2021 IEEE International Conference on Digital Health (ICDH)最新文献_第4页

Towards Blockchain-Based Secure Data Management for Remote Patient Monitoring 基于区块链的远程患者监护安全数据管理

2021 IEEE International Conference on Digital Health (ICDH) Pub Date : 2021-09-01 DOI: 10.1109/ICDH52753.2021.00054

Md Jobair Hossain Faruk, H. Shahriar, Maria Valero, S. Sneha, S. Ahamed, Mohammad Rahman

{"title":"Towards Blockchain-Based Secure Data Management for Remote Patient Monitoring","authors":"Md Jobair Hossain Faruk, H. Shahriar, Maria Valero, S. Sneha, S. Ahamed, Mohammad Rahman","doi":"10.1109/ICDH52753.2021.00054","DOIUrl":"https://doi.org/10.1109/ICDH52753.2021.00054","url":null,"abstract":"Traditional data collection, storage and processing of Electronic Health Records (EHR) utilize centralized techniques that pose several risks of single point of failure and lean the systems to a number of internal and external data breaches that compromise their reliability and availability. Blockchain is an emerging distributed technology that can solve these issues due to its immutability and architectural nature that prevent records manipulation or alterations. In this paper, we discuss the progress and opportunities of remote patient monitoring using futuristic blockchain technologies and its two primary frameworks: Ethereum and Hyperledger Fabric. We also discuss the possible blockchain use cases in software engineering for systematic, disciplined, and quantifiable application development. The study extends by introducing a system architecture for EHR data management using Ethereum as a model. We discuss the challenges and limitations along with the initial evaluation results of the proposed system and draw future research directions in this promising area.","PeriodicalId":93401,"journal":{"name":"2021 IEEE International Conference on Digital Health (ICDH)","volume":"9 1","pages":"299-308"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89678314","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: 20

Enabling Tiered and Coordinated Services in a Health Community of Primary Care Facilities and County Hospitals Based on HL7 FHIR 基于HL7 FHIR的初级保健机构和县级医院卫生社区分层协调服务

2021 IEEE International Conference on Digital Health (ICDH) Pub Date : 2021-09-01 DOI: 10.1109/icdh52753.2021.00048

Jingwen Nan, Li-Qun Xu, Qingsong Wang, Changyu Bu, Jianjun Ma, Feng Qiao

{"title":"Enabling Tiered and Coordinated Services in a Health Community of Primary Care Facilities and County Hospitals Based on HL7 FHIR","authors":"Jingwen Nan, Li-Qun Xu, Qingsong Wang, Changyu Bu, Jianjun Ma, Feng Qiao","doi":"10.1109/icdh52753.2021.00048","DOIUrl":"https://doi.org/10.1109/icdh52753.2021.00048","url":null,"abstract":"The HL7 FHIR is an emerging international standard designed to facilitate the interoperability among IT systems of healthcare providers' and other stakeholders'. The implementation of interoperability relies on three key elements: unified data standards, API specifications and the coordinated scheduling. In this paper, we addressed the interoperability between a health community of primary care facilities and county hospitals in China, which usually adopted diversified legacy health IT systems from different vendors over the years. We designed a series of open service FHIR API specifications targeting seven prior applications commonly encountered in such a community, as well as developed an integration service platform (ISPf) to serve these APIs. The seven scenarios included outpatient appointment process, two-way referrals, access to regional LIS, access to regional PACS, retrieval of medical service history, payment request and access to PHR. Each care institution needs to access the ISPf as per open service API specifications. The ISPf provides API authorization, calling and secure management services among healthcare providers. Our work demonstrated an open, flexible, effective and standards complied approach to promoting health IT systems' interoperability to enable tiered and coordinated healthcare services to the great benefit of under-served citizens.","PeriodicalId":93401,"journal":{"name":"2021 IEEE International Conference on Digital Health (ICDH)","volume":"37 1","pages":"254-259"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77894166","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: 3

Clinical de-identification using sub-document analysis and ELECTRA 应用子文件分析和电子电流图进行临床去识别

2021 IEEE International Conference on Digital Health (ICDH) Pub Date : 2021-09-01 DOI: 10.1109/icdh52753.2021.00050

Rosario Catelli, F. Gargiulo, Emanuele Damiano, M. Esposito, G. Pietro

{"title":"Clinical de-identification using sub-document analysis and ELECTRA","authors":"Rosario Catelli, F. Gargiulo, Emanuele Damiano, M. Esposito, G. Pietro","doi":"10.1109/icdh52753.2021.00050","DOIUrl":"https://doi.org/10.1109/icdh52753.2021.00050","url":null,"abstract":"The privacy protection mechanism in the health context is becoming a crucial task given the exponential increase in the adoption of the Electronic Health Records (EHRs) all around the world. This kind of data can be used for medical investigation and research only if it is filtered out of all the so called Protected Health Information (PHI). This paper proposes a clinical de-identification system based on deep learning techniques for Named Entity Recognition and aimed at recognizing PHI entities to be replaced by surrogates in EHRs for anonymization purposes. This system is based on ELECTRA, a recent neural language model, and is enhanced through a sub-document level analysis aimed at grouping input sentences together, through a Sentences Grouping Factor (SGF), with the aim of broadening the representation context and consequently enhancing its ability to learn. This system was experimentally tested on the official dataset distributed in 2014 by Informatics for Integrating Biology & the Bedside research group, exhibiting superior performance compared to the state of the art in terms of detection at the category level, crucial for properly substituting PHI entities with surrogates. The effectiveness of the proposed system with respect to its components has been also confirmed by a further experimental analysis performed by substituting BERT language model in place of ELECTRA and varying SGF in accordance with limitations concerning the maximum input size for the language model used.","PeriodicalId":93401,"journal":{"name":"2021 IEEE International Conference on Digital Health (ICDH)","volume":"89 1","pages":"266-275"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78991487","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: 6

Smartphone TBI Sensing using Deep Embedded Clustering and Extreme Boosted Outlier Detection 基于深度嵌入聚类和极端增强离群值检测的智能手机TBI传感

2021 IEEE International Conference on Digital Health (ICDH) Pub Date : 2021-09-01 DOI: 10.1109/icdh52753.2021.00024

Srinarayan Srikanthan, Florin Asani, B. Patel, E. Agu

{"title":"Smartphone TBI Sensing using Deep Embedded Clustering and Extreme Boosted Outlier Detection","authors":"Srinarayan Srikanthan, Florin Asani, B. Patel, E. Agu","doi":"10.1109/icdh52753.2021.00024","DOIUrl":"https://doi.org/10.1109/icdh52753.2021.00024","url":null,"abstract":"Traumatic Brain Injury (TBI), caused by a severe impact to the head, can have long-lasting and possibly life-long disability of patients. This ultimately creates a huge economic and social burden on patients and the healthcare system. Many TBI patients do not get early and adequate medical care. Sensor-rich, ubiquitously owned smartphones can now be used to passively sense a wide range of ailments, facilitating continuous monitoring of patients and high-risk groups in the real world. In this paper, we propose a deep learning approach for distinguishing smartphone users with TBI from healthy controls based on smartphone-sensed behaviors within 24-hours of the injury. Our method analyzes smartphone sensor data by first utilizing Deep Embedded Clustering (DEC) to identify clusters of users with similar smartphone-sensed behaviors. Extreme Gradient Boosted Outlier Detection (XGBOD) is then employed on each of the identified clusters to predict users with TBI. In rigorous evaluation, our method achieved a balanced accuracy of 88 % and a sensitivity of 74 %. Our proposed method can flag smartphone users with TBI, enabling them to receive early medical attention and improve their prognostic outlook.","PeriodicalId":93401,"journal":{"name":"2021 IEEE International Conference on Digital Health (ICDH)","volume":"63 1","pages":"122-132"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90253682","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

Putting BPMN and DMN to Work: a Pediatric Surgery Case Study 使BPMN和DMN发挥作用:一个儿科外科案例研究

2021 IEEE International Conference on Digital Health (ICDH) Pub Date : 2021-09-01 DOI: 10.1109/icdh52753.2021.00028

A. Bianchi, M. Mortari, Claudio Pintavalle, G. Pozzi

引用次数: 0

Non-Invasive Monitoring of Human Hygiene using Vibration Sensor and Classifiers 使用振动传感器和分类器的无创人体卫生监测

2021 IEEE International Conference on Digital Health (ICDH) Pub Date : 2021-09-01 DOI: 10.1109/icdh52753.2021.00044

Shashank Trivedi, Maria Valero, H. Shahriar, Liang Zhao

引用次数: 1

An IoT System for Autonomous, Continuous, Real-Time Patient Monitoring and Its Application to Pressure Injury Management 自主、连续、实时患者监测的物联网系统及其在压力损伤管理中的应用

2021 IEEE International Conference on Digital Health (ICDH) Pub Date : 2021-09-01 DOI: 10.1109/icdh52753.2021.00021

Sam Mansfield, Eric Vin, K. Obraczka

{"title":"An IoT System for Autonomous, Continuous, Real-Time Patient Monitoring and Its Application to Pressure Injury Management","authors":"Sam Mansfield, Eric Vin, K. Obraczka","doi":"10.1109/icdh52753.2021.00021","DOIUrl":"https://doi.org/10.1109/icdh52753.2021.00021","url":null,"abstract":"In this paper, we introduce PIMAP, an IoT-based system for continuous, real-time patient monitoring that operates in a fully autonomous fashion, i.e. without the need for human intervention. To our knowledge, PIMAP is the first open system that integrates the basic patient monitoring workflow including sensed data collection, storage, analysis, and real-time visualization. PIMAP's open design allows it to easily integrate a variety of sensors (custom and off-the-shelf), analytics, and visualization. Other novel features of PIMAP include its deployment flexibility, i.e., its ability to be deployed in different configurations depending on the specific application needs, setting, and resources, as well as PIMAP's self-profiling and self-tuning capabilities. While PIMAP can be applied to various patient monitoring applications and settings, in this paper we focus on the unsolved problem of preventing pressure ulcers, or pressure injuries. We describe how PIMAP's design addresses autonomous, continuous, realtime operation to sense, store, analyze, and visualize patient data from a variety of off-the-shelf as well as custom sensors. We present our current PIMAP prototype as well as different PIMAP configuration scenarios, e.g. cloud-based or edge-based deployment options. We also evaluate PIMAP's performance under different workloads and demonstrate its use collecting wearable pressure sensor data in real-world scenarios from patients with high risk of forming pressure injuries.","PeriodicalId":93401,"journal":{"name":"2021 IEEE International Conference on Digital Health (ICDH)","volume":"47 1","pages":"91-102"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89780017","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

Finding Similar Tweets in Health Related Topics. 在健康相关主题中查找相似的tweet。

2021 IEEE International Conference on Digital Health (ICDH) Pub Date : 2021-09-01 Epub Date: 2021-11-13 DOI: 10.1109/icdh52753.2021.00033

Danny Villanueva-Vega, Manuel Rodriguez-Martinez

引用次数: 1

A Framework for Secure Logging in Precision Healthcare Cloud-based Services 在基于云的精准医疗保健服务中用于安全登录的框架

2021 IEEE International Conference on Digital Health (ICDH) Pub Date : 2021-09-01 DOI: 10.1109/icdh52753.2021.00038

Parisa Moghaddam, Shahrear Iqbal, I. Traoré

引用次数: 3

Minimizing Epidemic Viral Total Exposure under the Droplet and Aerosol Models 在液滴和气溶胶模型下最小化流行病病毒的总暴露

2021 IEEE International Conference on Digital Health (ICDH) Pub Date : 2021-09-01 DOI: 10.1109/icdh52753.2021.00057

Abdalaziz Sawwan, Jie Wu

引用次数: 1