Clinical eHealth最新文献

{"title":"Expert consensus for smoking cessation with metaverse in medicine","authors":"Lian Wu , Dan Xiao , Weipen Jiang , Zhihao Jian , Katherine Song , Dawei Yang , Niels H. Chavannes , 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}

{"title":"“AI et al.” The perils of overreliance on Artificial Intelligence by authors in scientific research","authors":"Juan S. Izquierdo-Condoy,&nbsp;Jorge Vásconez-González,&nbsp;Esteban Ortiz-Prado","doi":"10.1016/j.ceh.2024.09.001","DOIUrl":"10.1016/j.ceh.2024.09.001","url":null,"abstract":"<div><div>The rapid integration of Artificial Intelligence (AI) into scientific research and publication processes marks a significant shift in knowledge generation. This transition from traditional literature searches to AI-driven algorithms has accelerated tasks such as writing, editing, and summarizing scientific manuscripts. While AI holds promise for improving efficiency and accuracy, concerns have arisen about its potential misuse and the erosion of scientific integrity.</div></div>","PeriodicalId":100268,"journal":{"name":"Clinical eHealth","volume":"7 ","pages":"Pages 133-135"},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S258891412400011X/pdfft?md5=70c6586f1ccff88f9064013143660cc2&pid=1-s2.0-S258891412400011X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142314099","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 systematic review of eHealth and mHealth interventions for lymphedema patients","authors":"Andrea Mangion,&nbsp;Bruno Ivasic,&nbsp;Neil Piller","doi":"10.1016/j.ceh.2024.08.002","DOIUrl":"10.1016/j.ceh.2024.08.002","url":null,"abstract":"<div><p>Lymphedema is a chronic inflammatory disease that causes chronic swelling in the affected area, necessitating daily treatment. Millions of people worldwide are affected. The investigation of strategies to improve the overall health of patients, such as through the utilisation of electronic health (eHealth), is justified considering the ongoing burden of daily self-care. This research aimed to (a) identify current published research in eHealth and mobile health (mHealth) interventions for patients living with lymphedema; (b) assess feasibility and efficacy of the interventions; and (c) understand whether intervention adherence was affected by using eHealth. A systematic review was undertaken. Seven databases including MEDLINE, Scopus, Web of Science, CINAHL, the Cochrane Library, PsycINFO and IEEE Xplore were searched. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses were used. 1857 studies were identified through the database search with 9 meeting the inclusion criteria for a total of 1031 participants. There were 3 types of eHealth, including instructive online content, telehealth, and digital gaming. The efficacy of various eHealth and mHealth modalities was demonstrated in areas such as lymphedema outcomes, self-care, psychosocial outcomes, and disease comprehension. Reports of feasibility demonstrated that eHealth modalities were generally well accepted or preferred over conventional methods. 7 studies reported or discussed adherence and provided insight into the relationship between the design of the eHealth tool and the completion of the intervention. Several distinct categories of eHealth and mHealth interventions were shown to improve disease comprehension, psychosocial and lymphedema outcomes. Findings from this systematic review may have an impact on the design of future studies in this domain, including consideration of early user acceptance testing when developing eHealth tools. With the ongoing progress in eHealth technology, further investigation into eHealth is warranted given the encouraging results observed in a limited number of studies.</p></div>","PeriodicalId":100268,"journal":{"name":"Clinical eHealth","volume":"7 ","pages":"Pages 120-132"},"PeriodicalIF":0.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2588914124000108/pdfft?md5=c54858a4e940ccc380deee2df6ac4e8f&pid=1-s2.0-S2588914124000108-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142084002","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":"Machine learning and transfer learning techniques for accurate brain tumor classification","authors":"Seyed Matin Malakouti,&nbsp;Mohammad Bagher Menhaj,&nbsp;Amir Abolfazl Suratgar","doi":"10.1016/j.ceh.2024.08.001","DOIUrl":"10.1016/j.ceh.2024.08.001","url":null,"abstract":"<div><p>Brain tumors, resulting from uncontrolled and rapid cell growth, pose significant health risks if not treated early. Despite numerous advancements, accurate segmentation and classification remain challenging. This study leverages machine learning (ML) and transfer learning techniques to classify healthy and sick individuals using numerical data and MRI images. We utilized 3762 MRI images alongside Light Gradient Boosting Machine (LightGBM), AdaBoost, gradient boosting, Random Forest, Quadratic Discriminant Analysis, Linear Discriminant Analysis, logistic regression, and transfer learning algorithms. Numerical data was processed with LightGBM, achieving an accuracy of 95.7 %. Transfer learning applied to image data using a modified GoogLeNet model further enhanced classification accuracy to 99.3 %. These results demonstrate the effectiveness of combining ML and transfer learning techniques for accurate brain tumor classification, addressing limitations of prior approaches and offering improved diagnostic reliability. All coding and model implementations were conducted on the Python platform.</p></div>","PeriodicalId":100268,"journal":{"name":"Clinical eHealth","volume":"7 ","pages":"Pages 106-119"},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2588914124000091/pdfft?md5=802f4d01ec0d0e73328b9da3cc172a18&pid=1-s2.0-S2588914124000091-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141963788","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":"Internet of Things in healthcare: An adaptive ethical framework for IoT in digital health","authors":"Abubakar Wakili,&nbsp;Sara Bakkali","doi":"10.1016/j.ceh.2024.07.001","DOIUrl":"10.1016/j.ceh.2024.07.001","url":null,"abstract":"<div><p>The emergence of the Internet of Things (IoT) has sparked a profound transformation in the field of digital health, leading to the rise of the Internet of Medical Things (IoMT). These IoT applications, while promising significant enhancements in patient care and health outcomes, simultaneously present a myriad of ethical dilemmas. This paper aims to address these ethical challenges by introducing the Adaptive Ethical Framework for IoT in Digital Health (AEFIDH), a comprehensive evaluation framework designed to examine the ethical implications of IoT technologies within digital health contexts. The AEFIDH is developed using a mixed-methods approach, encompassing expert consultations, surveys, and interviews. This approach was employed to validate and refine the AEFIDH, ensuring it encapsulates critical ethical dimensions, including data privacy, informed consent, user autonomy, algorithmic fairness, regulatory compliance, ethical design, and equitable access to healthcare services. The research reveals pressing issues related to data privacy, security, and user autonomy and highlights the imperative need for an increased focus on algorithmic transparency and the integration of ethical considerations in the design and development of IoT applications. Despite certain limitations, the AEFIDH provides a promising roadmap for guiding the responsible development, deployment, and utilization of IoT technologies in digital health, ensuring its relevance amidst the rapidly evolving digital health landscape. This paper contributes a novel, dynamic framework that encapsulates current ethical considerations and is designed to adapt to future technological evolutions, thereby fostering ethical resilience in the face of ongoing digital health innovation. The framework’s inherent adaptability allows it to evolve in tandem with technological advancements, positioning it as an invaluable tool for stakeholders navigating the ethical terrain of IoT in healthcare.</p></div>","PeriodicalId":100268,"journal":{"name":"Clinical eHealth","volume":"7 ","pages":"Pages 92-105"},"PeriodicalIF":0.0,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S258891412400008X/pdfft?md5=218e1f17d86b332d375ee1229471e106&pid=1-s2.0-S258891412400008X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141702840","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":"IoMT Tsukamoto Type-2 fuzzy expert system for tuberculosis and Alzheimer’s disease","authors":"M.K. Sharma ,&nbsp;Nitesh Dhiman ,&nbsp;Ajendra Sharma ,&nbsp;Tarun Kumar","doi":"10.1016/j.ceh.2024.05.002","DOIUrl":"10.1016/j.ceh.2024.05.002","url":null,"abstract":"<div><p>Accurate disease monitoring is an extremely time-consuming task for medical experts and technocrats involved, requiring technical support for diagnostic systems. To overcome this situation, we developed an Internet of Medical Things (IoMT) based on Tsukamoto Type 2 Fuzzy Inference System (TT2FIS) that can easily handle diagnostic and predictive aspects in the medical field. In the proposed system, we developed a Tsukamoto type 2 fuzzy inference system that takes the patient’s symptoms as input factors and the medical device as the output factor of the result. The aim of this work is to demonstrate the usefulness of type 2 fuzzy sets in Tuberculosis and Alzheimer’s disease diagnostic system. Numerical calculations are also performed to illustrate the applicability of the proposed method. A validation of the proposed derivation of the proposed IoMT model is also discussed in the results and conclusions section.</p></div>","PeriodicalId":100268,"journal":{"name":"Clinical eHealth","volume":"7 ","pages":"Pages 77-91"},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2588914124000078/pdfft?md5=01ce48d625ccd9df58e5d5a4a9fdbd41&pid=1-s2.0-S2588914124000078-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141050957","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}