Clinical eHealth最新文献

{"title":"Glu4: An open-source package for real-time forecasting and alerting post-bariatric hypoglycemia based on continuous glucose monitoring","authors":"Luca Cossu ,&nbsp;Francesco Prendin ,&nbsp;Giacomo Cappon ,&nbsp;David Herzig ,&nbsp;Lia Bally ,&nbsp;Andrea Facchinetti","doi":"10.1016/j.ceh.2025.01.003","DOIUrl":"10.1016/j.ceh.2025.01.003","url":null,"abstract":"<div><h3>Background</h3><div>Post-bariatric hypoglycemia (PBH) is a severe and often overlooked complication of bariatric surgery (BS), characterized by dangerously low blood glucose levels after meals, particularly those high in carbohydrates. Unlike in Type 1 and Type 2 diabetes (T1D, T2D), where decision support systems (DSS) and continuous glucose monitoring (CGM) tools aid blood glucose management, no dedicated DSS exists for PBH. This leaves individuals vulnerable to recurrent, unpredictable hypoglycemia, posing significant health risks. To address this gap, we propose Glu4, an open-source software package designed to predict and notify users of impending PBH events using CGM data.</div></div><div><h3>Methods</h3><div>Glu4 employs a two-step approach to predict<!--> <!-->PBH. A run-to-run algorithm forecasts future glucose levels using past CGM data, identifying potential hypoglycemic events 30 min in advance. An intelligent alarm system alerts users when glucose levels are predicted to drop below a critical threshold, prompting preventive action. A pilot study involving three PBH patients collected real-time glucose data to validate the system’s predictive performance.</div></div><div><h3>Results</h3><div>The pilot study demonstrated that Glu4 reliably predicted impending hypoglycemia in all participants, providing timely alerts 30 min before glucose drops. The system showed a high specificity, with no false alarms being triggered during the monitoring period. The proactive notifications enabled participants to manage their glucose levels more effectively by taking preventive actions such as consuming rescue carbohydrates before the onset of severe hypoglycemia.</div></div><div><h3>Conclusions</h3><div>Glu4 represents a promising tool for managing PBH, leveraging CGM data to deliver accurate, timely alerts that enable proactive intervention. By improving safety and quality of life for individuals with PBH, Glu4 addresses a critical unmet need. Future work will focus on enhancing system capabilities and conducting larger-scale studies to validate its effectiveness and refine its usability for clinical adoption.</div></div>","PeriodicalId":100268,"journal":{"name":"Clinical eHealth","volume":"8 ","pages":"Pages 1-6"},"PeriodicalIF":0.0,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143169850","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}

{"title":"Enhancing thyroid disease prediction and comorbidity management through advanced machine learning frameworks","authors":"P. Sanju ,&nbsp;N. Syed Siraj Ahmed ,&nbsp;P. Ramachandran ,&nbsp;P. Mohamed Sajid ,&nbsp;R. Jayanthi","doi":"10.1016/j.ceh.2025.01.002","DOIUrl":"10.1016/j.ceh.2025.01.002","url":null,"abstract":"<div><div>Thyroid disease is one of the most prevalent endocrine disorders worldwide, necessitating precise and efficient diagnostic models for improved clinical outcomes. This study proposes a Hybrid Feature Selection and Deep Learning Framework (HFSDLF) that integrates Random Forests with Principal Component Analysis (PCA) and L1 regularization for effective feature selection and classification. Utilizing the UCI Thyroid Dataset, the framework combines the strengths of deep learning-based feature extraction and traditional machine learning classifiers. The Random Forest classifier achieved the highest accuracy of 96.30 %, outperforming other models such as Decision Trees and Logistic Regression, with notable improvements in sensitivity and specificity. The novelty of this work lies in its hybrid approach to feature selection, which reduces dimensionality while retaining the most informative features, and its application of an optimized Random Forest model for enhanced classification accuracy. Comparative analysis with existing methods further highlights the superiority of the proposed framework in terms of accuracy and processing efficiency. This research addresses key limitations of existing approaches and contributes to the field by demonstrating a scalable and interpretable solution for thyroid disease diagnosis. The proposed framework provides a benchmark for future studies, underscoring the importance of hybrid methodologies in medical data analysis.</div></div>","PeriodicalId":100268,"journal":{"name":"Clinical eHealth","volume":"8 ","pages":"Pages 7-16"},"PeriodicalIF":0.0,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143169848","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}

{"title":"Conversational AI with large language models to increase the uptake of clinical guidance","authors":"Gloria Macia ,&nbsp;Alison Liddell ,&nbsp;Vincent Doyle","doi":"10.1016/j.ceh.2024.12.001","DOIUrl":"10.1016/j.ceh.2024.12.001","url":null,"abstract":"<div><div>The rise of large language models (LLMs) and conversational applications, like ChatGPT, prompts Health Technology Assessment (HTA) bodies, such as NICE, to rethink how healthcare professionals access clinical guidance. Integrating LLMs into systems like Retrieval-Augmented Generation (RAG) offers potential solutions to current LLMs’ problems, like the generation of false or misleading information. The objective of this paper is to design and debate the value of an AI-driven system, similar to ChatGPT, to enhance the uptake of clinical guidance within the National Health Service (NHS) of the UK. Conversational interfaces, powered by LLMs, offer healthcare practitioners clear benefits over traditional ways of getting clinical guidance, such as easy navigation through long documents, blending information from various trusted sources, or expediting evidence-based decisions in situ. But, putting these interfaces into practice brings new challenges for HTA bodies, like assuring quality, addressing data privacy concerns, navigating existing resource constraints, or preparing the organization for innovative practices. Rigorous empirical evaluations are necessary to validate their effectiveness in increasing the uptake of clinical guidance among healthcare practitioners. A feasible evaluation strategy is elucidated in this research while its implementation remains as future work.</div></div>","PeriodicalId":100268,"journal":{"name":"Clinical eHealth","volume":"7 ","pages":"Pages 147-152"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143160908","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}

{"title":"Development of a mobile health application for epilepsy self-management: Focus group discussion and validity of study results","authors":"Iin Ernawati ,&nbsp;Nanang Munif Yasin ,&nbsp;Ismail Setyopranoto ,&nbsp;Zullies Ikawati","doi":"10.1016/j.ceh.2024.12.005","DOIUrl":"10.1016/j.ceh.2024.12.005","url":null,"abstract":"<div><div>Mobile health systems in the current digital era can be an opportunity for the development of health services, especially epilepsy, which is expected to help therapy management in monitoring drug therapy. Mobile health-based interventions have now begun to be developed for chronic disease management in managing stress, monitoring drug side effects, adherence to drug use, and seizures in epilepsy patients. To create the mobile health system, it is necessary to explore information not only from the literature but also from experts and patients. Therefore, this study aimed to examine what features/elements are needed in the mobile health system application. This study used a qualitative methodology with focus group discussion (FGD). The discussion process was recorded and transcribed verbatim, and the data was analyzed using thematic analysis with a descriptive interpretation approach. In addition, content validity by experts was also carried out from features or domains found in the literature and during FGD. The results of the FGD showed that the features needed for application development include patient profiles, drug reminders, information about diseases and drugs, medication records, side effects/adverse events records, records of frequency and triggers of seizures, application appearance, and ease of use. Based on the validity content by experts, all domains and features obtained (Items Content Validation Index) I-CVI values &gt; 0.79 and were acceptable. In conclusion, this data can be used to develop the design and features of mobile health system applications for epilepsy patients.</div></div>","PeriodicalId":100268,"journal":{"name":"Clinical eHealth","volume":"7 ","pages":"Pages 190-199"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143160898","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}

{"title":"International collaboration in an online digital health education for undergraduate nursing students in China: Results and recommendations for course development from World eHealth Living Lab","authors":"Hongxia Shen ,&nbsp;Cynthia Hallensleben ,&nbsp;Haixing Shi ,&nbsp;Rianne van der Kleij ,&nbsp;Huohuo Dai ,&nbsp;Niels Chavannes","doi":"10.1016/j.ceh.2024.11.001","DOIUrl":"10.1016/j.ceh.2024.11.001","url":null,"abstract":"<div><div>Digital health enhances healthcare accessibility and should be integrated into nursing education to prepare future nurses for the evolving medical systems. An international collaboration between a Chinese medical university and World eHealth Living Lab of Leiden University Medical Center in the Netherlands developed and implemented the online course “Digital Health Empowerment and Nursing Innovation” for undergraduate nursing students in China. The course’s effectiveness was evaluated using a mixed methods approach, including a pre- and post-test assessing students’ scientific innovation ability, a post-test for protocol completion, students’ attitudes and satisfaction. 32 undergraduate nursing students completed the course, achieving a 100 % attendance rate and showing significant improvement in the total score of scientific innovation ability (37.87 ± 6.16 versus 40.97 ± 6.32, <em>P</em> = 0.049). Specifically, the score of thinking innovation improved significantly (17.31 ± 3.28 versus 19.28 ± 3.18, <em>P</em> = 0.017), while application innovation and scientific research practice scores remained unchanged. Participants highly rated the value of protocol writing with 23–25 (total score of 28) and presentation with 41–45 (total score of 48). Additionally, students reported high satisfaction with the aspects of this course including a well-structured schedule with lectures and workshops, feasible and sufficient materials on the online platform, and engaging and helpful teaching methods. Furthermore, suggestions of the course are mainly related to addressing the complexity of the platform, providing timely feedback and evaluation from teachers, and improving (online) interactions.This international collaboration effectively enhanced Chinese nursing students’ scientific innovation ability and thinking innovation, with high satisfaction reported. Future digital health education should emphasize practical research examples to implement innovations. Specifically, active teaching methods, such as practice units and student engagement in digital health innovation research implementation in clinical settings, are recommended for future courses. In areas with limited access to digital health specialists, online platforms can enhance access to high-quality medical education.</div></div>","PeriodicalId":100268,"journal":{"name":"Clinical eHealth","volume":"7 ","pages":"Pages 136-146"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142759749","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}

{"title":"Expert consensus for smoking cessation with metaverse in medicine","authors":"Lian Wu ,&nbsp;Dan Xiao ,&nbsp;Weipen Jiang ,&nbsp;Zhihao Jian ,&nbsp;Katherine Song ,&nbsp;Dawei Yang ,&nbsp;Niels H. Chavannes ,&nbsp;Chunxue Bai","doi":"10.1016/j.ceh.2024.10.001","DOIUrl":"10.1016/j.ceh.2024.10.001","url":null,"abstract":"","PeriodicalId":100268,"journal":{"name":"Clinical eHealth","volume":"7 ","pages":"Pages 164-175"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143160906","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}

{"title":"A narrative review of applications and enhancements of ChatGPT in respiratory medicine","authors":"Jun Qi Lin ,&nbsp;Zi Xuan Hua ,&nbsp;Liu Zhang ,&nbsp;Ying Ni Lin ,&nbsp;Yong Jie Ding ,&nbsp;Xi Xi Chen ,&nbsp;Shi Qi Li ,&nbsp;Yi Wang ,&nbsp;Qing Yun Li","doi":"10.1016/j.ceh.2024.12.006","DOIUrl":"10.1016/j.ceh.2024.12.006","url":null,"abstract":"<div><div>ChatGPT, a chatbot program pioneered by OpenAI and launched on 2022, stands alongside other notable large language models (LLMs) such as Google’s Bard Model and Baidu’s ERNIE Bot Model. These AI-powered tools have become integral to daily life, exerting considerable influence. Recently, AI’s medical applications gain traction as momentum grows. Meanwhile. chronic respiratory diseases pose a substantial global health burden, affecting nearly 550 million people in 2017, an increase of 39.8% compared to 1990. They remain a leading cause of death and disability worldwide, second only to cardiovascular diseases and cancer. The respiratory field grapples with unmet needs like antibiotic and anti-tuberculosis drug resistance, respiratory epidemics, and high prevalence of lung tumors, etc. Although the utilization of ChatGPT in medicine has been actively explored, its application in respiratory medicine remains in the early stages. In this context, we outline ChatGPT’s current respiratory medicine applications, address potential limitations, and envision future avenues for its advancement and development.</div></div>","PeriodicalId":100268,"journal":{"name":"Clinical eHealth","volume":"7 ","pages":"Pages 200-206"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143160907","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}

{"title":"Enhancing automatic early arteriosclerosis prediction: an explainable machine learning evidence","authors":"Eka Miranda ,&nbsp;Suko Adiarto","doi":"10.1016/j.ceh.2024.12.003","DOIUrl":"10.1016/j.ceh.2024.12.003","url":null,"abstract":"<div><h3>Objective</h3><div>This paper proposed a machine learning (ML) model to early predict patients with arteriosclerotic heart disease (AHD). We also used model-agnostic ML approaches to find and analyze informative aspects in the prediction model outcomes.</div></div><div><h3>Methods</h3><div>We employed an Electronic Health Record (EHR) for hematology that contained data on erythrocytes, hematocrit, hemoglobin, mean corpuscular hemoglobin, mean corpuscular hemoglobin concentration, leukocytes, thrombocytes, age, and sex. Our investigation included Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), Bagging Decision Tree (BDT), and Bagging Logistic Regression (BLR) for ML-based AHD detection. To handle imbalanced data and increase classifier accuracy, we used bagging and the Synthetic Minority Oversampling Technique (SMOTE). Following that, we used the Shapley Additive exPlanations (SHAP) framework to explain the ML model and quantify the feature contribution to predictions.</div></div><div><h3>Results</h3><div>SMOTE-balanced data with RF outperformed on practically all performance measures, including accuracy, precision, recall, f1-score, and ROCAUC, by 82.12 %, 81.31 %, 83.37 %, 82.57 %, and 89 %, respectively. According to the SHAP summary bar plot method for global feature importance, hemoglobin was the most important attribute for detecting and predicting AHD patients. Then, local interpretability in the form of a force plot illustrated the consequences of a single observation’s prediction as well as the magnitude of the SHAP value for each feature. Our findings demonstrated that hemoglobin, erythrocytes, hematocrit, hermch, khermchc, leukocytes, thrombocytes, and age all contributed positively to the prediction of class 1 (AHD patients), however gender had a negative impact on the prediction on a case-by-case basis. For class 0 (patients with no AHD), thrombocytes, hematocrit, and gender contributed positively, but leukocytes, erythrocytes, hemoglobin, and khermchc contributed adversely.</div></div><div><h3>Conclusion</h3><div>Explainable ML paved the way for early AHD prediction since it examined black-box ML models to determine how each feature contributed to the final prediction.</div></div>","PeriodicalId":100268,"journal":{"name":"Clinical eHealth","volume":"7 ","pages":"Pages 153-163"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143160909","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}

{"title":"Leveraging deep edge intelligence for real-time respiratory disease detection","authors":"Tahiya Tasneem Oishee,&nbsp;Jareen Anjom,&nbsp;Uzma Mohammed,&nbsp;Md. Ishan Arefin Hossain","doi":"10.1016/j.ceh.2025.01.001","DOIUrl":"10.1016/j.ceh.2025.01.001","url":null,"abstract":"<div><div>Detecting respiratory diseases such as COPD, bronchiolitis, URTI, and pneumonia is crucial for early medical intervention. This study utilizes the ICBHI dataset to train and evaluate deep learning architectures such as CNN-GRU, VGGish, YAMNet, CNN-LSTM, and basic CNN to automate this process. After a detailed analysis of the performance of these models, the CNN-LSTM model achieved an impressive accuracy and F1 score of 96% each. The model is also considerably lightweight, as its weights are further pruned and then quantized using TensorFlow Lite (TFLite), with the model being optimized at a significantly small size of 0.38 MB with only a loss of about 1% in performance. Subsequently, this was deployed to the smartphone application RespiScan. The application uses the prediction capabilities of the disease detection model on patients’ audio recordings. By providing a portable, cost-effective, and efficient, lightweight solution for respiratory health monitoring, this work contributes significantly to timely disease detection. It promotes proactive health management, thereby reducing the burden on healthcare systems. This work can be further validated in real-world conditions, such as for initial preliminary auscultation purposes, to ensure the proposed work’s efficacy across different environmental settings.</div></div>","PeriodicalId":100268,"journal":{"name":"Clinical eHealth","volume":"7 ","pages":"Pages 207-220"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143160899","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}

{"title":"A survey on define daily dose of watch- and access-category antibiotics in two Indonesian hospitals following the implementation of digital antimicrobial stewardship tool","authors":"Ronald Irwanto Natadidjaja ,&nbsp;Aziza Ariyani ,&nbsp;Hadianti Adlani ,&nbsp;Raymond Adianto ,&nbsp;Iin Indah Pertiwi ,&nbsp;Grace Nerry Legoh ,&nbsp;Alvin Lekonardo Rantung ,&nbsp;Hadi Sumarsono","doi":"10.1016/j.ceh.2024.12.004","DOIUrl":"10.1016/j.ceh.2024.12.004","url":null,"abstract":"<div><h3>Background</h3><div>In 2023, the World Health Organization (WHO) began targeting a shift in antibiotic prescribing trends from Watch to Access category. The expected target is including 60% of antibiotic prescribing in the Access category.</div></div><div><h3>Method</h3><div>This survey was a preliminary study, in which our study group designed a digital model of antimicrobial stewardship and the model was known as e-RASPRO. It was an initial review on the implementation of e-RASPRO tool prior to its wider use in future hospitals. The survey on the use of antibiotic Define Daily Dose / 100 patient days (DDD) was carried out in two hospitals in Indonesia at 3 months and 9 months of use, respectively. Hospital 1 as a primary hospital, Hospital 2 as a referral hospital. Data was retrieved retrospectively at the inpatient wards of both hospitals.</div></div><div><h3>Result</h3><div>Three months before and after the implementation of e-RASPRO in Hospital 1, we found an increase in DDD of prophylactic antibiotic Cefazolin by 167.18 %. In hospital 2, it could not be described because Cefazolin had been used since the hospital applied the manual RASPRO concept. DDD of Watch category antibiotics within 9 months following the implementation of e-RASPRO tool in hospital 1 showed a decrease of 49.01 %. Meanwhile, the implementation of e-RASPRO for 3 months in Hospital 2 still showed an increase in Watch category antibiotics by 20.18 %; however, there was a decrease in DDD of Cephalosporin and Glycopeptide antibiotics by 7.63 % and 49.30 %, respectively. In the meantime, as a way of saving antibiotic use and shifting antibiotic prescribing to the Access category, we found a decrease in DDD of Access category antibiotics in Hospital 1 by 3.64 % and an increase in Hospital 2 by 8.14 %</div></div><div><h3>Conclusion</h3><div>The survey may indicate that there are savings attempts in antibiotic use as well as an early change in DDD antibiotics from the Watch category to the Access category following the implementation of e-RASPRO tool in both hospitals. The time period of using the digital devices may still affect the results; however, this survey certainly has not illustrated a strong cause-and-effect correlation between the use of e-RASPRO tool and antibiotic DDD.</div></div>","PeriodicalId":100268,"journal":{"name":"Clinical eHealth","volume":"7 ","pages":"Pages 176-189"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143159891","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}