionized patient monitoring and medical data management. However, ensuring the privacy and security of Electronic Health Records (EHR) remains a critical challenge, especially in IoT-based environments with resource-constrained devices. This paper proposes a novel Blockchain-Enabled Federated Learning (BFL) framework to enhance privacy preservation in EHR processing. The proposed framework leverages zero-knowledge proofs (ZKP) for authentication and homomorphic encryption for secure computation, ensuring robust data security without exposing raw patient data. Federated Learning (FL) enables decentralized model training across IoT devices, reducing privacy risks while maintaining data utility. Additionally, blockchain technology enhances the integrity and transparency of EHR transactions by creating a tamper-proof ledger. The performance of the proposed BFL framework is evaluated based on data utility, model accuracy, execution time, and scalability across varying sizes of EHR datasets. Results demonstrate improved privacy preservation, reduced computational overhead, and enhanced model efficiency, making it a promising approach for secure and privacy-aware IoT-based smart healthcare systems.</div></div>","PeriodicalId":100268,"journal":{"name":"Clinical eHealth","volume":"8 ","pages":"Pages 125-133"},"PeriodicalIF":0.0,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144563801","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

Significance of GPT-enabled LDCT lung cancer screening gpt辅助下LDCT肺癌筛查的意义

Clinical eHealth

Pub Date : 2025-04-15 DOI: 10.1016/j.ceh.2025.04.001

Shixiong Yang , Qiong Liang , Weipeng Jiang , Chunxue Bai

Lung cancer is a major global health threat, with China experiencing a high incidence and mortality rate and a particularly low five-year survival rate. While lung cancer screening is crucial for improving early diagnosis and survival rates, it faces multiple challenges in China, including public awareness, limited medical resources, high costs, follow-up management, technical capabilities, coverage, and policy funding. The rapid development of generative pretrained transformer (GPT) technology presents new opportunities for lung cancer screening. It can enhance health education, optimize resource allocation, reduce costs, improve coverage, strengthen follow-up management, and advance technical capabilities. Furthermore, it can help improve policy and financial support while fostering collaboration among the government, medical institutions, and various sectors of society to overcome these obstacles. This collaboration would facilitate early diagnosis and treatment of lung cancer, ultimately reducing the mortality rate. However, several challenges remain in the practical application of these technologies, including the need for technological innovation, policy support, and ethical considerations. Multidisciplinary cooperation is needed to overcome these challenges.

肺癌是全球主要的健康威胁，中国的发病率和死亡率都很高，五年生存率特别低。尽管肺癌筛查对于提高早期诊断和生存率至关重要，但它在中国面临着多重挑战，包括公众意识、有限的医疗资源、高昂的成本、后续管理、技术能力、覆盖范围和政策资金。生成预训练变压器（GPT）技术的快速发展为肺癌筛查提供了新的机遇。它可以加强健康教育，优化资源配置，降低成本，提高覆盖率，加强后续管理，提高技术能力。此外，它可以帮助改善政策和财政支持，同时促进政府、医疗机构和社会各部门之间的合作，以克服这些障碍。这种合作将有助于肺癌的早期诊断和治疗，最终降低死亡率。然而，在这些技术的实际应用中仍然存在一些挑战，包括需要技术创新、政策支持和伦理考虑。需要多学科合作来克服这些挑战。

{"title":"Significance of GPT-enabled LDCT lung cancer screening","authors":"Shixiong Yang , Qiong Liang , Weipeng Jiang , Chunxue Bai","doi":"10.1016/j.ceh.2025.04.001","DOIUrl":"10.1016/j.ceh.2025.04.001","url":null,"abstract":"<div><div>Lung cancer is a major global health threat, with China experiencing a high incidence and mortality rate and a particularly low five-year survival rate. While lung cancer screening is crucial for improving early diagnosis and survival rates, it faces multiple challenges in China, including public awareness, limited medical resources, high costs, follow-up management, technical capabilities, coverage, and policy funding. The rapid development of generative pretrained transformer (GPT) technology presents new opportunities for lung cancer screening. It can enhance health education, optimize resource allocation, reduce costs, improve coverage, strengthen follow-up management, and advance technical capabilities. Furthermore, it can help improve policy and financial support while fostering collaboration among the government, medical institutions, and various sectors of society to overcome these obstacles. This collaboration would facilitate early diagnosis and treatment of lung cancer, ultimately reducing the mortality rate. However, several challenges remain in the practical application of these technologies, including the need for technological innovation, policy support, and ethical considerations. Multidisciplinary cooperation is needed to overcome these challenges.</div></div>","PeriodicalId":100268,"journal":{"name":"Clinical eHealth","volume":"8 ","pages":"Pages 117-124"},"PeriodicalIF":0.0,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221989","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

Sexual content in Australian crisis telehealth 澳大利亚危机远程医疗中的性内容

Clinical eHealth

Pub Date : 2025-04-05 DOI: 10.1016/j.ceh.2025.03.003

Bridie Allan, PJ Matt Tilley, Jacqueline Hendriks

An increasing number of contacts to Australian crisis helplines include issues related to sexual wellbeing. This research is an exploratory investigation into the diversity of sexual content received by crisis telehealth services, and equally, clinicians were asked to reflect upon their level of comfort and competence to support these presentations. Twelve Australian-based crisis telehealth clinicians participated in a semi-structured interview. Interviews were transcribed verbatim and analysed via thematic analysis. Four primary themes were evident: (1) clinician experience of telehealth, (2) the impact of disingenuous calls, (3) factors influencing clinician comfort to address sexological presentations, and (4) factors influencing clinician competence to address sexological presentations. Findings highlighted that crisis telehealth clinicians hold clinical responsibility for a diverse range of presentations of a sexual nature. These research findings have strong implications for ongoing workforce development and clinician wellbeing, as participants were largely self- reliant in developing professional comfort and competence to support sexual content. Due to the increasing prevalence of clients experiencing concerns of a sexual nature, it is critical that service provisions and overall quality of interventions account for the breadth of sexual presentations.

越来越多的澳大利亚危机求助热线包括与性健康有关的问题。本研究是对危机远程医疗服务收到的性内容多样性的探索性调查，同样，临床医生被要求反映他们的舒适程度和能力，以支持这些陈述。12位澳大利亚危机远程医疗临床医生参加了一次半结构化访谈。采访内容逐字记录，并通过专题分析进行分析。四个主要主题是显而易见的：(1)临床医生的远程医疗经验，(2)虚假电话的影响，(3)影响临床医生处理性学陈述舒适度的因素，以及(4)影响临床医生处理性学陈述能力的因素。调查结果强调，危机远程保健临床医生对各种各样的性表现负有临床责任。这些研究结果对正在进行的劳动力发展和临床医生的健康有很强的影响，因为参与者在很大程度上是自力更生的，在发展专业舒适和能力来支持性内容。由于越来越多的客户经历了性方面的担忧，服务提供和干预的总体质量对性表现的广度至关重要。

{"title":"Sexual content in Australian crisis telehealth","authors":"Bridie Allan, PJ Matt Tilley, Jacqueline Hendriks","doi":"10.1016/j.ceh.2025.03.003","DOIUrl":"10.1016/j.ceh.2025.03.003","url":null,"abstract":"<div><div>An increasing number of contacts to Australian crisis helplines include issues related to sexual wellbeing. This research is an exploratory investigation into the diversity of sexual content received by crisis telehealth services, and equally, clinicians were asked to reflect upon their level of comfort and competence to support these presentations. Twelve Australian-based crisis telehealth clinicians participated in a semi-structured interview. Interviews were transcribed verbatim and analysed via thematic analysis. Four primary themes were evident: (1) <em>clinician experience of telehealth</em>, (2) <em>the impact of disingenuous calls,</em> (3) <em>factors influencing clinician comfort to address sexological presentations,</em> and (4) <em>factors influencing clinician competence to address sexological presentations</em>. Findings highlighted that crisis telehealth clinicians hold clinical responsibility for a diverse range of presentations of a sexual nature. These research findings have strong implications for ongoing workforce development and clinician wellbeing, as participants were largely self- reliant in developing professional comfort and competence to support sexual content. Due to the increasing prevalence of clients experiencing concerns of a sexual nature, it is critical that service provisions and overall quality of interventions account for the breadth of sexual presentations.</div></div>","PeriodicalId":100268,"journal":{"name":"Clinical eHealth","volume":"8 ","pages":"Pages 103-116"},"PeriodicalIF":0.0,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144068959","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

Personalizing nutrition and recipe recommendation using attention mechanism with an ensemble model 基于集成模型的注意机制个性化营养和食谱推荐

Clinical eHealth

Pub Date : 2025-04-04 DOI: 10.1016/j.ceh.2025.03.002

Shilpa Chaudhari , Archana Rane , Amala Rashmi Kumar

Nutrient management in the context of this proposed work aims to quantize the consumption of essential nutrients in an efficient format such that it leads to a healthy and balanced lifestyle. This paper presents an intelligent nutrition management and recipe recommendation system tailored to individuals’ nutritional profiles, using an ensemble model augmented by an attention mechanism. The system quantifies user nutritional deficiencies based on blood analysis and personal preferences, generating personalized food and recipe suggestions to address these gaps. By integrating multiple supervised learning algorithms such as Random Forest, XGBoost, and MLP, the model dynamically prioritizes nutrients relevant to the user’s needs. Leveraging data from the National Institute of Nutrition, recipes are recommended in video format, aiming to enhance users’ health and dietary habits. The proposed model outperforms baseline systems in detecting nutritional deficiencies and offers efficient, personalized recipe recommendations through a user-friendly web and mobile interface.

在这项拟议工作的背景下，营养管理旨在以有效的形式量化必需营养素的消耗，从而导致健康和平衡的生活方式。本文提出了一种基于关注机制的集成模型，针对个体的营养状况设计了智能营养管理和食谱推荐系统。该系统根据血液分析和个人偏好量化用户的营养缺乏症，生成个性化的食物和食谱建议，以解决这些差距。通过集成多种监督学习算法，如随机森林、XGBoost和MLP，该模型动态地优先考虑与用户需求相关的营养成分。利用美国国家营养研究所的数据，以视频形式推荐食谱，旨在增强用户的健康和饮食习惯。该模型在检测营养缺乏症方面优于基线系统，并通过用户友好的网络和移动界面提供高效、个性化的食谱建议。

引用次数: 0

An interpretable machine learning model to predict hospitalizations 一个可解释的机器学习模型来预测住院情况

Clinical eHealth

Pub Date : 2025-04-04 DOI: 10.1016/j.ceh.2025.03.004

Hagar Elbatanouny , Hissam Tawfik , Tarek Khater , Anatoliy Gorbenko

Hospital management plays a pivotal role in ensuring the efficient delivery of medical services, especially in the face of challenges posed by pandemics such as COVID-19. This paper explores the application of machine learning techniques in addressing the challenge of hospitalization during pandemics. Leveraging a comprehensive dataset sourced from the Mexican government, various supervised learning algorithms including Random Forest, Gradient Boosting, Support Vector Machine, K-Nearest Neighbors, and Multilayer Perceptron are trained and evaluated to discern factors contributing to hospitalizations. Feature importance analysis and dimensionality reduction techniques are employed to enhance models predictive performance. The best model was Gradient Boosting algorithm with an accuracy of 85.63% and AUC score of 0.8696. The interpretability plots showed that pneumonia had a positive impact on the hospitalization prediction of the model. Our analysis indicates that women aged over 45 with pneumonia and concurrent COVID-19 exhibit the highest likelihood of hospitalization. This study underscores the potential of interpretable machine learning in aiding hospital managers to optimize resource allocation, hospitalization cases, and make data-driven decisions during pandemics.

医院管理在确保高效提供医疗服务方面发挥着关键作用，特别是在面临COVID-19等大流行病带来的挑战时。本文探讨了机器学习技术在应对流行病期间住院治疗挑战中的应用。利用来自墨西哥政府的综合数据集，对各种监督学习算法（包括随机森林、梯度增强、支持向量机、k近邻和多层感知器）进行了训练和评估，以识别导致住院的因素。采用特征重要性分析和降维技术来提高模型的预测性能。最佳模型为Gradient Boosting算法，准确率为85.63%，AUC得分为0.8696。可解释性图显示肺炎对模型的住院预测有正向影响。我们的分析表明，45岁以上的女性肺炎和COVID-19合并住院的可能性最高。这项研究强调了可解释机器学习在帮助医院管理人员优化资源分配、住院病例和在大流行期间做出数据驱动决策方面的潜力。

{"title":"An interpretable machine learning model to predict hospitalizations","authors":"Hagar Elbatanouny , Hissam Tawfik , Tarek Khater , Anatoliy Gorbenko","doi":"10.1016/j.ceh.2025.03.004","DOIUrl":"10.1016/j.ceh.2025.03.004","url":null,"abstract":"<div><div>Hospital management plays a pivotal role in ensuring the efficient delivery of medical services, especially in the face of challenges posed by pandemics such as COVID-19. This paper explores the application of machine learning techniques in addressing the challenge of hospitalization during pandemics. Leveraging a comprehensive dataset sourced from the Mexican government, various supervised learning algorithms including Random Forest, Gradient Boosting, Support Vector Machine, K-Nearest Neighbors, and Multilayer Perceptron are trained and evaluated to discern factors contributing to hospitalizations. Feature importance analysis and dimensionality reduction techniques are employed to enhance models predictive performance. The best model was Gradient Boosting algorithm with an accuracy of 85.63% and AUC score of 0.8696. The interpretability plots showed that pneumonia had a positive impact on the hospitalization prediction of the model. Our analysis indicates that women aged over 45 with pneumonia and concurrent COVID-19 exhibit the highest likelihood of hospitalization. This study underscores the potential of interpretable machine learning in aiding hospital managers to optimize resource allocation, hospitalization cases, and make data-driven decisions during pandemics.</div></div>","PeriodicalId":100268,"journal":{"name":"Clinical eHealth","volume":"8 ","pages":"Pages 53-65"},"PeriodicalIF":0.0,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143799502","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