Clinical eHealth最新文献

{"title":"The rise of AI in healthcare: Are chatbots ready to lead?","authors":"Ahmed Yaseen Alqutaibi , Anas Saeed AL-Zaghruri","doi":"10.1016/j.ceh.2025.09.001","DOIUrl":"10.1016/j.ceh.2025.09.001","url":null,"abstract":"","PeriodicalId":100268,"journal":{"name":"Clinical eHealth","volume":"8 ","pages":"Pages 175-176"},"PeriodicalIF":0.0,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145157278","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}

{"title":"Acceptance of telehealth in the Kingdom of Saudi Arabia: an application of the UTAUT model","authors":"Abdullah A. Almojaibel ,&nbsp;Assim M. AlAbdulKader ,&nbsp;Mohammad A. Al-Bsheish ,&nbsp;Abdulelah M. Aldhahir ,&nbsp;Saeed M. Alghamdi ,&nbsp;Fatma I. Almaghlouth ,&nbsp;Abdullah S. Alqahtani ,&nbsp;Jaber S. Alqahtani ,&nbsp;Yousef D. Alqurashi ,&nbsp;Mohammed E. Alsubaiei ,&nbsp;Abdulrahman M. Jabour ,&nbsp;Mu’taman K. Jarrar ,&nbsp;Jithin K. Sreedharan ,&nbsp;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 &lt; 0.01). EE was also a significant predictor of the positive intention to use telehealth (p &lt; 0.01). SI significantly predicted telehealth usage (p &lt; 0.01), as did the FC construct (p &lt; 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}

{"title":"Integrated feature selection-based stacking ensemble model using optimized hyperparameters to predict breast cancer with smart web application","authors":"Rajib Kumar Halder,&nbsp;Marzana Akter Lima,&nbsp;Mohammed Nasir Uddin,&nbsp;Md.Aminul Islam,&nbsp;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}

{"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}

{"title":"Usability evaluation of wearable technology: A pilot study on a smart diabetic shoe for foot care","authors":"Khadijeh Moulaei ,&nbsp;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}

{"title":"The smart and healthy city business model Canvas—A post Covid-19 resilience for smart city business modeling framework","authors":"Siti Jahroh ,&nbsp;Dikky Indrawan ,&nbsp;Z.B. Junaid ,&nbsp;Asaduddin Abdullah ,&nbsp;Idqan Fahmi ,&nbsp;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}

{"title":"Securing electronic health records using blockchain-enabled federated learning for IoT-based smart healthcare","authors":"A. Althaf Ali ,&nbsp;M.A. Gunavathie ,&nbsp;V. Srinivasan ,&nbsp;M. Aruna ,&nbsp;R. Chennappan ,&nbsp;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}

{"title":"Significance of GPT-enabled LDCT lung cancer screening","authors":"Shixiong Yang ,&nbsp;Qiong Liang ,&nbsp;Weipeng Jiang ,&nbsp;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}

{"title":"Sexual content in Australian crisis telehealth","authors":"Bridie Allan,&nbsp;PJ Matt Tilley,&nbsp;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}

{"title":"Personalizing nutrition and recipe recommendation using attention mechanism with an ensemble model","authors":"Shilpa Chaudhari ,&nbsp;Archana Rane ,&nbsp;Amala Rashmi Kumar","doi":"10.1016/j.ceh.2025.03.002","DOIUrl":"10.1016/j.ceh.2025.03.002","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":100268,"journal":{"name":"Clinical eHealth","volume":"8 ","pages":"Pages 66-77"},"PeriodicalIF":0.0,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143815165","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}