Pub Date : 2024-07-09DOI: 10.3390/informatics11030044
Reza Torkman, A. Ghapanchi, Reza Ghanbarzadeh
Health information systems (HISs) are essential information systems used by organisations and individuals for various purposes. Past research has studied different types of HIS, such as rostering systems, Electronic Medical Records (EMRs), and Personal Health Records (PHRs). Although several past confirmatory studies have quantitatively examined EMR uptake by health professionals, there is a lack of exploratory and qualitative studies that uncover various drivers of healthcare professionals’ uptake of EMRs. Applying an exploratory and qualitative approach, this study introduces various antecedents of healthcare professionals’ uptake of EMRs. This study conducted 78 semi-structured, open-ended interviews with 15 groups of healthcare professional users of EMRs in two large Australian hospitals. Data analysis of qualitative data resulted in proposing a framework comprising 23 factors impacting healthcare professionals’ uptake of EMRs, which are categorised into ten main categories: perceived benefits of EMR, perceived difficulties, hardware/software compatibility, job performance uncertainty, ease of operation, perceived risk, assistance society, user confidence, organisational support, and technological support. Our findings have important implications for various practitioner groups, such as healthcare policymakers, hospital executives, hospital middle and line managers, hospitals’ IT departments, and healthcare professionals using EMRs. Implications of the findings for researchers and practitioners are provided herein in detail.
{"title":"A Framework for Antecedents to Health Information Systems Uptake by Healthcare Professionals: An Exploratory Study of Electronic Medical Records","authors":"Reza Torkman, A. Ghapanchi, Reza Ghanbarzadeh","doi":"10.3390/informatics11030044","DOIUrl":"https://doi.org/10.3390/informatics11030044","url":null,"abstract":"Health information systems (HISs) are essential information systems used by organisations and individuals for various purposes. Past research has studied different types of HIS, such as rostering systems, Electronic Medical Records (EMRs), and Personal Health Records (PHRs). Although several past confirmatory studies have quantitatively examined EMR uptake by health professionals, there is a lack of exploratory and qualitative studies that uncover various drivers of healthcare professionals’ uptake of EMRs. Applying an exploratory and qualitative approach, this study introduces various antecedents of healthcare professionals’ uptake of EMRs. This study conducted 78 semi-structured, open-ended interviews with 15 groups of healthcare professional users of EMRs in two large Australian hospitals. Data analysis of qualitative data resulted in proposing a framework comprising 23 factors impacting healthcare professionals’ uptake of EMRs, which are categorised into ten main categories: perceived benefits of EMR, perceived difficulties, hardware/software compatibility, job performance uncertainty, ease of operation, perceived risk, assistance society, user confidence, organisational support, and technological support. Our findings have important implications for various practitioner groups, such as healthcare policymakers, hospital executives, hospital middle and line managers, hospitals’ IT departments, and healthcare professionals using EMRs. Implications of the findings for researchers and practitioners are provided herein in detail.","PeriodicalId":507941,"journal":{"name":"Informatics","volume":"58 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141664789","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}
Pub Date : 2024-07-03DOI: 10.3390/informatics11030043
Choyeal Park, Jikyeong Park
This study examined the awareness of the EMR certification system among employees of public and private hospitals that have obtained EMR certification. It also assessed how this awareness impacted the evaluation of EMR interoperability. The objective of this study is to contribute to the stable adoption and further development of EMR system certification in Korea. Data were collected through 3600 questionnaires distributed over three years from 2021 to 2023. After excluding 24 questionnaires owing to missing values or insincere responses, 3576 responses were analyzed. The analysis involved descriptive statistics, cross-tabulation, t-tests, ANOVA, and multiple regression using SPSS 26.0. The significance level (α) for statistical tests was set at 0.05. This study revealed differences in awareness of EMR system certification and interoperability among hospital employees. In both public and private hospitals, awareness of the EMR system certification positively influences the evaluation of interoperability.
{"title":"Impact of Hospital Employees’ Awareness of the EMR System Certification on Interoperability Evaluation: Comparison of Public and Private Hospitals","authors":"Choyeal Park, Jikyeong Park","doi":"10.3390/informatics11030043","DOIUrl":"https://doi.org/10.3390/informatics11030043","url":null,"abstract":"This study examined the awareness of the EMR certification system among employees of public and private hospitals that have obtained EMR certification. It also assessed how this awareness impacted the evaluation of EMR interoperability. The objective of this study is to contribute to the stable adoption and further development of EMR system certification in Korea. Data were collected through 3600 questionnaires distributed over three years from 2021 to 2023. After excluding 24 questionnaires owing to missing values or insincere responses, 3576 responses were analyzed. The analysis involved descriptive statistics, cross-tabulation, t-tests, ANOVA, and multiple regression using SPSS 26.0. The significance level (α) for statistical tests was set at 0.05. This study revealed differences in awareness of EMR system certification and interoperability among hospital employees. In both public and private hospitals, awareness of the EMR system certification positively influences the evaluation of interoperability.","PeriodicalId":507941,"journal":{"name":"Informatics","volume":"89 s377","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141682646","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}
Pub Date : 2024-05-17DOI: 10.3390/informatics11020033
Akoramurthy Balasubramaniam, B. Surendiran
A significant increase in the demand for quality healthcare has resulted from people becoming more aware of health issues. With blockchain, healthcare providers may safely share patient information electronically, which is especially important given the sensitive nature of the data contained inside them. However, flaws in the current blockchain design have surfaced since the dawn of quantum computing systems. The study proposes a novel quantum-inspired blockchain system (Qchain) and constructs a unique entangled quantum medical record (EQMR) system with an emphasis on privacy and security. This Qchain relies on entangled states to connect its blocks. The automated production of the chronology indicator reduces storage capacity requirements by connecting entangled BloQ (blocks with quantum properties) to controlled activities. We use one qubit to store the hash value of each block. A lot of information regarding the quantum internet is included in the protocol for the entangled quantum medical record (EQMR). The EQMR can be accessed in Medical Internet of Things (M-IoT) systems that are kept private and secure, and their whereabouts can be monitored in the event of an emergency. The protocol also uses quantum authentication in place of more conventional methods like encryption and digital signatures. Mathematical research shows that the quantum converged blockchain (QCB) is highly safe against attacks such as external attacks, intercept measure -repeat attacks, and entanglement measure attacks. We present the reliability and auditability evaluations of the entangled BloQ, along with the quantum circuit design for computing the hash value. There is also a comparison between the suggested approach and several other quantum blockchain designs.
{"title":"QUMA: Quantum Unified Medical Architecture Using Blockchain","authors":"Akoramurthy Balasubramaniam, B. Surendiran","doi":"10.3390/informatics11020033","DOIUrl":"https://doi.org/10.3390/informatics11020033","url":null,"abstract":"A significant increase in the demand for quality healthcare has resulted from people becoming more aware of health issues. With blockchain, healthcare providers may safely share patient information electronically, which is especially important given the sensitive nature of the data contained inside them. However, flaws in the current blockchain design have surfaced since the dawn of quantum computing systems. The study proposes a novel quantum-inspired blockchain system (Qchain) and constructs a unique entangled quantum medical record (EQMR) system with an emphasis on privacy and security. This Qchain relies on entangled states to connect its blocks. The automated production of the chronology indicator reduces storage capacity requirements by connecting entangled BloQ (blocks with quantum properties) to controlled activities. We use one qubit to store the hash value of each block. A lot of information regarding the quantum internet is included in the protocol for the entangled quantum medical record (EQMR). The EQMR can be accessed in Medical Internet of Things (M-IoT) systems that are kept private and secure, and their whereabouts can be monitored in the event of an emergency. The protocol also uses quantum authentication in place of more conventional methods like encryption and digital signatures. Mathematical research shows that the quantum converged blockchain (QCB) is highly safe against attacks such as external attacks, intercept measure -repeat attacks, and entanglement measure attacks. We present the reliability and auditability evaluations of the entangled BloQ, along with the quantum circuit design for computing the hash value. There is also a comparison between the suggested approach and several other quantum blockchain designs.","PeriodicalId":507941,"journal":{"name":"Informatics","volume":"1 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140962176","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}
Pub Date : 2024-05-17DOI: 10.3390/informatics11020032
Fray L. Becerra-Suarez, Victor A. Tuesta-Monteza, Heber I. Mejia-Cabrera, Juan Arcila-Diaz
The Internet of Things (IoT) presents great potential in various fields such as home automation, healthcare, and industry, among others, but its infrastructure, the use of open source code, and lack of software updates make it vulnerable to cyberattacks that can compromise access to data and services, thus making it an attractive target for hackers. The complexity of cyberattacks has increased, posing a greater threat to public and private organizations. This study evaluated the performance of deep learning models for classifying cybersecurity attacks in IoT networks, using the CICIoT2023 dataset. Three architectures based on DNN, LSTM, and CNN were compared, highlighting their differences in layers and activation functions. The results show that the CNN architecture outperformed the others in accuracy and computational efficiency, with an accuracy rate of 99.10% for multiclass classification and 99.40% for binary classification. The importance of data standardization and proper hyperparameter selection is emphasized. These results demonstrate that the CNN-based model emerges as a promising option for detecting cyber threats in IoT environments, supporting the relevance of deep learning in IoT network security.
{"title":"Performance Evaluation of Deep Learning Models for Classifying Cybersecurity Attacks in IoT Networks","authors":"Fray L. Becerra-Suarez, Victor A. Tuesta-Monteza, Heber I. Mejia-Cabrera, Juan Arcila-Diaz","doi":"10.3390/informatics11020032","DOIUrl":"https://doi.org/10.3390/informatics11020032","url":null,"abstract":"The Internet of Things (IoT) presents great potential in various fields such as home automation, healthcare, and industry, among others, but its infrastructure, the use of open source code, and lack of software updates make it vulnerable to cyberattacks that can compromise access to data and services, thus making it an attractive target for hackers. The complexity of cyberattacks has increased, posing a greater threat to public and private organizations. This study evaluated the performance of deep learning models for classifying cybersecurity attacks in IoT networks, using the CICIoT2023 dataset. Three architectures based on DNN, LSTM, and CNN were compared, highlighting their differences in layers and activation functions. The results show that the CNN architecture outperformed the others in accuracy and computational efficiency, with an accuracy rate of 99.10% for multiclass classification and 99.40% for binary classification. The importance of data standardization and proper hyperparameter selection is emphasized. These results demonstrate that the CNN-based model emerges as a promising option for detecting cyber threats in IoT environments, supporting the relevance of deep learning in IoT network security.","PeriodicalId":507941,"journal":{"name":"Informatics","volume":"36 39","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140966397","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}
Pub Date : 2024-05-17DOI: 10.3390/informatics11020031
Franklin Parrales-Bravo, Rosangela Caicedo-Quiroz, Elianne Rodríguez-Larraburu, Julio Barzola-Monteses
While preeclampsia is the leading cause of maternal death in Guayas province (Ecuador), its causes have not yet been studied in depth. The objective of this research is to build a Bayesian network classifier to diagnose cases of preeclampsia while facilitating the understanding of the causes that generate this disease. Data for the years 2017 through 2023 were gathered retrospectively from medical histories of patients treated at “IESS Los Ceibos” hospital in Guayaquil, Ecuador. Naïve Bayes (NB), The Chow–Liu Tree-Augmented Naïve Bayes (TANcl), and Semi Naïve Bayes (FSSJ) algorithms have been considered for building explainable classification models. A proposed Non-Redundant Feature Selection approach (NoReFS) is proposed to perform the feature selection task. The model trained with the TANcl and NoReFS was the best of them, with an accuracy close to 90%. According to the best model, patients whose age is above 35 years, have a severe vaginal infection, live in a rural area, use tobacco, have a family history of diabetes, and have had a personal history of hypertension are those with a high risk of developing preeclampsia.
{"title":"ACME: A Classification Model for Explaining the Risk of Preeclampsia Based on Bayesian Network Classifiers and a Non-Redundant Feature Selection Approach","authors":"Franklin Parrales-Bravo, Rosangela Caicedo-Quiroz, Elianne Rodríguez-Larraburu, Julio Barzola-Monteses","doi":"10.3390/informatics11020031","DOIUrl":"https://doi.org/10.3390/informatics11020031","url":null,"abstract":"While preeclampsia is the leading cause of maternal death in Guayas province (Ecuador), its causes have not yet been studied in depth. The objective of this research is to build a Bayesian network classifier to diagnose cases of preeclampsia while facilitating the understanding of the causes that generate this disease. Data for the years 2017 through 2023 were gathered retrospectively from medical histories of patients treated at “IESS Los Ceibos” hospital in Guayaquil, Ecuador. Naïve Bayes (NB), The Chow–Liu Tree-Augmented Naïve Bayes (TANcl), and Semi Naïve Bayes (FSSJ) algorithms have been considered for building explainable classification models. A proposed Non-Redundant Feature Selection approach (NoReFS) is proposed to perform the feature selection task. The model trained with the TANcl and NoReFS was the best of them, with an accuracy close to 90%. According to the best model, patients whose age is above 35 years, have a severe vaginal infection, live in a rural area, use tobacco, have a family history of diabetes, and have had a personal history of hypertension are those with a high risk of developing preeclampsia.","PeriodicalId":507941,"journal":{"name":"Informatics","volume":"3 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140963548","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}
Pub Date : 2024-05-17DOI: 10.3390/informatics11020034
Enrique Maldonado Belmonte, Salvador Oton-Tortosa, J. Gutierrez-Martinez, Ana Castillo-Martinez
This paper describes the design and methodology for the development and validation of an intelligent model in the healthcare domain. The generated model relies on artificial intelligence techniques, aiming to predict the length of stay and survival rate of patients admitted to a critical care hospitalization unit with better results than predictive systems using scoring. The proposed methodology is based on the following stages: preliminary data analysis, analysis of the architecture and systems integration model, the big data model approach, information structure and process development, and the application of machine learning techniques. This investigation substantiates that automated machine learning models significantly surpass traditional prediction techniques for patient outcomes within critical care settings. Specifically, the machine learning-based model attained an F1 score of 0.351 for mortality forecast and 0.615 for length of stay, in contrast to the traditional scoring model’s F1 scores of 0.112 for mortality and 0.412 for length of stay. These results strongly support the advantages of integrating advanced computational techniques in critical healthcare environments. It is also shown that the use of integration architectures allows for improving the quality of the information by providing a data repository large enough to generate intelligent models. From a clinical point of view, obtaining more accurate results in the estimation of the ICU stay and survival offers the possibility of expanding the uses of the model to the identification and prioritization of patients who are candidates for admission to the ICU, as well as the management of patients with specific conditions.
本文介绍了开发和验证医疗保健领域智能模型的设计和方法。生成的模型依赖于人工智能技术，旨在预测重症监护病房住院患者的住院时间和存活率，其结果优于使用评分的预测系统。所提出的方法基于以下阶段：初步数据分析、架构和系统集成模型分析、大数据模型方法、信息结构和流程开发，以及机器学习技术的应用。这项调查证实，在重症监护环境下，自动机器学习模型大大超过了传统的患者预后预测技术。具体来说，基于机器学习的模型在死亡率预测方面的 F1 得分为 0.351，在住院时间方面的 F1 得分为 0.615，而传统评分模型在死亡率方面的 F1 得分为 0.112，在住院时间方面的 F1 得分为 0.412。这些结果有力地证明了在关键医疗环境中整合先进计算技术的优势。研究还表明，使用集成架构可以提供足够大的数据存储库来生成智能模型，从而提高信息质量。从临床角度来看，在估计重症监护室的住院时间和存活率方面获得更准确的结果，为将模型的用途扩展到识别和优先考虑重症监护室的候选病人以及管理患有特殊疾病的病人提供了可能性。
{"title":"An Intelligent Model and Methodology for Predicting Length of Stay and Survival in a Critical Care Hospital Unit","authors":"Enrique Maldonado Belmonte, Salvador Oton-Tortosa, J. Gutierrez-Martinez, Ana Castillo-Martinez","doi":"10.3390/informatics11020034","DOIUrl":"https://doi.org/10.3390/informatics11020034","url":null,"abstract":"This paper describes the design and methodology for the development and validation of an intelligent model in the healthcare domain. The generated model relies on artificial intelligence techniques, aiming to predict the length of stay and survival rate of patients admitted to a critical care hospitalization unit with better results than predictive systems using scoring. The proposed methodology is based on the following stages: preliminary data analysis, analysis of the architecture and systems integration model, the big data model approach, information structure and process development, and the application of machine learning techniques. This investigation substantiates that automated machine learning models significantly surpass traditional prediction techniques for patient outcomes within critical care settings. Specifically, the machine learning-based model attained an F1 score of 0.351 for mortality forecast and 0.615 for length of stay, in contrast to the traditional scoring model’s F1 scores of 0.112 for mortality and 0.412 for length of stay. These results strongly support the advantages of integrating advanced computational techniques in critical healthcare environments. It is also shown that the use of integration architectures allows for improving the quality of the information by providing a data repository large enough to generate intelligent models. From a clinical point of view, obtaining more accurate results in the estimation of the ICU stay and survival offers the possibility of expanding the uses of the model to the identification and prioritization of patients who are candidates for admission to the ICU, as well as the management of patients with specific conditions.","PeriodicalId":507941,"journal":{"name":"Informatics","volume":"1 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140963442","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}
Pub Date : 2024-05-16DOI: 10.3390/informatics11020030
Lin Cheng, Junping Xu, Younghwan Pan
As an innovative form in the digital age, VR art exhibitions have attracted increasing attention. This study aims to explore the key factors that influence visitors’ continuance intention to VR art exhibitions using the expectation confirmation model and experience economy theory and to explore ways to enhance visitor immersion in virtual environments. We conducted a quantitative study of 235 art professionals and enthusiasts, conducted using the partial least squares structural equation modeling (PLS-SEM), to examine the complex relationship between confirmation (CON), Perceived Usefulness (PU), Aesthetic Experiences (AE), Escapist Experiences (EE), Satisfaction (SAT), and Continuance Intention (CI). The results show that confirmation plays a key role in shaping PU, AE, and EE, which in turn positively affect visitors’ SAT. Among these factors, AE positively impacts PU, but EE have no impact. A comprehensive theoretical model was then constructed based on the findings. This research provides empirical support for designing and improving VR art exhibitions. It also sheds light on the application of expectation confirmation theory and experience economy theory in the art field to improve user experience and provides theoretical guidance for the sustainable development of virtual digital art environment.
作为数字时代的一种创新形式，VR 艺术展览越来越受到人们的关注。本研究旨在利用期望确认模型和体验经济理论，探讨影响参观者对 VR 艺术展览持续意向的关键因素，并探索如何增强参观者在虚拟环境中的沉浸感。我们采用偏最小二乘结构方程模型（PLS-SEM）对 235 名艺术专业人士和爱好者进行了定量研究，考察了确认（CON）、感知有用性（PU）、审美体验（AE）、逃避体验（EE）、满意度（SAT）和持续意向（CI）之间的复杂关系。结果表明，确认在形成 PU、AE 和 EE 方面起着关键作用，而这些因素反过来又对游客的 SAT 产生积极影响。在这些因素中，AE 对 PU 有积极影响，但 EE 没有影响。根据研究结果，我们构建了一个全面的理论模型。这项研究为设计和改进 VR 艺术展览提供了实证支持。研究还揭示了期望确认理论和体验经济理论在艺术领域的应用，以改善用户体验，并为虚拟数字艺术环境的可持续发展提供理论指导。
{"title":"Investigating User Experience of VR Art Exhibitions: The Impact of Immersion, Satisfaction, and Expectation Confirmation","authors":"Lin Cheng, Junping Xu, Younghwan Pan","doi":"10.3390/informatics11020030","DOIUrl":"https://doi.org/10.3390/informatics11020030","url":null,"abstract":"As an innovative form in the digital age, VR art exhibitions have attracted increasing attention. This study aims to explore the key factors that influence visitors’ continuance intention to VR art exhibitions using the expectation confirmation model and experience economy theory and to explore ways to enhance visitor immersion in virtual environments. We conducted a quantitative study of 235 art professionals and enthusiasts, conducted using the partial least squares structural equation modeling (PLS-SEM), to examine the complex relationship between confirmation (CON), Perceived Usefulness (PU), Aesthetic Experiences (AE), Escapist Experiences (EE), Satisfaction (SAT), and Continuance Intention (CI). The results show that confirmation plays a key role in shaping PU, AE, and EE, which in turn positively affect visitors’ SAT. Among these factors, AE positively impacts PU, but EE have no impact. A comprehensive theoretical model was then constructed based on the findings. This research provides empirical support for designing and improving VR art exhibitions. It also sheds light on the application of expectation confirmation theory and experience economy theory in the art field to improve user experience and provides theoretical guidance for the sustainable development of virtual digital art environment.","PeriodicalId":507941,"journal":{"name":"Informatics","volume":"1 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140967780","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}
Pub Date : 2024-05-15DOI: 10.3390/informatics11020029
Ibtissam Azzi, Abdelhay Radouane, Loubna Laaouina, Adil Jeghal, Ali Yahyaouy, H. Tairi
In e-learning systems, even though the automatic detection of learning styles is considered the key element in the adaptation process, it does not represent the main goal of this process at all. Indeed, to accomplish the task of adaptation, it is also necessary to be able to automatically select the learning objects according to the detected styles. The classification techniques are the most used techniques to automatically select the learning objects by processing data derived from learning object metadata. By using these classification techniques, considerable results are obtained via several approaches and consist of mapping the learning objects into different teaching strategies and then mapping these strategies into the identified learning styles. However, these approaches have some limitations related to robustness. Indeed, a common feature of these approaches is that they do not directly map learning object metadata elements to learning style dimensions. Moreover, they do not consider the fuzzy nature of learning objects. Indeed, any learning object can be suitable for different learning styles at varying degrees of suitability. This highlights the need to find a way to remedy this shortcoming. Our work is part of the automatic selection of learning objects. So, we will propose an approach that uses the fuzzy classification technique to select learning objects based on learning styles. In this approach, the metadata of each learning object that complies with the Institute of Electrical and Electronics Engineers (IEEE) standard are stored in a database as an Extensible Markup Language (XML) file. The Fuzzy C Means algorithm is used, on one hand, to assign fuzzy suitability rates to the stored learning objects and, on the other hand, to cluster them into the Felder and Silverman learning styles model categories. The experiment results show the performance of our approach.
在电子学习系统中，尽管学习风格的自动检测被认为是适应过程中的关键因素，但它并不代表这一过程的主要目标。事实上，要完成适应任务，还必须能够根据检测到的学习风格自动选择学习对象。分类技术是通过处理从学习对象元数据中提取的数据来自动选择学习对象的最常用技术。使用这些分类技术，可以通过几种方法获得相当可观的结果，包括将学习对象映射到不同的教学策略中，然后将这些策略映射到已识别的学习风格中。然而，这些方法在稳健性方面存在一些局限性。事实上，这些方法的一个共同特点是没有直接将学习对象元数据元素映射到学习风格维度。此外，它们也没有考虑到学习对象的模糊性。事实上，任何学习对象都可能在不同程度上适合不同的学习风格。因此，我们需要找到一种方法来弥补这一缺陷。我们的工作是自动选择学习对象的一部分。因此，我们将提出一种使用模糊分类技术来根据学习风格选择学习对象的方法。在这种方法中，符合电气和电子工程师协会（IEEE）标准的每个学习对象的元数据都以可扩展标记语言（XML）文件的形式存储在数据库中。一方面使用模糊 C 平均值算法为存储的学习对象分配模糊适合率，另一方面将它们聚类为 Felder 和 Silverman 学习风格模型类别。实验结果表明了我们的方法的性能。
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Pub Date : 2024-05-01DOI: 10.3390/informatics11020028
F. Kalaw, Jimmy S. Chen, Sally L. Baxter
Data harmonization is vital for secondary electronic health record data analysis, especially when combining data from multiple sources. Currently, there is a gap in knowledge as to how studies identify cohorts of patients with age-related macular degeneration (AMD), a leading cause of blindness. We hypothesize that there is variation in using medical condition codes to define cohorts of AMD patients that can lead to either the under- or overrepresentation of such cohorts. This study identified articles studying AMD using the International Classification of Diseases (ICD-9, ICD-9-CM, ICD-10, and ICD-10-CM). The data elements reviewed included the year of publication; dataset origin (Veterans Affairs, registry, national or commercial claims database, and institutional EHR); total number of subjects; and ICD codes used. A total of thirty-seven articles were reviewed. Six (16%) articles used cohort definitions from two ICD terminologies. The Medicare database was the most used dataset (14, 38%), and there was a noted increase in the use of other datasets in the last few years. We identified substantial variation in the use of ICD codes for AMD. For the studies that used ICD-10 terminologies, 7 (out of 9, 78%) defined the AMD codes correctly, whereas, for the studies that used ICD-9 and 9-CM terminologies, only 2 (out of 30, 7%) defined and utilized the appropriate AMD codes (p = 0.0001). Of the 43 cohort definitions used from 37 articles, 31 (72%) had missing or incomplete AMD codes used, and only 9 (21%) used the exact codes. Additionally, 13 articles (35%) captured ICD codes that were not within the scope of AMD diagnosis. Efforts to standardize data are needed to provide a reproducible research output.
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Pub Date : 2024-01-26DOI: 10.3390/informatics11010007
Mariana Ávalos-Arce, Heráclito Pérez-Díaz, Carolina Del-Valle-Soto, Ramon A. Briseño
Wireless networks play a pivotal role in various domains, including industrial automation, autonomous vehicles, robotics, and mobile sensor networks. This research investigates the critical issue of packet loss in modern wireless networks and aims to identify the conditions within a network’s environment that lead to such losses. We propose a packet status prediction model for data packets that travel through a wireless network based on the IEEE 802.15.4 standard and are exposed to five different types of interference in a controlled experimentation environment. The proposed model focuses on the packetization process and its impact on network robustness. This study explores the challenges posed by packet loss, particularly in the context of interference, and puts forth the hypothesis that specific environmental conditions are linked to packet loss occurrences. The contribution of this work lies in advancing our understanding of the conditions leading to packet loss in wireless networks. Data are retrieved with a single CC2531 USB Dongle Packet Sniffer, whose pieces of information on packets become the features of each packet from which the classifier model will gather the training data with the aim of predicting whether a packet will unsuccessfully arrive at its destination. We found that interference causes more packet loss than that caused by various devices using a WiFi communication protocol simultaneously. In addition, we found that the most important predictors are network strength and packet size; low network strength tends to lead to more packet loss, especially for larger packets. This study contributes to the ongoing efforts to predict and mitigate packet loss, emphasizing the need for adaptive models in dynamic wireless environments.
无线网络在工业自动化、自动驾驶汽车、机器人技术和移动传感器网络等多个领域发挥着举足轻重的作用。本研究调查了现代无线网络中数据包丢失这一关键问题，旨在确定导致数据包丢失的网络环境条件。我们根据 IEEE 802.15.4 标准提出了一种数据包状态预测模型，该模型适用于在受控实验环境中受到五种不同类型干扰的无线网络中传输的数据包。所提模型的重点是数据包化过程及其对网络鲁棒性的影响。这项研究探讨了丢包带来的挑战，特别是在干扰的情况下，并提出了特定环境条件与丢包现象相关的假设。这项工作的贡献在于加深了我们对导致无线网络丢包的条件的理解。数据是通过单个 CC2531 USB 加密狗数据包嗅探器获取的，数据包上的信息片段成为每个数据包的特征，分类器模型将从中收集训练数据，目的是预测数据包是否会无法成功到达目的地。我们发现，与同时使用 WiFi 通信协议的各种设备相比，干扰造成的数据包丢失更多。此外，我们发现最重要的预测因素是网络强度和数据包大小；低网络强度往往会导致更多的数据包丢失，尤其是较大的数据包。这项研究为目前预测和缓解数据包丢失的工作做出了贡献，强调了在动态无线环境中建立自适应模型的必要性。
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