Clinical eHealth最新文献

Blockchain affordances in digital health: a systematic review and research agenda for personalized health-care 区块链在数字卫生方面的支持：个性化卫生保健的系统审查和研究议程

Clinical eHealth

Pub Date : 2025-12-01 DOI: 10.1016/j.ceh.2025.11.002

Arnob Zahid , Stephen C. Wingreen , Ravishankar Sharma

We aimed to systematically review Blockchain affordances in digital health and synthesize a framework and research agenda for personalized health-care. Following PRISMA guidance, we conducted a qualitative thematic synthesis of peer-reviewed studies (January 2020 – October 2025 search window; no meta-analysis). Blockchain technology is an emerging solution that can meet these needs. However, a nuanced application of Blockchain affordances in digital health is necessary to harness its full potential. This paper comprehensively reviews Blockchain affordances in digital health, aiming to identify perceived affordances and explore recent research in the field. We applied Preferred Reporting Items for Systematic Reviews, following the PRISMA guidelines and the lens of affordance theory to analyze about 5300 relevant papers, with 194 selected for deeper analysis. Our analysis identified 14 Blockchain affordances (access control, decentralization, interoperability, security, tamper-resistance, traceability, anonymity, data provenance, identity, immutability, integrity, privacy, transparency, and trust) that are perceived and realized in personalized health-care. Our study also discovered several constraints in Blockchain implementation, such as security and privacy, interoperability, scalability, and infrastructural support, that require further research attention. This study presents Blockchain research in the digital health domain and informs the design and development of computational medicine for personalized health-care.

我们的目标是系统地审查区块链在数字健康方面的能力，并综合个性化医疗保健的框架和研究议程。在PRISMA的指导下，我们对同行评议的研究进行了定性的专题综合（2020年1月至2025年10月的搜索窗口；没有荟萃分析）。区块链技术是一种新兴的解决方案，可以满足这些需求。然而，为了充分利用区块链的潜力，有必要在数字医疗中细致入微地应用区块链的功能。本文全面回顾了数字健康中的区块链可得性，旨在识别可得性并探索该领域的最新研究。我们按照PRISMA指南和信息性理论的视角，应用首选报告项目进行系统评价，分析了约5300篇相关论文，其中194篇论文进行了深入分析。我们的分析确定了在个性化医疗保健中可感知和实现的14个区块链功能（访问控制、去中心化、互操作性、安全性、防篡改、可追溯性、匿名性、数据来源、身份、不变性、完整性、隐私性、透明度和信任）。我们的研究还发现了区块链实现中的一些限制，例如安全性和隐私性、互操作性、可伸缩性和基础设施支持，这些都需要进一步的研究关注。本研究介绍了数字健康领域的区块链研究，并为个性化医疗保健的计算医学的设计和开发提供了信息。

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引用次数: 0

An ensemble model for detection of Parkinson’s disease by comparing numerous machine learning models and XGBoost based on vocal features 通过比较众多机器学习模型和基于声音特征的XGBoost来检测帕金森病的集成模型

Clinical eHealth

Pub Date : 2025-12-01 DOI: 10.1016/j.ceh.2025.11.003

Putta Durga , Ali B.M. Ali , Deepthi Godavarthi , Sachi Nandan Mohanty , Shoira Formanova , M. Ijaz Khan

Parkinson’s disease (PD), a condition of the brain, causes accidental or intractable tendencies including shaking, stiffness, and issues with balance and coordination. In most cases, symptoms start mildly and get worse with time. Patients may have problems speaking and walking as the illness worsens. Additionally, they may exhibit altered mental and behavioral patterns, sleep disorders, sadness, memory difficulty, and exhaustion. In general, it is difficult to forecast sickness. Additionally, more than 25 % of PD diagnoses are incorrect because of the significant similarity between PD symptoms and other neurological symptoms. This prompted us to conduct a comparative literature review of how cutting-edge Machine Learning (ML) implementations are used in these methodologies with their corresponding datasets, including Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), K-nearest neighbors (KNN), Random Forest (RF), and Naïve Bayes (NB) classifiers. To increase accuracy, we have used multi-ensemble methods like the XGBoost Classifier and Ensemble (Majority Voting: RF & LSTM) are also used. Our results are contrasted with those from each study. The Static Spiral Test, which is used to identify tremors, performed significantly better in all experiments after applying XGBoost. As a result, it can be deduced that the multi-modal technique is efficient when used in conjunction with the ensemble method Xgboost classification (Extreme gradient boosting) and Ensemble (Majority Voting: RF & LSTM) that it offers a high accuracy of (95 %, and 96 %) in comparison to other classifier models. The approaches’ performance was assessed using a trustworthy dataset from the UCI ML repository.

帕金森氏症（PD）是一种脑部疾病，会导致意外或棘手的倾向，包括颤抖、僵硬以及平衡和协调问题。在大多数情况下，症状开始轻微，随着时间的推移而恶化。随着病情的恶化，患者可能会出现说话和行走的问题。此外，他们可能表现出精神和行为模式的改变、睡眠障碍、悲伤、记忆困难和疲惫。一般来说，疾病是很难预测的。此外，超过25%的PD诊断是不正确的，因为PD症状与其他神经系统症状之间存在显著的相似性。这促使我们对这些方法及其相应的数据集（包括逻辑回归（LR）、支持向量机（SVM）、决策树（DT）、k近邻（KNN）、随机森林（RF）和Naïve贝叶斯（NB）分类器）中如何使用前沿机器学习（ML）实现进行比较文献综述。为了提高准确性，我们使用了多集成方法，如XGBoost分类器和集成（多数投票：RF & &； LSTM）也被使用。我们的结果与每个研究的结果进行了对比。使用XGBoost后，用于识别震颤的静态螺旋测试在所有实验中表现明显更好。因此，可以推断出，当与集成方法Xgboost分类（极端梯度增强）和集成（多数投票：RF & &； LSTM）结合使用时，多模态技术是有效的，与其他分类器模型相比，它提供了较高的准确率（95%和96%）。使用来自UCI ML存储库的可信数据集评估了这些方法的性能。

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引用次数: 0

OVT-Net: Semantic segmentation of gastrointestinal cancer using an optimized vision transformer model with explainable AI OVT-Net：使用优化的视觉转换模型和可解释的人工智能对胃肠道癌症进行语义分割

Clinical eHealth

Pub Date : 2025-12-01 DOI: 10.1016/j.ceh.2025.11.001

Anika Tahsin Meem, Sifat Momen

Gastric and intestinal cancers are among the deadliest gastrointestinal diseases, necessitating precise organ segmentation for effective detection and treatment planning. Conventional deep-learning models, such as CNN-based U-Net architectures, struggle with long-range dependencies and complex anatomical variations. This study introduces OVT-Net (Optimized Vision Transformer Network), an innovative deep-learning framework integrating Swin Transformer blocks, EfficientNetB7, Adaptive Contextual Attention (ACA) module, Atrous Spatial Pyramid Fusion (ASPF), and Squeeze-and-Excite (SE) Networks. Unlike traditional architectures, OVT-Net employs a hybrid dual-encoder structure, combining EfficientNetB7 for low-level feature extraction and Swin Transformers for global context modeling, addressing intricate anatomical complexities and imaging variabilities of the gastrointestinal tract. The model is trained on 38,496 MRI/CT scans paired with RLE-encoded masks that contain structural and labeling inconsistencies. These inconsistencies are resolved through a comprehensive preprocessing pipeline incorporating path generation, label restructuring, and augmentation to improve generalizability. Experimental results demonstrate superior performance, with a Dice score of 0.9350, an IoU score of 0.9218, a BCE loss of 0.0716, and robust surface distance metrics (HD95 and ASSD), outperforming conventional segmentation methods. To enhance clinical applicability, Explainable AI (XAI) techniques, including Grad-CAM and Grad-CAM++, provide interpretability by highlighting critical regions, improving model transparency in decision-making. Furthermore, OVT-Net is deployed in a Django-based web application, facilitating real-time segmentation and classification with an average accuracy of 97.5 %. This research presents OVT-Net as a transformative AI-driven segmentation model, bridging advanced vision transformers with XAI for enhanced medical diagnostics. Its integration into real-world clinical settings paves the way for improved cancer detection and early intervention.

胃癌和肠癌是最致命的胃肠道疾病之一，为了有效的检测和治疗计划，需要精确的器官分割。传统的深度学习模型，如基于cnn的U-Net架构，难以应对长期依赖关系和复杂的解剖变化。本研究介绍了OVT-Net（优化视觉变压器网络），这是一个创新的深度学习框架，集成了Swin变压器模块、EfficientNetB7、自适应上下文注意（ACA）模块、亚特拉斯空间金字塔融合（ASPF）和挤压和激发（SE）网络。与传统架构不同，OVT-Net采用混合双编码器结构，结合了用于低级特征提取的EfficientNetB7和用于全局上下文建模的Swin transformer，解决了胃肠道复杂的解剖复杂性和成像变异性。该模型在38,496个MRI/CT扫描上进行训练，这些扫描与包含结构和标记不一致的rle编码掩码配对。这些不一致通过综合的预处理管道解决，包括路径生成、标签重组和增强，以提高通用性。实验结果表明，该方法性能优越，Dice分数为0.9350，IoU分数为0.9218，BCE损失为0.0716，并且具有鲁棒的表面距离度量（HD95和ASSD），优于传统的分割方法。为了提高临床适用性，可解释AI （XAI）技术，包括Grad-CAM和Grad-CAM++，通过突出关键区域提供可解释性，提高决策模型的透明度。此外，OVT-Net部署在基于django的web应用程序中，促进实时分割和分类，平均准确率为97.5%。这项研究将OVT-Net作为一种变革性的人工智能驱动的分割模型，将先进的视觉变压器与XAI连接起来，以增强医疗诊断。它与现实世界的临床环境相结合，为改善癌症检测和早期干预铺平了道路。

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引用次数: 0

Attitudes toward smartphone-based self-hearing screening among mild cognitive impairment: A mixed method study 轻度认知障碍患者对智能手机自听筛查的态度：一项混合方法研究

Clinical eHealth

Pub Date : 2025-12-01 DOI: 10.1016/j.ceh.2025.12.001

Patcharaorn Limkitisupasin , Nattawan Utoomprurkporn , Pimrada Potipimpanon , Sookjaroen Tangwongchai , Doris-eva Bamiou

Background

Hearing loss is the largest modifiable risk factor for dementia with high prevalence in memory clinics. Early identification and intervention are crucial to prevent cognitive decline. Pure-tone audiometry is often lacking in many settings. Smartphone-based hearing screening tests present an innovative solution; however, cognitive impairment poses a barrier to their use. This study aims to explore the perceptions, acceptability, and attitudes of older adults with cognitive impairment toward this tool.

Methods

Seventeen cognitively impaired patients participated in this cross-sectional study, using a mixed-method approach. Participants used a self-administered smartphone application, including the Hearing Handicap Inventory for the Elderly, pure-tone hearing screening test, and digit-in-noise test. They completed a System Usability Scale (SUS) questionnaire for quantitative analysis, and qualitative data were collected through three focus group interviews.

Results

Participants averaged 71.8 years old, with a mean MoCA score of 25.8. The SUS score was 61 ± 24, indicating marginal acceptability. Thematic analysis revealed five main themes: hearing status, past hearing test experiences, experiences with the smartphone test, test performance and engagement, and attitudes toward the smartphone test. User experience was shaped by several interrelated factors, including ease of use, guidance support, aging, and trust in the technology.

Conclusions

Smartphone-based hearing tests are a possible workable option for people with mild cognitive impairment, though their usability remains modest and practical challenges persist. The valuable findings of this study will help refine these tools to better meet their specific needs.

背景：听力损失是痴呆的最大可改变的危险因素，在记忆诊所中发病率很高。早期识别和干预对于预防认知能力下降至关重要。纯音测听在很多情况下是缺乏的。基于智能手机的听力筛查测试提供了一种创新的解决方案；然而，认知障碍对它们的使用构成了障碍。本研究旨在探讨认知障碍老年人对该工具的认知、接受度和态度。方法采用混合方法对17例认知障碍患者进行横断面研究。参与者使用了一个自我管理的智能手机应用程序，包括老年人听力障碍清单、纯音听力筛查测试和数字噪声测试。他们完成了系统可用性量表（SUS）问卷进行定量分析，并通过三次焦点小组访谈收集了定性数据。结果参与者平均年龄71.8岁，MoCA得分25.8分。SUS评分为61±24分，表示边缘性可接受。主题分析揭示了五个主要主题：听力状况、过去的听力测试经历、智能手机测试经历、测试表现和参与度，以及对智能手机测试的态度。用户体验是由几个相互关联的因素形成的，包括易用性、指导支持、老化和对技术的信任。结论基于智能手机的听力测试对于轻度认知障碍的人来说是一种可行的选择，尽管它们的可用性仍然有限，而且实际的挑战仍然存在。这项研究的有价值的发现将有助于改进这些工具，以更好地满足他们的具体需求。

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引用次数: 0

DigiCAS-HPS: A tailored digital competence assessment scale for health professions students in the post-COVID era in Vietnam DigiCAS-HPS：为越南后covid时代卫生专业学生量身定制的数字能力评估量表

Clinical eHealth

Pub Date : 2025-11-07 DOI: 10.1016/j.ceh.2025.10.002

Cua Ngoc Le , Uyen Thi To Nguyen , Luan Minh Le , Dien Phu Tran

The COVID-19 pandemic has accelerated the adoption of digital health, highlighting the critical role of digital health competencies in delivering reliable and effective healthcare services. These competencies remain essential post-pandemic to integrate eHealth technologies, telemedicine, and IoT-enabled healthcare solutions into routine clinical practice. This study aimed to develop and validate a tailored digital competence assessment scale (DigiCAS-HPS) for health professions students and to identify differences in digital competence across various demographic and academic groups. A stratified sampling method selected 717 health professions students from four majors in Dong Thap province, Vietnam. Based on previous literature on digital frameworks, pilot testing, and expert review, we generated, refined, and utilized the questionnaire to collect data from the first sample of 366 students to identify the underlying latent factor structure by an exploratory factor analysis. We then applied a confirmatory factor analysis to affirm the structural validity and model-data fit with the second sample of 351 students. The Mann-Whitney and Kruskal Wallis test determined significant differences in digital competence levels among student groups classified by gender, age, field of study, and academic year. The DigiCAS-HPS scale resulted in 16 items and two factors, encompassing Factor 1 (Digital Interaction & Responsibility) and Factor 2 (Digital Content Development and Software Mastery), and demonstrated valid results in model fit, construct validity, and reliability. Personal factors such as older and soon-to-graduate students revealed significant associations with higher proficiency in digital content development and software mastery (p < 0.05). The DigiCAS-HPS scale might be a helpful tool for educators, healthcare institutions, and policymakers to assess and integrate digital competencies into healthcare education. To address the demands of eHealth, telemedicine, and IoT-enabled healthcare systems, the scale supports the development of a digitally proficient healthcare workforce prepared to navigate the dynamic and technology-driven healthcare landscape.

2019冠状病毒病大流行加速了数字卫生的采用，凸显了数字卫生能力在提供可靠和有效的医疗保健服务方面的关键作用。这些能力对于将电子卫生技术、远程医疗和支持物联网的医疗保健解决方案整合到常规临床实践中仍然至关重要。本研究旨在为卫生专业学生开发和验证量身定制的数字能力评估量表（DigiCAS-HPS），并确定不同人口统计学和学术群体之间的数字能力差异。采用分层抽样方法，选取越南同塔省4个卫生专业的717名学生。基于以往关于数字框架、试点测试和专家评审的文献，我们生成、提炼并使用了问卷，收集了366名学生的第一样本数据，通过探索性因素分析来识别潜在因素结构。然后，我们应用验证性因子分析来确认结构效度和模型数据的拟合与第二样本351名学生。Mann-Whitney和Kruskal Wallis测试确定了按性别、年龄、学习领域和学年分类的学生群体在数字能力水平上的显著差异。DigiCAS-HPS量表包括16个项目和两个因素，包括因素1（数字交互和责任）和因素2（数字内容开发和软件掌握），并在模型拟合、结构效度和信度方面证明了有效的结果。年龄较大和即将毕业的学生等个人因素显示出与数字内容开发和软件精通程度较高的显著关联（p < 0.05）。DigiCAS-HPS量表可能是教育工作者、医疗保健机构和政策制定者评估数字能力并将其整合到医疗保健教育中的有用工具。为了满足电子医疗、远程医疗和支持物联网的医疗保健系统的需求，该规模支持培养精通数字的医疗保健工作人员，以应对动态和技术驱动的医疗保健领域。

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引用次数: 0

Association between social media use and cyberchondria during the COVID-19 pandemic: a cross-sectional study COVID-19大流行期间社交媒体使用与网络病症之间的关系：一项横断面研究

Clinical eHealth

Pub Date : 2025-11-04 DOI: 10.1016/j.ceh.2025.10.004

Nadia Koleilat , Abir Ghosson , Adel Ghandour , Fatima Soufan , Hussein Kaddoura , Mohammad Jounblat , Saria Abdallah , Issam Shaarani

Cyberchondria is defined as an excessive or repeated online health-related information-seeking behavior exacerbated by information overload and quarantine, resulting in amplified health anxiety. A total of 406 Lebanese participants, residing in Lebanon, participated in this cross-sectional study conducted between February and March 2022 to investigate the association between social media use and cyberchondria severity. Participants filled an online questionnaire assessing the severity of cyberchondria (via short Cyberchondria Severity Scale (CSS-12)), fear of COVID-19 (via the Fear of COVID-19 Scale (FCV–19S)), and social media use. The majority of recruited participants were females (76.6 %) with an average age of 30.87 ± 12.68 years. The average time spent on social media per day was 4.19 ± 2.86 h, and the mean scores per item were 2.27 ± 0.73 and 2 ± 0.71 of CSS-12 and Fear of COVID-19, respectively. Social media use for health-related information and considering health-related information from social media, google, and medical websites reliable, were found to be significantly associated with cyberchondria. The developed multiple linear regression model justified 23.3 % of the variation of cyberchondria severity score. Besides, social media use for health-related information (p-value < 0.001), Fear of COVID-19 (p-value < 0.001), and age (p-value = 0.046) were significantly associated with cyberchondria severity. This implies the importance of social media implementation in the health care field in the forms of e-medicine and telehealth.

网络疑病症被定义为一种过度或反复的在线健康相关信息搜索行为，因信息过载和隔离而加剧，导致健康焦虑放大。共有406名居住在黎巴嫩的黎巴嫩人参加了这项横断面研究，该研究于2022年2月至3月进行，旨在调查社交媒体使用与网络疑病症严重程度之间的关系。参与者填写了一份在线问卷，评估网络疑病的严重程度（通过简短的网络疑病严重程度量表（CSS-12））、对COVID-19的恐惧（通过对COVID-19的恐惧量表（FCV-19S））和社交媒体的使用。大多数招募的参与者为女性（76.6% %），平均年龄为30.87 ± 12.68 岁。平均每天使用社交媒体时间为4.19 ± 2.86 h， CSS-12和恐惧得分分别为2.27 ± 0.73和2 ± 0.71。研究发现，使用社交媒体获取与健康相关的信息，并考虑来自社交媒体、谷歌和可靠的医疗网站的与健康相关的信息，与网络疑病症显著相关。建立的多元线性回归模型证实了网络疑病严重程度评分的变异率为23.3% %。此外，社交媒体对健康相关信息的使用（p值 <； 0.001）、对COVID-19的恐惧（p值 <； 0.001）和年龄（p值 = 0.046）与网络疑病症严重程度显著相关。这意味着以电子医疗和远程保健的形式在保健领域实施社会媒体的重要性。

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引用次数: 0

Healthcare 4.0: Opportunities and barriers in the implementation of medical equipment 医疗保健4.0：医疗设备实施中的机遇和障碍

Clinical eHealth

Pub Date : 2025-11-03 DOI: 10.1016/j.ceh.2025.10.005

Auro de Jesus Cardoso Correia , Guilherme Henrique de Magalhães , Walter Cardoso Satyro , Mauro de Mesquita Spinola

This study investigates the opportunities and barriers in the implementation of medical equipment within the context of Healthcare 4.0, offering a comprehensive analysis based on the perspectives of 104 industry experts through a sample survey. Using a methodology that combines literature review with descriptive and non-parametric statistical analysis, including the Friedman and Holm-Sidak tests, the study reveals a clear hierarchy of perceived opportunities and challenges. The results indicate that Remote Monitoring, Technology for Medical Precision, and Microelectronics and Innovation in Healthcare are the most valued opportunities. These results reflect a strong trend toward technologies that promote personalized and preventive healthcare. Conversely, Implementation and Maintenance Costs, Poor Infrastructure and Legacy Systems, and challenges in System Integration and Interoperability emerge as the most significant barriers. The statistical analysis demonstrates significant differences in experts’ perceptions, providing valuable insights for the prioritization of investments and implementation strategies in the healthcare sector. This study contributes to the understanding of the factors that drive and inhibit the adoption of advanced healthcare technologies, offering practical implications for healthcare organizations, policymakers, and researchers. The findings have the potential to guide the development of more effective strategies for implementing Healthcare 4.0, promoting a faster and more successful digital transformation in the healthcare sector, with significant benefits for the quality and accessibility of healthcare in society.

本研究调查了医疗保健4.0背景下实施医疗设备的机会和障碍，通过抽样调查，基于104位行业专家的观点，提供了全面的分析。采用文献综述与描述性和非参数统计分析相结合的方法，包括Friedman和Holm-Sidak测试，该研究揭示了感知机会和挑战的清晰层次结构。结果表明，远程监控、医疗精密技术、微电子和医疗保健创新是最有价值的机会。这些结果反映了促进个性化和预防性医疗保健技术的强烈趋势。相反，实现和维护成本，糟糕的基础设施和遗留系统，以及系统集成和互操作性方面的挑战成为最重要的障碍。统计分析表明，专家的看法存在显著差异，为医疗保健部门的投资优先级和实施战略提供了有价值的见解。本研究有助于了解推动和抑制采用先进医疗保健技术的因素，为医疗保健组织、政策制定者和研究人员提供实际意义。研究结果有可能指导制定更有效的战略，以实施医疗保健4.0，促进医疗保健行业更快、更成功的数字化转型，为社会医疗保健的质量和可及性带来显著效益。

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引用次数: 0

Recommending high-quality health apps: Identifying key behavioral determinants of healthcare professional behavior 推荐高质量的健康应用程序：确定医疗保健专业行为的关键行为决定因素

Clinical eHealth

Pub Date : 2025-11-02 DOI: 10.1016/j.ceh.2025.10.001

I. Biliunaite , M.A. Adriaanse , A.P.Y. Hoogendoorn , A. Montvila , M.R. Crone , L.C. van Gestel

Healthcare professionals (HCPs) commonly see the potential of health apps for their patients, but in practice do not actively recommend them during consultation. As quality concerns have been identified as a key barrier, a health and wellness app assessment framework and related quality label was previously developed. Yet, even when health apps are of high quality, recommendation behavior may not necessarily follow due to other factors that are yet to be identified and targeted. The main aim of this study was to explore a wide range of HCP behavioral determinants and identify the key determinants of HCP app recommendation behavior. We used the TDF-checklist, which is based on the Theoretical Domains Framework (TDF), an evidence-based framework for the systematic assessment of behavioral determinants of HCP behavior, and adapted it to the study context. 290 Catalan HCPs filled in the survey. For all determinants, room for improvement (deviation from the maximum), relevance (correlation with anticipated behavior), and the potential for change (based on combining room for improvement and relevance) were assessed. A large majority of HCPs indicated they would recommend high-quality apps to their patients. Overall, HCPs were motivated, but more room for improvement was found for capability and opportunity-related domains. Anticipated recommendation behavior correlated strongest with motivational factors like beliefs about consequences and beliefs about capabilities. The potential for change was highest for nature of the behaviors (habit), beliefs about capabilities and knowledge. When implementing the label, efforts should focus on promoting habit formation for recommending high-quality apps, boosting confidence of HCPs, and providing further knowledge regarding health apps.

医疗保健专业人员（HCPs）通常会看到健康应用程序对患者的潜力，但实际上在咨询期间不会积极推荐它们。由于质量问题已被确定为主要障碍，因此之前开发了健康和保健应用程序评估框架和相关的质量标签。然而，即使健康应用质量很高，由于其他尚未确定和针对的因素，推荐行为也不一定会随之而来。本研究的主要目的是探索广泛的HCP行为决定因素，并确定HCP应用程序推荐行为的关键决定因素。我们使用了基于理论领域框架（TDF）的TDF清单，这是一个基于证据的框架，用于系统评估HCP行为的行为决定因素，并将其调整为研究背景。290名加泰罗尼亚医护人员填写了调查问卷。对于所有的决定因素，改进的空间（偏离最大值）、相关性（与预期行为的相关性）和变化的潜力（基于改进的空间和相关性的结合）被评估。绝大多数医护人员表示，他们会向患者推荐高质量的应用程序。总体而言，HCPs是有动力的，但在能力和机会相关领域发现了更多的改进空间。预期的推荐行为与动机因素相关性最强，比如对结果的信念和对能力的信念。行为（习惯）的本质、对能力和知识的信念的改变潜力最大。在实施标签时，应注重促进习惯的形成，以推荐高质量的应用程序，增强hcp的信心，并提供更多关于健康应用程序的知识。

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引用次数: 0

An application to detect osteoporosis using ensemble Machine learning with hyperparameter tuning and model interpretability 利用集成机器学习与超参数调整和模型可解释性来检测骨质疏松症的应用

Clinical eHealth

Pub Date : 2025-11-02 DOI: 10.1016/j.ceh.2025.10.003

Abir Chowdhury , Md.Mahbubur Rahman Druvo , Md.Shariful Islam , Khandaker Mohammad Mohi Uddin , Md Ashraf Uddin

Millions of people worldwide suffer greatly from osteoporosis, a chronic bone disease marked by decreased bone mass and structural degradation. Timely intervention and therapy of osteoporosis depend heavily on accurate early osteoporosis prediction. In the proposed method, use a chronic dataset of patient characteristics and risk variables to present a machine learning framework for osteoporosis prediction. Class imbalance is handled by the pipeline by utilizing synthetic minority over-sampling technique (SMOTE) and other data preprocessing techniques including scaling and normalization. Then, the data was split in an 80:20 ratio and seven features were selected by mutual information. Using an ensemble learning technique and also adjusted the hyperparameters of several classification algorithms such as random forest, k-nearest neighbors, support vector machine, XGBoost and logistic regression. XGBoost, the top-performing algorithm has an AUC score of 81.08%, showing excellent classification performance. Furthermore, the interpretability of the model was improved through the utilization of shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME) by the XGBoost. This facilitated a more profound comprehension of the fundamental elements propelling the prediction. At last, develops a web interface where patients can know about their own condition by it. According to the work, the suggested framework is a useful tool for osteoporosis early prognosis which could help medical practitioners make treatment decisions.

全世界有数百万人严重患有骨质疏松症，这是一种以骨量减少和结构退化为特征的慢性骨病。骨质疏松症的及时干预和治疗在很大程度上取决于准确的骨质疏松症早期预测。在提出的方法中，使用患者特征和风险变量的慢性数据集来呈现骨质疏松症预测的机器学习框架。类不平衡由管道处理，利用合成少数过采样技术（SMOTE）和其他数据预处理技术，包括缩放和归一化。然后，将数据按80:20的比例进行分割，通过互信息选择出7个特征。采用集成学习技术，对随机森林、k近邻、支持向量机、XGBoost和逻辑回归等几种分类算法的超参数进行了调整。表现最好的算法XGBoost的AUC得分为81.08%，表现出优异的分类性能。此外，XGBoost利用shapley加性解释（SHAP）和局部可解释模型不可知解释（LIME）提高了模型的可解释性。这有助于更深刻地理解推动这一预测的基本因素。最后，开发了一个web界面，患者可以通过该界面了解自己的病情。根据本研究，建议的框架是骨质疏松症早期预后的有用工具，可以帮助医生做出治疗决策。

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引用次数: 0

The rise of AI in healthcare: Are chatbots ready to lead? 人工智能在医疗领域的崛起：聊天机器人准备好引领潮流了吗？

Clinical eHealth

Pub Date : 2025-09-19 DOI: 10.1016/j.ceh.2025.09.001

Ahmed Yaseen Alqutaibi , Anas Saeed AL-Zaghruri

引用次数: 0