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End User Satisfaction With Cloud Computing: The Case of Hamad Medical Corporation in Qatar 最终用户对云计算的满意度:卡塔尔哈马德医疗公司的案例
Int. J. Heal. Inf. Syst. Informatics Pub Date : 2021-10-01 DOI: 10.4018/ijhisi.295821
Fatima Al-Qahtani, E. Abu-Shanab
{"title":"End User Satisfaction With Cloud Computing: The Case of Hamad Medical Corporation in Qatar","authors":"Fatima Al-Qahtani, E. Abu-Shanab","doi":"10.4018/ijhisi.295821","DOIUrl":"https://doi.org/10.4018/ijhisi.295821","url":null,"abstract":"Cloud computing assures a faster, cheaper and more efficient rendering of resources, which leads to huge popularity among businesses and specifically the health sector. The major objective of this research is to identify the benefits of cloud computing (CC) and the factors influencing users satisfaction. Utilizing a survey collected from 219 employees, the research model was tested. Results indicated that employee compliance issues, security and privacy issues, economic benefits, operational benefits, functional benefits, and trust are all significant predictors of satisfaction. Management issues and private cloud risks were not significant predictors of satisfaction. The coefficient of determination R2 = 0.81. This study conducted comparisons between different categories of the sample based on their satisfaction level and concluded that age and education were significant discriminators, while gender, experience, and department were not. Conclusions and future research are stated in the last section.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133417317","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: 1
Classification of Parkinson Disease Based on Analysis and Synthesis of Voice Signal 基于语音信号分析与合成的帕金森病分类
Int. J. Heal. Inf. Syst. Informatics Pub Date : 2021-10-01 DOI: 10.4018/ijhisi.20211001.oa30
Vikas Mittal, R. Sharma
{"title":"Classification of Parkinson Disease Based on Analysis and Synthesis of Voice Signal","authors":"Vikas Mittal, R. Sharma","doi":"10.4018/ijhisi.20211001.oa30","DOIUrl":"https://doi.org/10.4018/ijhisi.20211001.oa30","url":null,"abstract":"The most important application of voice profiling is pathological voice detection. Parkinson's disease is a chronic neurological degenerative disease affecting the central nervous system responsible for essentially progressive evolution movement disorders. 70% to 90% of Parkinson’s disease (PD) patients show an affected voice. This paper proposes a methodology for PD based on acoustic, glottal, physical, and electrical parameters. The results show that the acoustic parameter is more important in the case of Parkinson’s disease as compared to glottal and physical parameters. The authors achieved 97.2% accuracy to differentiate Parkinson and healthy voice using jitter to pitch ratio proposed algorithm. The Authors also proposed an algorithm of poles calculation of the vocal tract to find formants of the vocal tract. Further, formants are used for finding the transfer function of vocal tract filter. In the end, the authors suggested parameters of the electrical vocal tract model are also changed in the case of PD voices.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114171837","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: 2
Factors Affecting User Intention to Pay via Online Medical Service Platform: Role of Misdiagnosis Risk and Timeliness of Response 影响用户在线医疗服务平台付费意愿的因素:误诊风险和响应及时性的作用
Int. J. Heal. Inf. Syst. Informatics Pub Date : 2021-10-01 DOI: 10.4018/ijhisi.295819
Pinghao Ye, Liqiong Liu
{"title":"Factors Affecting User Intention to Pay via Online Medical Service Platform: Role of Misdiagnosis Risk and Timeliness of Response","authors":"Pinghao Ye, Liqiong Liu","doi":"10.4018/ijhisi.295819","DOIUrl":"https://doi.org/10.4018/ijhisi.295819","url":null,"abstract":"With regard to platform performance and trust, we study the influencing factors of users’ intention to pay on an online medical service platform. Results of this paper will provide a new perspective for online medical service platform research in the context of the Internet.A questionnaire survey is administered to collect 312 effective sample data, and the data are analyzed by partial least squares structural equation modeling. Results showed that the information quality, system quality, and convenience of the platform significantly affect the perceived benefit (PB) of users. Users’ perceived reliability of the platform significantly positively affects doctor reliability (DR). Users’ PB significantly positively affects their ITP and DR, and DR significantly positively affects the users’ ITP. The misdiagnosis risk positively regulates the relationship between the users’ trust tendency and DR. The timeliness of the response of the platform positively adjusts the relationship between DR and users’ ITP.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114430013","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
Security/Privacy Perceptions in Patient Use of Online Medical Records: Study From a Large National Survey 患者使用在线医疗记录的安全/隐私感知:来自一项大型全国调查的研究
Int. J. Heal. Inf. Syst. Informatics Pub Date : 2021-10-01 DOI: 10.4018/ijhisi.295816
S. Mukhopadhyay, R. Basak, Brian J. Reithel
{"title":"Security/Privacy Perceptions in Patient Use of Online Medical Records: Study From a Large National Survey","authors":"S. Mukhopadhyay, R. Basak, Brian J. Reithel","doi":"10.4018/ijhisi.295816","DOIUrl":"https://doi.org/10.4018/ijhisi.295816","url":null,"abstract":"Data breaches expose individuals to economic, mental, and social trauma. Electronic health information of individuals not only includes reports of medical diagnosis, medication histories but also comprises personally identifiable information (PII) (e.g, birth date). We examined the association of vulnerability perception - defined as privacy or security breach concerns and provider encouragement with the use of online medical records (OMR) and moderating effects of provider encouragement and age in the relationship between vulnerability and usage. Data came from a national population-based survey, the Health Information National Trends Survey (HINTS). This study included 1770 adult individuals many of who are chronic disease patients or cancer survivors. The majority of these subjects did report use of OMR. We found security/privacy related vulnerability and provider encouragement significantly predict patients' use of OMR. Healthcare providers and developers should work with patients to mitigate concerns and enable patients to derive benefits from using online medical records.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115116042","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
Food Intake Vision-Based Recognition System via Histogram of Oriented Gradients and Support Vector Machine for Persons With Alzheimer's Disease 基于定向梯度直方图和支持向量机的阿尔茨海默病患者食物摄入视觉识别系统
Int. J. Heal. Inf. Syst. Informatics Pub Date : 2021-10-01 DOI: 10.4018/ijhisi.295817
Haitham Al-Anssari, I. Abdel-Qader, M. Mickus
{"title":"Food Intake Vision-Based Recognition System via Histogram of Oriented Gradients and Support Vector Machine for Persons With Alzheimer's Disease","authors":"Haitham Al-Anssari, I. Abdel-Qader, M. Mickus","doi":"10.4018/ijhisi.295817","DOIUrl":"https://doi.org/10.4018/ijhisi.295817","url":null,"abstract":"Due to cognitive decline, individuals with Alzheimer’s often suffer from malnutrition, forgetting to eat, even if food is presented. Therefore, assistance with feeding is needed. In this paper a vision-based system for monitoring of eating patterns is presented. Upper Body Region (UBR) is detected using Viola-Jones method, a histogram of oriented gradients (HOG) is generated for feature extraction, and a support vector machine (SVM) is used to distinguish eating versus non-eating. To reduce false positive results, Haar-like features are used to detect hands while moving between served food and mouth within the identified upper body region (UBR). A combined template image (CTI) method is proposed in this work to eliminate false positive hand detections where 30 hand eating posture images have been selected and combined into one template image. Matching implemented using CTI is 2.86 times faster than matching the subject to the 30 images separately. Experimental simulation used 33 videos of 163840 frames indicates that the proposed method achieves a high accuracy of 90.65%.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130980058","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
Analysis and Design Process for Predicting and Controlling Blood Glucose in Type 1 Diabetic Patients: A Requirements Engineering Approach 预测和控制1型糖尿病患者血糖的分析与设计过程:需求工程方法
Int. J. Heal. Inf. Syst. Informatics Pub Date : 2021-10-01 DOI: 10.4018/ijhisi.289461
I. Gambo, Rhodes Massenon, B. Kolawole, Rhoda Ikono
{"title":"Analysis and Design Process for Predicting and Controlling Blood Glucose in Type 1 Diabetic Patients: A Requirements Engineering Approach","authors":"I. Gambo, Rhodes Massenon, B. Kolawole, Rhoda Ikono","doi":"10.4018/ijhisi.289461","DOIUrl":"https://doi.org/10.4018/ijhisi.289461","url":null,"abstract":"Engineering smart software that can monitor, predict, and control blood glucose is critical to improving patients' quality of treatments with type 1 Diabetic Mellitus (T1DM). However, ensuring a reasonable glycemic level in diabetic patients is quite challenging, as many methods do not adequately capture the complexities involved in glycemic control. This problem introduces a new level of complexity and uncertainty to the patient's psychological state, thereby making this problem nonlinear and unobservable. In this paper, we formulated a mathematical model using carbohydrate counting, insulin requirements, and the Harris-Benedict energy equations to establish the framework for predicting and controlling blood glucose level regulation in T1DM. We implemented the framework and evaluated its performance using root mean square error (RMSE) and mean absolute error (MAE) on a case study. Our framework had less error rate in terms of RMSE and MAE, which indicates a better fit with reasonable accuracy.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134224237","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
A Two-Stage Registration Method for Dental Volumetric Data and Mesh Data 牙体数据与网格数据的两阶段配准方法
Int. J. Heal. Inf. Syst. Informatics Pub Date : 2021-10-01 DOI: 10.4018/ijhisi.20211001.oa29
Shuai Yang, Wenjing Shi, Yongzhen Ke, Yongjiang Xue
{"title":"A Two-Stage Registration Method for Dental Volumetric Data and Mesh Data","authors":"Shuai Yang, Wenjing Shi, Yongzhen Ke, Yongjiang Xue","doi":"10.4018/ijhisi.20211001.oa29","DOIUrl":"https://doi.org/10.4018/ijhisi.20211001.oa29","url":null,"abstract":"Dental computed tomography (CT) images and optical surface scan data are widely used in dental computer-aided design systems. Registration is essential if they are used in software systems. Existing automatic registration methods are either time-consuming or rough, and interactive registration methods are experience-dependent and tedious because of a great deal of purely manual interactions. For overcoming these disadvantages, a two-stage registration method is proposed. In the rough registration stage, the rough translation and rotation matrices are obtained by applying unit quaternion based method on the points interactively selected from the two types of data. In the precise registration stage, the stridden sampling is used to reduce computational complexity and the proposed registration algorithm with scale transformation is used for precise registration. The proposed method offers a good trade-off between precision and time cost. The experimental results demonstrate that the proposed method provides faster convergence and smaller registration errors than existing methods.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131756563","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: 1
Adaptive Neuro-Fuzzy Inference Model for Monitoring Hypertension Risk 高血压风险监测的自适应神经模糊推理模型
Int. J. Heal. Inf. Syst. Informatics Pub Date : 2021-10-01 DOI: 10.4018/ijhisi.295818
Ngozi Chidozie Egejuru, O. Ogunlade, P. Idowu, A. Asinobi
{"title":"Adaptive Neuro-Fuzzy Inference Model for Monitoring Hypertension Risk","authors":"Ngozi Chidozie Egejuru, O. Ogunlade, P. Idowu, A. Asinobi","doi":"10.4018/ijhisi.295818","DOIUrl":"https://doi.org/10.4018/ijhisi.295818","url":null,"abstract":"This study presented a model to classify risk of hypertension using Adaptive Neuro-Fuzzy Inference System (ANFIS). In order to develop the model cardiologists from teaching hospitals in Nigeria were interviewed so as to identify required variables for classification. Structured questionnaires were used to elicit information about the risk factors and the associated risk of hypertension from respondents. The MATLAB ANFIS Toolbox was used to simulate the model. The result of this study revealed that there were 33 main variables identified for monitoring hypertension risk and they were in line with the WHO/ISH classification standard. The result showed that majority of the patients selected had very high risk (57.0%) of hypertension which consisted more than 50% of the patients selected followed by 19% representing patients with high risk of hypertension, followed by patients with medium risk of hypertension. In conclusion, the model assist healthcare professionals to have accurate diagnosis, early detection and proper management of hypertension.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125735067","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: 1
Towards Detecting Dementia via Deep Learning 通过深度学习检测痴呆症
Int. J. Heal. Inf. Syst. Informatics Pub Date : 2021-10-01 DOI: 10.4018/ijhisi.20211001.oa31
Deepika Bansal, K. Khanna, R. Chhikara, R. Dua, Rajeev Malini
{"title":"Towards Detecting Dementia via Deep Learning","authors":"Deepika Bansal, K. Khanna, R. Chhikara, R. Dua, Rajeev Malini","doi":"10.4018/ijhisi.20211001.oa31","DOIUrl":"https://doi.org/10.4018/ijhisi.20211001.oa31","url":null,"abstract":"Dementia is a brain disorder that causes loss of memory leading to disruption in the normal course of life of an individual. It is emerging as a global health problem in adults with age 65 years or above. Early diagnosis of dementia has gone forth as a key research zone with the aim of early identification for hindering the advancement. Deep learning provides path-breaking applications in medical imaging. This study provides a detailed summary of different implementation approaches of deep learning for detecting the disease. Transfer learning for multi-class classification has also been explored for detecting dementia. The pre-trained convolutional network, AlexNet is used with 3 optimizers, SGDM, ADAM, RMSProp. A Dataset of 60 MRI images is taken from the OASIS dataset. Accuracy of the methods has been compared and the best parameters including classifier, learning rate, and a batch size of the model have been identified. SGDM classifier with a learning rate 10-4 and a mini-batch size of 10 have shown the best performance in a reasonable time.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124141527","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 Efficient Method for Biomedical Word Sense Disambiguation Based on Web-Kernel Similarity 基于web核相似度的生物医学词义消歧方法
Int. J. Heal. Inf. Syst. Informatics Pub Date : 2021-10-01 DOI: 10.4018/IJHISI.20211001.OA9
Mohammed Rais, M. Bekkali, Abdelmonaime Lachkar
{"title":"An Efficient Method for Biomedical Word Sense Disambiguation Based on Web-Kernel Similarity","authors":"Mohammed Rais, M. Bekkali, Abdelmonaime Lachkar","doi":"10.4018/IJHISI.20211001.OA9","DOIUrl":"https://doi.org/10.4018/IJHISI.20211001.OA9","url":null,"abstract":"Searching for the best sense for a polysemous word remains one of the greatest challenges in the representation of biomedical text. To this end, word sense disambiguation (WSD) algorithms mostly rely on an external source of knowledge, like a thesaurus or ontology, for automatically selecting the proper concept of an ambiguous term in a given window of context using semantic similarity and relatedness measures. In this paper, the authors propose a web-based kernel function for measuring the semantic relatedness between concepts to disambiguate an expression versus multiple possible concepts. This measure uses the large volume of documents returned by PubMed search engine to determine the greater context for a biomedical short text through a new term weighting scheme based on rough set theory (RST). To illustrate the efficiency of our proposed method, they evaluate a WSD algorithm based on this measure on a biomedical dataset (MSH-WSD) that contains 203 ambiguous terms and acronyms. The obtained results demonstrate promising improvements. KEyWoRDS Biomedical Word Sense Disambiguation, Conceptualization, Context Concept, MSH-WSD, Rough Set Theory, Short Text Similarity","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129913405","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
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