2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom)最新文献_第4页

Role-based Access Control Solution for GraphQL-based Fast Healthcare Interoperability Resources Health Application Programming Interface 基于graphql的快速医疗保健互操作性资源健康应用程序编程接口的基于角色的访问控制解决方案

2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom) Pub Date : 2022-10-17 DOI: 10.1109/HealthCom54947.2022.9982782

Mohammed S. Baihan

{"title":"Role-based Access Control Solution for GraphQL-based Fast Healthcare Interoperability Resources Health Application Programming Interface","authors":"Mohammed S. Baihan","doi":"10.1109/HealthCom54947.2022.9982782","DOIUrl":"https://doi.org/10.1109/HealthCom54947.2022.9982782","url":null,"abstract":"Recently, GraphQL, a query language for Application Programming Interface (API), attracts many organizations and implementers in different domains, including healthcare informatic, to utilize it as an alternative to Representational State Transfer (REST) API. It is believed that GraphQL overcomes some issues of REST API. Moreover, GraphQL is known for its security issues as identified by OWASP organization, specifically Broken Object Level Authorization (BOLA) and Broken Function Level Authorization (BFLA) which are basically access control related issues. Furthermore, to the best of our knowledge there is no solution exists, in the academia or industry, to protect GraphQL-based Fast Healthcare Interoperability Resources (FHIR) API against BOLA and BFLA. In this paper, we present a Role-based Access Control (RBAC) solution to intercept all FHIR GraphQL requests to prevent related BOLA and BFLA vulnerabilities. To prove our work, we have implemented the RBAC solution as a server interceptor based on the HAPI FHIR reference implementation. Moreover, our evaluation showed that the suggested solution introduced minimal overhead.","PeriodicalId":202664,"journal":{"name":"2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132196294","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

Detecting Diabetic Autonomic Neuropathy from Electronic Health Records Using Machine Learning 利用机器学习从电子健康记录中检测糖尿病自主神经病变

2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom) Pub Date : 2022-10-17 DOI: 10.1109/HealthCom54947.2022.9982752

Zahra Solatidehkordi, S. Dhou

{"title":"Detecting Diabetic Autonomic Neuropathy from Electronic Health Records Using Machine Learning","authors":"Zahra Solatidehkordi, S. Dhou","doi":"10.1109/HealthCom54947.2022.9982752","DOIUrl":"https://doi.org/10.1109/HealthCom54947.2022.9982752","url":null,"abstract":"Diabetes is a disease that affects a large number of people worldwide, and diabetic neuropathy is one of its most common and serious complications. Diabetic autonomic neuropathy (DAN) is a type of diabetic neuropathy that is defined as a disorder of the autonomous nervous system and can affect various organs in the body, including the heart and kidney. DAN is widely under-diagnosed due to reasons such as the cost and unavailability of testing equipment, the difficulty of performing cardiovascular tests, and the oftentimes asymptomatic state of the disease in its early stages. However, a late diagnosis can lead to dangerous health complications in the long run. As such, this paper aims to use machine learning to detect DAN in the kidney and heart in diabetic patients by retrieving their information from electronic health records. For this purpose, a dataset of 1275 patient records was used with a variety of traditional machine learning and deep learning algorithms. The best performing model was TabNet with an F1 score of 85.82 for the heart and 73.37 for the kidney.","PeriodicalId":202664,"journal":{"name":"2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127883192","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

Optical Fibre FPI End-Tip based Sensor for Protein Aggregation Detection 基于光纤FPI端尖的蛋白质聚集检测传感器

2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom) Pub Date : 2022-10-17 DOI: 10.1109/HealthCom54947.2022.9982774

M. Fátima Domingues, I. Direito, C. Sousa, A. Radwan, P. Antunes, P. André, L. Helguero, N. Alberto

引用次数: 1

Herb–Drug Interactions: A Holistic Decision Support System in Healthcare 草药-药物相互作用:医疗保健中的整体决策支持系统

2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom) Pub Date : 2022-10-17 DOI: 10.1109/HealthCom54947.2022.9982729

Andreia S. Martins, Eva Maia, Isabel Praça

引用次数: 4

Eat This, Not That! – a Personalised Restaurant Menu Decoder That Helps You Pick the Right Food 吃这个，不要吃那个!-一个个性化的餐厅菜单解码器，帮助你选择正确的食物

2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom) Pub Date : 2022-10-17 DOI: 10.1109/HealthCom54947.2022.9982770

W. Hasan, Kimia Tuz Zaman, Maryam Sadat Amiri Tehrani Zadeh, Juan Li

引用次数: 1

The Effect of Convolutional Neural Network Layers on Payload-Based Traffic Classification 卷积神经网络层对基于有效负载的流量分类的影响

2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom) Pub Date : 2022-10-17 DOI: 10.1109/HealthCom54947.2022.9982767

Wafaa Alharthi, R. Ouni, K. Saleem

{"title":"The Effect of Convolutional Neural Network Layers on Payload-Based Traffic Classification","authors":"Wafaa Alharthi, R. Ouni, K. Saleem","doi":"10.1109/HealthCom54947.2022.9982767","DOIUrl":"https://doi.org/10.1109/HealthCom54947.2022.9982767","url":null,"abstract":"Different applications in modern networks produce various types of traffic with diverse service requirements. In the network traffic classification, \"unknown applications\" are regarded as a difficult problem that remains unsolved, especially in the healthcare sector. Traffic classification helps in classifying and aggregate traffic flows into categories with the same traffic patterns. Identification and classification of traffic are critical for network management efficiency, which includes Quality of Service (QoS), detection of intrusions, and lawful interception. Only the network traffic classification technology based on payloads is fitting because most of the applications are IP based, whether is attached to a specific port number or is dynamic or is temporary. Payload-based classifiers consist of finding the features in the payload of data packets to differentiate between the application protocols. In this work, we propose a model using machine learning (ML) for an accurate and efficient traffic classification. ML allows for an automatic response to various applications by classifying traffic without a network operator interference. Experimental results demonstrate that ML-based traffic classification methods are effective and obtained high accuracy and a low data loss rate in front of other available models.","PeriodicalId":202664,"journal":{"name":"2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom)","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132411115","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 Polypharmacy for Patients with Multi Encounters Using the QL4POMR Framework 使用QL4POMR框架调查多重就诊患者的多重用药情况

2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom) Pub Date : 2022-10-17 DOI: 10.1109/HealthCom54947.2022.9982796

Sabah Mohammed, J. Fiaidhi

{"title":"Investigating Polypharmacy for Patients with Multi Encounters Using the QL4POMR Framework","authors":"Sabah Mohammed, J. Fiaidhi","doi":"10.1109/HealthCom54947.2022.9982796","DOIUrl":"https://doi.org/10.1109/HealthCom54947.2022.9982796","url":null,"abstract":"Comorbidity is the presence of two or more clinical conditions encountering a patient at the time of visit, hospitalization or being with the patient for a considerable time. Most of these conditions are chronic conditions. Treatment of comorbidity and patients with comorbid diseases poses a major health issue for millions of people worldwide and requires collaboration between clinical providers as well as the use of knowledge cross domains of practice. Having multiple diseases inevitably lead to the use of multiple drugs, a condition known as polypharmacy. This article investigates internists might how to tackle polypharmacy using our previously developed QL4POMR which is kind of medical translation system. QL4POMR provide clinicians with SOAP to describe the patient case at the bedside and link it to the biomedical repositories like the OpenTargets and DrugBank. Based on this link clinician can identify issues associated with polypharmacy like disease to drug interactions or drug to drug interactions. Polypharmacy for comorbid conditions like asthma and hypertension has been investigated as proof of concept.","PeriodicalId":202664,"journal":{"name":"2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128345098","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

Low-cost BLE bracelet as patients monitoring platform: range restrictions 低成本BLE手环作为患者监测平台:范围限制

2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom) Pub Date : 2022-10-17 DOI: 10.1109/HealthCom54947.2022.9982765

Kristina Zovko, D. Begusic, P. Šolić, T. Perković

引用次数: 0

Person Identification and Authentication via Ultrasound Hand-gesture-signature Analysis 基于超声手势签名分析的人的身份识别与认证

2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom) Pub Date : 2022-10-17 DOI: 10.1109/HealthCom54947.2022.9982742

S. Franceschini, M. Ambrosanio, F. Baselice, V. Pascazio

引用次数: 1

ShareTrace: Contact Tracing with the Actor Model ShareTrace:与参与者模型的接触跟踪

2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom) Pub Date : 2022-03-23 DOI: 10.1109/HealthCom54947.2022.9982762

R. Tatton, Erman Ayday, Youngjin Yoo, Anisa Halimi

引用次数: 0