Informatics最新文献_第2页

A Framework for Antecedents to Health Information Systems Uptake by Healthcare Professionals: An Exploratory Study of Electronic Medical Records 医疗保健专业人员采用医疗信息系统的前因框架：电子病历探索性研究

Informatics Pub Date : 2024-07-09 DOI: 10.3390/informatics11030044

Reza Torkman, A. Ghapanchi, Reza Ghanbarzadeh

{"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}

引用次数: 0

Impact of Hospital Employees’ Awareness of the EMR System Certification on Interoperability Evaluation: Comparison of Public and Private Hospitals 医院员工对 EMR 系统认证的认识对互操作性评估的影响：公立医院与私立医院的比较

Informatics Pub Date : 2024-07-03 DOI: 10.3390/informatics11030043

Choyeal Park, Jikyeong Park

引用次数: 0

QUMA: Quantum Unified Medical Architecture Using Blockchain QUMA：使用区块链的量子统一医疗架构

Informatics Pub Date : 2024-05-17 DOI: 10.3390/informatics11020033

Akoramurthy Balasubramaniam, B. Surendiran

{"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}

引用次数: 0

Performance Evaluation of Deep Learning Models for Classifying Cybersecurity Attacks in IoT Networks 用于对物联网网络中的网络安全攻击进行分类的深度学习模型性能评估

Informatics Pub Date : 2024-05-17 DOI: 10.3390/informatics11020032

Fray L. Becerra-Suarez, Victor A. Tuesta-Monteza, Heber I. Mejia-Cabrera, Juan Arcila-Diaz

{"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}

引用次数: 0

ACME: A Classification Model for Explaining the Risk of Preeclampsia Based on Bayesian Network Classifiers and a Non-Redundant Feature Selection Approach ACME：基于贝叶斯网络分类器和非冗余特征选择方法的子痫前期风险分类模型

Informatics Pub Date : 2024-05-17 DOI: 10.3390/informatics11020031

Franklin Parrales-Bravo, Rosangela Caicedo-Quiroz, Elianne Rodríguez-Larraburu, Julio Barzola-Monteses

{"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}

引用次数: 0

An Intelligent Model and Methodology for Predicting Length of Stay and Survival in a Critical Care Hospital Unit 预测重症监护病房住院时间和存活率的智能模型和方法

Informatics Pub Date : 2024-05-17 DOI: 10.3390/informatics11020034

Enrique Maldonado Belmonte, Salvador Oton-Tortosa, J. Gutierrez-Martinez, Ana Castillo-Martinez

{"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}

引用次数: 0

Investigating User Experience of VR Art Exhibitions: The Impact of Immersion, Satisfaction, and Expectation Confirmation 研究 VR 艺术展览的用户体验：沉浸感、满意度和预期确认的影响

Informatics Pub Date : 2024-05-16 DOI: 10.3390/informatics11020030

Lin Cheng, Junping Xu, Younghwan Pan

{"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}

引用次数: 0

Fuzzy Classification Approach to Select Learning Objects Based on Learning Styles in Intelligent E-Learning Systems 智能电子学习系统中基于学习风格选择学习对象的模糊分类方法

Informatics Pub Date : 2024-05-15 DOI: 10.3390/informatics11020029

Ibtissam Azzi, Abdelhay Radouane, Loubna Laaouina, Adil Jeghal, Ali Yahyaouy, H. Tairi

{"title":"Fuzzy Classification Approach to Select Learning Objects Based on Learning Styles in Intelligent E-Learning Systems","authors":"Ibtissam Azzi, Abdelhay Radouane, Loubna Laaouina, Adil Jeghal, Ali Yahyaouy, H. Tairi","doi":"10.3390/informatics11020029","DOIUrl":"https://doi.org/10.3390/informatics11020029","url":null,"abstract":"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.","PeriodicalId":507941,"journal":{"name":"Informatics","volume":"64 44","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140972223","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}

引用次数: 0

Variations in Using Diagnosis Codes for Defining Age-Related Macular Degeneration Cohorts 使用诊断代码定义年龄相关性黄斑变性队列的差异

Informatics Pub Date : 2024-05-01 DOI: 10.3390/informatics11020028

F. Kalaw, Jimmy S. Chen, Sally L. Baxter

{"title":"Variations in Using Diagnosis Codes for Defining Age-Related Macular Degeneration Cohorts","authors":"F. Kalaw, Jimmy S. Chen, Sally L. Baxter","doi":"10.3390/informatics11020028","DOIUrl":"https://doi.org/10.3390/informatics11020028","url":null,"abstract":"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.","PeriodicalId":507941,"journal":{"name":"Informatics","volume":"19 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141029948","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}

引用次数: 0

Uncovering the Limitations and Insights of Packet Status Prediction Models in IEEE 802.15.4-Based Wireless Networks and Insights from Data Science 揭示基于 IEEE 802.15.4 无线网络的数据包状态预测模型的局限性和启示以及数据科学的启示

Informatics Pub Date : 2024-01-26 DOI: 10.3390/informatics11010007

Mariana Ávalos-Arce, Heráclito Pérez-Díaz, Carolina Del-Valle-Soto, Ramon A. Briseño

{"title":"Uncovering the Limitations and Insights of Packet Status Prediction Models in IEEE 802.15.4-Based Wireless Networks and Insights from Data Science","authors":"Mariana Ávalos-Arce, Heráclito Pérez-Díaz, Carolina Del-Valle-Soto, Ramon A. Briseño","doi":"10.3390/informatics11010007","DOIUrl":"https://doi.org/10.3390/informatics11010007","url":null,"abstract":"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.","PeriodicalId":507941,"journal":{"name":"Informatics","volume":"4 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139593088","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}

引用次数: 0