INTRODUCTION: Nosocomial infection is a critical global public health issue. The education of medical personnel can effectively enhance compliance with nosocomial infection protocols and reduce the incidence of such infections. However, the current training provided to third-party staff is inadequate, necessitating an urgent enhancement of their knowledge on nosocomial infection through effective and tailored training programs.
OBJECTIVES: The objective is to establish a hierarchical training system for nosocomial infection, customized to meet the specific requirements of hospitals at all levels, and evaluate its efficacy.
METHODS: A questionnaire survey was conducted among third-party staff members at hospitals of different levels to assess their understanding of nosocomial infection prevention measures. Based on the survey results, a hierarchical training system was developed for nosocomial infection among the participants. After the training, a post-training assessment was carried out to evaluate the participants' comprehension of nosocomial infections.
RESULTS: A total of 561 third-party employees participated in the baseline hospital infection knowledge questionnaire. The baseline findings unveiled disparities in the extent to which third-party staff members across various tiers of medical institutions have mastered their knowledge on nosocomial infections. After undergoing hierarchical training, the deficiencies of hospitals at all levels have been rectified, thereby effectively enhancing the level of knowledge regarding nosocomial infections among third-party personnel. The results of multivariate analysis indicate that individuals with limited work experience should enhance their training in medical waste disposal and acquire a deeper understanding of personal protection measures related to nosocomial infections. Moreover, infrequent annual training sessions may impede the comprehension of nosocomial infection among third-party staff.
CONCLUSION: The knowledge of hospital infection among third-party staff at all levels of medical institutions exhibits varying deficiencies. Implementing a hierarchical training approach is a meaningful strategy that effectively enhances the level of hospital infection knowledge among these staff members.
{"title":"Construction and effect evaluation of a hierarchical training system for nosocomial infection based on hospitals at all levels","authors":"R Chang, D Meng","doi":"10.4108/eetpht.9.4293","DOIUrl":"https://doi.org/10.4108/eetpht.9.4293","url":null,"abstract":"INTRODUCTION: Nosocomial infection is a critical global public health issue. The education of medical personnel can effectively enhance compliance with nosocomial infection protocols and reduce the incidence of such infections. However, the current training provided to third-party staff is inadequate, necessitating an urgent enhancement of their knowledge on nosocomial infection through effective and tailored training programs.
 OBJECTIVES: The objective is to establish a hierarchical training system for nosocomial infection, customized to meet the specific requirements of hospitals at all levels, and evaluate its efficacy.
 METHODS: A questionnaire survey was conducted among third-party staff members at hospitals of different levels to assess their understanding of nosocomial infection prevention measures. Based on the survey results, a hierarchical training system was developed for nosocomial infection among the participants. After the training, a post-training assessment was carried out to evaluate the participants' comprehension of nosocomial infections.
 RESULTS: A total of 561 third-party employees participated in the baseline hospital infection knowledge questionnaire. The baseline findings unveiled disparities in the extent to which third-party staff members across various tiers of medical institutions have mastered their knowledge on nosocomial infections. After undergoing hierarchical training, the deficiencies of hospitals at all levels have been rectified, thereby effectively enhancing the level of knowledge regarding nosocomial infections among third-party personnel. The results of multivariate analysis indicate that individuals with limited work experience should enhance their training in medical waste disposal and acquire a deeper understanding of personal protection measures related to nosocomial infections. Moreover, infrequent annual training sessions may impede the comprehension of nosocomial infection among third-party staff.
 CONCLUSION: The knowledge of hospital infection among third-party staff at all levels of medical institutions exhibits varying deficiencies. Implementing a hierarchical training approach is a meaningful strategy that effectively enhances the level of hospital infection knowledge among these staff members.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"73 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135932985","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}
Loveleen Kumar, C Anitha, Venka Namdev Ghodke, N Nithya, Vinayak A Drave, Azmath Farhana
In many parts of the world, heart disease is the leading cause of mortality diagnosis is critical Towards Efficient Medical Care and prevention of heart attacks and other cardiac events. Deep learning algorithms have shown promise in accurately predicting heart disease based on medical data, including electrocardiograms (ECGs) and other health metrics. With this abstract, Specifically, we advocate for deep learning algorithm in accordance with CNNs for Deep Learning effective heart disease prediction. The proposed method uses a combination of ECG signals, demographic data, and clinical measurements Identifying risk factors for cardiovascular disease in patients. The proposed CNN-based model includes several layers, such as convolutional ones, pooling ones, and fully connected ones. The model takes input in the form of ECG signals, along with demographic data and clinical measurements, and uses convolutional layers to get features out of raw data. To lessen the effect of this, pooling layers are dimensionality of the extracted features, while layers that are already completely linked to estimate the risk of cardiovascular disease based on the extracted features. Training and evaluating the suggested model, We consulted a broad pool of ECG signals together with patient clinical data, both with and without heart disease. Training and test sets were created from the dataset testing arrays, and the prototype was trained using backpropagation and stochastic gradient descent. The model was evaluated using standard quantitative indicators such the F1 score, recall rate, and accuracy rate. The outcomes of experiments demonstrate the suggested CNN-based model achieves high accuracy in predicting heart disease, with an overall accuracy of over 90%. The model also outperforms several alternatives to classical techniques for heart disease prediction, including the more conventional forms of AI algorithms different forms of deep learning models. In conclusion, the proposed deep learning algorithm based on CNNs shows great potential for effective heart disease prediction. The model can be integrated into healthcare systems to provide accurate and timely diagnosis and treatment for patients with heart disease. Further research can be done to optimize the model's performance and test its effectiveness on different patient populations.
{"title":"Deep Learning Based Healthcare Method for Effective Heart Disease Prediction","authors":"Loveleen Kumar, C Anitha, Venka Namdev Ghodke, N Nithya, Vinayak A Drave, Azmath Farhana","doi":"10.4108/eetpht.9.4283","DOIUrl":"https://doi.org/10.4108/eetpht.9.4283","url":null,"abstract":"In many parts of the world, heart disease is the leading cause of mortality diagnosis is critical Towards Efficient Medical Care and prevention of heart attacks and other cardiac events. Deep learning algorithms have shown promise in accurately predicting heart disease based on medical data, including electrocardiograms (ECGs) and other health metrics. With this abstract, Specifically, we advocate for deep learning algorithm in accordance with CNNs for Deep Learning effective heart disease prediction. The proposed method uses a combination of ECG signals, demographic data, and clinical measurements Identifying risk factors for cardiovascular disease in patients. The proposed CNN-based model includes several layers, such as convolutional ones, pooling ones, and fully connected ones. The model takes input in the form of ECG signals, along with demographic data and clinical measurements, and uses convolutional layers to get features out of raw data. To lessen the effect of this, pooling layers are dimensionality of the extracted features, while layers that are already completely linked to estimate the risk of cardiovascular disease based on the extracted features. Training and evaluating the suggested model, We consulted a broad pool of ECG signals together with patient clinical data, both with and without heart disease. Training and test sets were created from the dataset testing arrays, and the prototype was trained using backpropagation and stochastic gradient descent. The model was evaluated using standard quantitative indicators such the F1 score, recall rate, and accuracy rate. The outcomes of experiments demonstrate the suggested CNN-based model achieves high accuracy in predicting heart disease, with an overall accuracy of over 90%. The model also outperforms several alternatives to classical techniques for heart disease prediction, including the more conventional forms of AI algorithms different forms of deep learning models. In conclusion, the proposed deep learning algorithm based on CNNs shows great potential for effective heart disease prediction. The model can be integrated into healthcare systems to provide accurate and timely diagnosis and treatment for patients with heart disease. Further research can be done to optimize the model's performance and test its effectiveness on different patient populations.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"23 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135872282","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}
Anil V Turukmane, Sagar Pande, Vaidehi Bedekar, Aditya Kadam
An enormous amount of heterogeneous Textual Medical Knowledge (TMK), which is crucial to healthcare information systems, has been produced by the explosion of healthcare information. Existing efforts to incorporate and use textual medical knowledge primarily concentrate on setting up simple links and pay less attention to creating computers comprehend information accurately and rapidly. Self-diagnostic symptom checkers and clinical decision support systems have seen a significant rise in demand in recent years. Existing systems rely on knowledge bases that are either automatically generated using straightforward paired statistics or manually constructed through a time-consuming procedure. The study explored process to learn textual data, linking disease and symptoms from web-based documents. Medical concepts were scrapped and collected from different web-based sources. The research aims to generate a disease- symptom-diagnosis knowledge graph (DSDKG), with the help of web-based documents. Moreover, the knowledge graph is fed in to Graph neural network with Attention Mechanism (GAT) for learning the nodes and edges relationships. . Lastly Generative Pretrained Transformer 2 (GPT2) all enclosed in a Reinforced learning environment, is used on the trained model to generate text based recommendations.
{"title":"Reinforced Hybrid Graph Transformer for Medical Recommendations","authors":"Anil V Turukmane, Sagar Pande, Vaidehi Bedekar, Aditya Kadam","doi":"10.4108/eetpht.9.4285","DOIUrl":"https://doi.org/10.4108/eetpht.9.4285","url":null,"abstract":"An enormous amount of heterogeneous Textual Medical Knowledge (TMK), which is crucial to healthcare information systems, has been produced by the explosion of healthcare information. Existing efforts to incorporate and use textual medical knowledge primarily concentrate on setting up simple links and pay less attention to creating computers comprehend information accurately and rapidly. Self-diagnostic symptom checkers and clinical decision support systems have seen a significant rise in demand in recent years. Existing systems rely on knowledge bases that are either automatically generated using straightforward paired statistics or manually constructed through a time-consuming procedure. The study explored process to learn textual data, linking disease and symptoms from web-based documents. Medical concepts were scrapped and collected from different web-based sources. The research aims to generate a disease- symptom-diagnosis knowledge graph (DSDKG), with the help of web-based documents. Moreover, the knowledge graph is fed in to Graph neural network with Attention Mechanism (GAT) for learning the nodes and edges relationships. . Lastly Generative Pretrained Transformer 2 (GPT2) all enclosed in a Reinforced learning environment, is used on the trained model to generate text based recommendations.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"23 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135870003","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}
G Sucharitha, G Sai Aditya, J Varsha, G Sai Nikhil
Blockchain technology has emerged as a crucial tool for ensuring security and reliability in various domains, particularly in healthcare. In this study, we utilize blockchain to establish an append-only chain of transaction blocks, ensuring the integrity and security of patient medical records. By employing blockchain, we aim to safeguard patient data, grant specific clinicians’ access to medical records, and ensure user privacy. The doctor will only receive prescription information after the patient has granted access, ensuring comprehensive protection for both parties. Consensus mechanisms within the blockchain guarantee consistency among blocks and require agreement from existing nodes before adding new transactions. Traditional healthcare systems often result in delays in data exchange and strict restrictions on access due to concerns about sensitive data leakage. By integrating blockchain technology into healthcare records and data, this article seeks to enhance data sharing while mitigating the risks of data tampering and security breaches.
{"title":"Electronic Medical Records Using Blockchain Technology","authors":"G Sucharitha, G Sai Aditya, J Varsha, G Sai Nikhil","doi":"10.4108/eetpht.9.4284","DOIUrl":"https://doi.org/10.4108/eetpht.9.4284","url":null,"abstract":"Blockchain technology has emerged as a crucial tool for ensuring security and reliability in various domains, particularly in healthcare. In this study, we utilize blockchain to establish an append-only chain of transaction blocks, ensuring the integrity and security of patient medical records. By employing blockchain, we aim to safeguard patient data, grant specific clinicians’ access to medical records, and ensure user privacy. The doctor will only receive prescription information after the patient has granted access, ensuring comprehensive protection for both parties. Consensus mechanisms within the blockchain guarantee consistency among blocks and require agreement from existing nodes before adding new transactions. Traditional healthcare systems often result in delays in data exchange and strict restrictions on access due to concerns about sensitive data leakage. By integrating blockchain technology into healthcare records and data, this article seeks to enhance data sharing while mitigating the risks of data tampering and security breaches.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"1037 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135863174","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}
Detecting skin cancer at the preliminary stage is a challenging issue, and is of high significance for the affected patients. Here, Fractional Gazelle Optimization Algorithm_Convolutional Neural Network based Transfer Learning with Visual Geometric Group-16 (FGOA_CNN based TL with VGG-16) is introduced for primary prediction of skin cancer. Initially, input skin data is acquired from the database and it is fed to the data preprocessing. Here, data preprocessing is done by missing value imputation and linear normalization. Once data is preprocessed, the feature selection is done by the proposed FGOA. Here, the proposed FGOA is an integration of Fractional Calculus (FC) and Gazelle Optimization Algorithm (GOA). After that, skin cancer detection is carried out using CNN-based TL with VGG-16, which is trained by the proposed FGOA and it is an integration of FC and GOA. Moreover, the efficiency of the proposed FGOA_ CNN-based TL with VGG-16 is examined based on five various metrics, like accuracy, Positive Predictive Value (PPV), True Positive Rate (TPR), True Negative Rate (TNR), and Negative Predictive Value (NPV) and the outcome of experimentation reveals that the devised work is highly superior and has attained maximal values of metrics is 92.65%, 90.35%, 91.48%, 93.56%, 90.77% respectively.
{"title":"novel skin cancer Detection based transfer learning with optimization algorithm using Dermatology Dataset","authors":"Polasi Sudhakar, Suresh Chandra Satapathy","doi":"10.4108/eetpht.9.4277","DOIUrl":"https://doi.org/10.4108/eetpht.9.4277","url":null,"abstract":"Detecting skin cancer at the preliminary stage is a challenging issue, and is of high significance for the affected patients. Here, Fractional Gazelle Optimization Algorithm_Convolutional Neural Network based Transfer Learning with Visual Geometric Group-16 (FGOA_CNN based TL with VGG-16) is introduced for primary prediction of skin cancer. Initially, input skin data is acquired from the database and it is fed to the data preprocessing. Here, data preprocessing is done by missing value imputation and linear normalization. Once data is preprocessed, the feature selection is done by the proposed FGOA. Here, the proposed FGOA is an integration of Fractional Calculus (FC) and Gazelle Optimization Algorithm (GOA). After that, skin cancer detection is carried out using CNN-based TL with VGG-16, which is trained by the proposed FGOA and it is an integration of FC and GOA. Moreover, the efficiency of the proposed FGOA_ CNN-based TL with VGG-16 is examined based on five various metrics, like accuracy, Positive Predictive Value (PPV), True Positive Rate (TPR), True Negative Rate (TNR), and Negative Predictive Value (NPV) and the outcome of experimentation reveals that the devised work is highly superior and has attained maximal values of metrics is 92.65%, 90.35%, 91.48%, 93.56%, 90.77% respectively.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"462 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136023404","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}
Fabrizio Del Carpio-Delgado, David Hugo Bernedo-Moreira, Antony Paul Espiritu-Martinez, José Luis Aguilar-Cruzado, Carlos Eduardo Joo-García, Marilí Ruth Mamani-Laura, Rafael Romero-Carazas
Introduction: Over the past decade, telemedicine and mobile health have experienced significant growth, becoming essential tools for healthcare in an increasingly digitized world. This research focuses on exploring how these technologies have improved the accessibility, efficiency and quality of healthcare, despite challenges related to data security and equity of access, with the aim of understanding their impact and potential in modern healthcare.
Methods: a PubMed search was performed using the keywords "Telemedicine" and "mHealth" to find relevant studies on its application in clinical practice, with inclusion criteria covering articles in Spanish and English published between 2018 and 2023, freely available. The PRISMA workflow was followed to review and synthesize key findings and trends in this field.
Result: the contribution of countries such as China, Australia and the United States in telemedicine and mobile health, with a focus on cardiovascular diseases and metabolic disorders, is highlighted. The positive impact on chronic diseases, mental health, physical activity and treatment adherence is highlighted, but the need to adapt interventions and lack of COVID-19 studies is emphasized.
Conclusions: Telemedicine addresses a variety of pathologies, focusing on chronic diseases, with China leading in contributions. eHealth seeks to improve health outcomes and reduce the burden of disease.
{"title":"Telemedicine and eHealth Solutions in Clinical Practice","authors":"Fabrizio Del Carpio-Delgado, David Hugo Bernedo-Moreira, Antony Paul Espiritu-Martinez, José Luis Aguilar-Cruzado, Carlos Eduardo Joo-García, Marilí Ruth Mamani-Laura, Rafael Romero-Carazas","doi":"10.4108/eetpht.9.4272","DOIUrl":"https://doi.org/10.4108/eetpht.9.4272","url":null,"abstract":"Introduction: Over the past decade, telemedicine and mobile health have experienced significant growth, becoming essential tools for healthcare in an increasingly digitized world. This research focuses on exploring how these technologies have improved the accessibility, efficiency and quality of healthcare, despite challenges related to data security and equity of access, with the aim of understanding their impact and potential in modern healthcare. 
 Methods: a PubMed search was performed using the keywords \"Telemedicine\" and \"mHealth\" to find relevant studies on its application in clinical practice, with inclusion criteria covering articles in Spanish and English published between 2018 and 2023, freely available. The PRISMA workflow was followed to review and synthesize key findings and trends in this field. 
 Result: the contribution of countries such as China, Australia and the United States in telemedicine and mobile health, with a focus on cardiovascular diseases and metabolic disorders, is highlighted. The positive impact on chronic diseases, mental health, physical activity and treatment adherence is highlighted, but the need to adapt interventions and lack of COVID-19 studies is emphasized. 
 Conclusions: Telemedicine addresses a variety of pathologies, focusing on chronic diseases, with China leading in contributions. eHealth seeks to improve health outcomes and reduce the burden of disease.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136067711","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}
Bryan Tito-Llana, Nils Riveros-Torre, Brian Meneses-Claudio, Monica Auccacusi-Kañahuire
During breast cancer treatment, patients face various physical and psychological problems. However, a promising solution has been found in the use of virtual reality as a tool to address these problems. Our goal was to identify the most common problems and symptoms during treatment, as well as investigate the effectiveness of virtual reality in addressing them. We also set out to determine if there are any disadvantages associated with using this system. To this end, we conducted a systematic review using a non-experimental, descriptive, and qualitative-quantitative approach. 20 open access articles were selected in the Scopus database, following established inclusion and exclusion criteria. The results revealed that anxiety and pain are the most common symptoms experienced during breast cancer treatment. Regarding the effectiveness of virtual reality to treat these symptoms, differences were found: a significant impact on anxiety was observed (p < 0.001), but no significant impact on pain was found (p < 0.07). In addition, only three studies mentioned the possible presence of cyberdisease as an obstacle. In conclusion, anxiety and pain are the most common symptoms during breast cancer treatment. Virtual reality shows high efficacy in managing anxiety, but its effectiveness in pain management is limited. In addition, technological advances appear to have reduced the occurrence of cyberdisease and associated drawbacks, although little information is available in the studies reviewed.
{"title":"Virtual reality for physical and psychological improvement during the treatment of patients with breast cancer: Systematic review","authors":"Bryan Tito-Llana, Nils Riveros-Torre, Brian Meneses-Claudio, Monica Auccacusi-Kañahuire","doi":"10.4108/eetpht.9.4275","DOIUrl":"https://doi.org/10.4108/eetpht.9.4275","url":null,"abstract":"During breast cancer treatment, patients face various physical and psychological problems. However, a promising solution has been found in the use of virtual reality as a tool to address these problems. Our goal was to identify the most common problems and symptoms during treatment, as well as investigate the effectiveness of virtual reality in addressing them. We also set out to determine if there are any disadvantages associated with using this system. To this end, we conducted a systematic review using a non-experimental, descriptive, and qualitative-quantitative approach. 20 open access articles were selected in the Scopus database, following established inclusion and exclusion criteria. The results revealed that anxiety and pain are the most common symptoms experienced during breast cancer treatment. Regarding the effectiveness of virtual reality to treat these symptoms, differences were found: a significant impact on anxiety was observed (p < 0.001), but no significant impact on pain was found (p < 0.07). In addition, only three studies mentioned the possible presence of cyberdisease as an obstacle. In conclusion, anxiety and pain are the most common symptoms during breast cancer treatment. Virtual reality shows high efficacy in managing anxiety, but its effectiveness in pain management is limited. In addition, technological advances appear to have reduced the occurrence of cyberdisease and associated drawbacks, although little information is available in the studies reviewed.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136104461","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}
Fabrizio Del Carpio-Delgado, Rafael Romero-Carazas, Gustavo Eduardo Pino-Espinoza, Linda Flor Villa-Ricapa, Eva Luisa Núñez-Palacios, Margoth Marleny Aguilar-Cuevas, Antony Paul Espiritu-Martinez
Introduction: Telemedicine revolutionizes health care by removing geographic barriers and improving access. Although it faces challenges such as privacy and equity of access, bibliometric studies are crucial to understanding its impact and guiding future research. Methods: The study used a descriptive bibliometric methodology based on the Scopus database to analyze telemedicine research in Latin America over the last ten years, resulting in 2105 academic articles. Tools such as SciVal and VOSviewer were used to perform quantitative and visual analyses of the publications, including creating bibliometric maps. Result: From 2013-2022, 2105 academic articles on telemedicine were published in Latin America, with a significant impact on the health field. A particular focus is observed on topics such as psychological support, COVID-19, imaging diagnosis and cancer treatment, highlighting the relevance of telemedicine in these contexts. In addition, international collaboration was associated with a more significant impact. Brazil produced articles, and the importance of collaboration between academia and the corporate sector in this field was highlighted. Conclusions: Telemedicine has grown in Latin America, especially during the pandemic, offering benefits such as psychological support and expedited diagnosis and treatment; however, it faces challenges such as a lack of equitable access to technology and concerns about data privacy. Brazil leads scientific production in this field.
{"title":"Telemedicine in Latin America: a bibliometric analysis","authors":"Fabrizio Del Carpio-Delgado, Rafael Romero-Carazas, Gustavo Eduardo Pino-Espinoza, Linda Flor Villa-Ricapa, Eva Luisa Núñez-Palacios, Margoth Marleny Aguilar-Cuevas, Antony Paul Espiritu-Martinez","doi":"10.4108/eetpht.9.4273","DOIUrl":"https://doi.org/10.4108/eetpht.9.4273","url":null,"abstract":"Introduction: Telemedicine revolutionizes health care by removing geographic barriers and improving access. Although it faces challenges such as privacy and equity of access, bibliometric studies are crucial to understanding its impact and guiding future research. Methods: The study used a descriptive bibliometric methodology based on the Scopus database to analyze telemedicine research in Latin America over the last ten years, resulting in 2105 academic articles. Tools such as SciVal and VOSviewer were used to perform quantitative and visual analyses of the publications, including creating bibliometric maps. Result: From 2013-2022, 2105 academic articles on telemedicine were published in Latin America, with a significant impact on the health field. A particular focus is observed on topics such as psychological support, COVID-19, imaging diagnosis and cancer treatment, highlighting the relevance of telemedicine in these contexts. In addition, international collaboration was associated with a more significant impact. Brazil produced articles, and the importance of collaboration between academia and the corporate sector in this field was highlighted. Conclusions: Telemedicine has grown in Latin America, especially during the pandemic, offering benefits such as psychological support and expedited diagnosis and treatment; however, it faces challenges such as a lack of equitable access to technology and concerns about data privacy. Brazil leads scientific production in this field.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"43 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136103453","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}
Leonardo Enco-Jáuregui, Brian Meneses-Claudio, Monica Auccacusi-Kañahuire
As the digital age advances, the internet has become a vital source of information and social participation; And with it, opportunities and benefits are manifested that can only be obtained through this single means. That is why it is essential to ensure that everyone can have equal access and opportunities when browsing the web. This review focuses on investigating the current state of knowledge of web accessibility for people with dyslexia. To achieve this, various computer solutions, design recommendations and study of web accessibility guidelines were reviewed, whose main objective is to improve the experience of users with dyslexia when browsing the web. A total of 120 original articles were extracted from the Scopus database, of which 22 studies met the inclusion criteria. The results showed that many of the web design customization options provided by these solutions were able to improve the web browsing and reading experience for people with dyslexia. In conclusion, this RSL allowed to identify a large number of software-based solutions and design recommendations to provide accessibility to people with dyslexia. Among the most important factors considered in these studies is the organization of content, typography and color contrast. Additionally, it is important to highlight the need to continue adjusting these proposals according to the different opinions and suggestions provided by the participants during the evaluations. And finally, it is recommended to obtain larger samples of participants so that, in this way, more representative results can be obtained during future research.
{"title":"Web accessibility for people with dyslexia: A systematic literature review","authors":"Leonardo Enco-Jáuregui, Brian Meneses-Claudio, Monica Auccacusi-Kañahuire","doi":"10.4108/eetpht.9.4274","DOIUrl":"https://doi.org/10.4108/eetpht.9.4274","url":null,"abstract":"As the digital age advances, the internet has become a vital source of information and social participation; And with it, opportunities and benefits are manifested that can only be obtained through this single means. That is why it is essential to ensure that everyone can have equal access and opportunities when browsing the web. This review focuses on investigating the current state of knowledge of web accessibility for people with dyslexia. To achieve this, various computer solutions, design recommendations and study of web accessibility guidelines were reviewed, whose main objective is to improve the experience of users with dyslexia when browsing the web. A total of 120 original articles were extracted from the Scopus database, of which 22 studies met the inclusion criteria. The results showed that many of the web design customization options provided by these solutions were able to improve the web browsing and reading experience for people with dyslexia. In conclusion, this RSL allowed to identify a large number of software-based solutions and design recommendations to provide accessibility to people with dyslexia. Among the most important factors considered in these studies is the organization of content, typography and color contrast. Additionally, it is important to highlight the need to continue adjusting these proposals according to the different opinions and suggestions provided by the participants during the evaluations. And finally, it is recommended to obtain larger samples of participants so that, in this way, more representative results can be obtained during future research.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136316761","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}
Modern, highly developed technology has impacted reputable procedures in the medical and healthcare industries. Smart healthcare prediction to the senior sick patient is not only for quick access to data but also to get dependable treatment in an accurate prediction by healthcare service provider. smart health prediction helps in the identification of numerous diseases. Based on patient experience, Deep learning technology provides a robust application space in the medical sector for health disease prediction problems by applying deep learning techniques to analyze various symptoms. In order to classify things and make precise predictions about diseases, deep learning techniques are utilized. people's health will be more secure, medical care will be of a higher caliber, and personal information will be kept more secret. As deep learning algorithms become more widely used to construct an interactive smart healthcare prediction and evaluation model on the basis of the deep learning model, CNN is upgraded. Advanced deep learning algorithms combined with multi-mode approaches and resting-state functional magnetic resonance represent an innovative approach that researchers have taken. A DL structure for the programmed ID ASD using highlights separated from the corpus callosum and cerebrum volume from the Stand dataset is proposed. Imaging is used to reveal hidden diseased brain connectome patterns to find diagnostic and prognostic indicators.
{"title":"Autism Spectrum Disorder Classification Using Machine Learning and Deep Learning- A Survey","authors":"Reeja S R, Sunkara Mounika","doi":"10.4108/eetpht.9.4240","DOIUrl":"https://doi.org/10.4108/eetpht.9.4240","url":null,"abstract":"Modern, highly developed technology has impacted reputable procedures in the medical and healthcare industries. Smart healthcare prediction to the senior sick patient is not only for quick access to data but also to get dependable treatment in an accurate prediction by healthcare service provider. smart health prediction helps in the identification of numerous diseases. Based on patient experience, Deep learning technology provides a robust application space in the medical sector for health disease prediction problems by applying deep learning techniques to analyze various symptoms. In order to classify things and make precise predictions about diseases, deep learning techniques are utilized. people's health will be more secure, medical care will be of a higher caliber, and personal information will be kept more secret. As deep learning algorithms become more widely used to construct an interactive smart healthcare prediction and evaluation model on the basis of the deep learning model, CNN is upgraded. Advanced deep learning algorithms combined with multi-mode approaches and resting-state functional magnetic resonance represent an innovative approach that researchers have taken. A DL structure for the programmed ID ASD using highlights separated from the corpus callosum and cerebrum volume from the Stand dataset is proposed. Imaging is used to reveal hidden diseased brain connectome patterns to find diagnostic and prognostic indicators.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"22 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134908432","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}
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