2021 IEEE International Conference on Digital Health (ICDH)最新文献_第5页

PyDPLib: Python Differential Privacy Library for Private Medical Data Analytics PyDPLib:用于私人医疗数据分析的Python差分隐私库

2021 IEEE International Conference on Digital Health (ICDH) Pub Date : 2021-09-01 DOI: 10.1109/icdh52753.2021.00034

Sana Imtiaz, P. Matthies, Francisco Pinto, M. Maros, H. Wenz, R. Sadre, Vladimir Vlassov

{"title":"PyDPLib: Python Differential Privacy Library for Private Medical Data Analytics","authors":"Sana Imtiaz, P. Matthies, Francisco Pinto, M. Maros, H. Wenz, R. Sadre, Vladimir Vlassov","doi":"10.1109/icdh52753.2021.00034","DOIUrl":"https://doi.org/10.1109/icdh52753.2021.00034","url":null,"abstract":"Pharmaceutical and medical technology companies accessing real-world medical data are not interested in personally identifiable data but rather in cohort data such as statistical aggregates, patterns, and trends. These companies cooperate with medical institutions that collect medical data and want to share it but they need to protect the privacy of individuals on the shared data. We present PyDPLib, a Python Differential Privacy library for private medical data analytics. We illustrate an application of differential privacy using PyDPLib in our platform for visualizing private statistics on a database of prostate cancer patients. Our experimental results show that PyDPLib allows creating statistical data plots without compromising patients’ privacy while preserving underlying data distributions. Even though PyDPLib has been developed to be used in our platform for reporting the radiological examinations and procedures, it is general enough to be used to provide differential privacy on data in any data analytics and visualization platform, service or application.","PeriodicalId":93401,"journal":{"name":"2021 IEEE International Conference on Digital Health (ICDH)","volume":"366 1","pages":"191-196"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74194216","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

2021 IEEE International Conference on Digital Health 2021年IEEE数字健康国际会议

2021 IEEE International Conference on Digital Health (ICDH) Pub Date : 2021-09-01 DOI: 10.1109/icdh52753.2021.00001

Icdh, K. Joshi, Seung Geol Choi

引用次数: 0

A Wireless Single Lead ECG Module for Cloud-Computing Based Postoperative Monitoring of Cardiac Surgical Patients 一种用于心脏外科术后云计算监测的无线单导联心电模块

2021 IEEE International Conference on Digital Health (ICDH) Pub Date : 2021-09-01 DOI: 10.1109/icdh52753.2021.00039

Ravi Durbha, V. Koomson

引用次数: 1

Exploring Relationships between Cerebral and Peripheral Biosignals with Neural Networks 用神经网络探索大脑和外周生物信号之间的关系

2021 IEEE International Conference on Digital Health (ICDH) Pub Date : 2021-09-01 DOI: 10.1109/icdh52753.2021.00022

Alexander H. Hatteland, Ricards Marcinkevics, R. Marquis, Thomas Frick, Ilona Hubbard, Julia E. Vogt, T. Brunschwiler, P. Ryvlin

{"title":"Exploring Relationships between Cerebral and Peripheral Biosignals with Neural Networks","authors":"Alexander H. Hatteland, Ricards Marcinkevics, R. Marquis, Thomas Frick, Ilona Hubbard, Julia E. Vogt, T. Brunschwiler, P. Ryvlin","doi":"10.1109/icdh52753.2021.00022","DOIUrl":"https://doi.org/10.1109/icdh52753.2021.00022","url":null,"abstract":"Autonomic peripheral activity is partly governed by brain autonomic centers. However, there is still a lot of uncertainties regarding the precise link between peripheral and central autonomic biosignals. Clarifying these links could have a profound impact on the interpretability, and thus usefulness, of peripheral autonomic biosignals captured with wearable devices. In this study, we take advantage of a unique dataset consisting of intracranial stereo-electroencephalography (SEEG) and peripheral biosignals acquired simultaneously for several days from four subjects undergoing epilepsy monitoring. Compared to previous work, we apply a deep neural network to explore high-dimensional nonlinear correlations between the cerebral brainwaves and variations in heart rate and electrodermal activity (EDA). Further, neural network explainability methods were applied to identify most relevant brainwave frequencies, brain regions and temporal information to predict a specific biosignal. Strongest brain-peripheral correlations were observed from contacts located in the central autonomic network, in particular in the alpha, theta and 52 to 58 Hz frequency band. Furthermore, a temporal delay of 12 to 14 s between SEEG and EDA signal was observed. Finally, we believe that this pilot study demonstrates a promising approach to mapping brain-peripheral relationships in a data-driven manner by leveraging the expressiveness of deep neural networks.","PeriodicalId":93401,"journal":{"name":"2021 IEEE International Conference on Digital Health (ICDH)","volume":"22 1","pages":"103-113"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86767980","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

Remote Photoplethysmography Heart Rate Variability Detection Using Signal to Noise Ratio Bandpass Filtering 利用信噪比带通滤波的远程光电容积脉搏图心率变异性检测

2021 IEEE International Conference on Digital Health (ICDH) Pub Date : 2021-09-01 DOI: 10.1109/ICDH52753.2021.00025

Lin He, Kazi Shafiul Alam, Jiachen Ma, Eric Burkholder, William Cheng Chung Chu, Anik Iqbal, Sheikh Iqbal Ahamed

引用次数: 3

A Software Simulator for Optimizing Ambulance Location and Response Time: A Preliminary Report 一种优化救护车位置和响应时间的软件模拟器:初步报告

2021 IEEE International Conference on Digital Health (ICDH) Pub Date : 2021-09-01 DOI: 10.1109/icdh52753.2021.00037

David La Barbera, Kevin Roitero, Stefano Mizzaro, V. D. Mea, F. Valent

引用次数: 1

Message from the Organizing Committee 组委会寄语

2021 IEEE International Conference on Digital Health (ICDH) Pub Date : 2021-09-01 DOI: 10.1109/iciafs.2016.7946580

Hong Kong

引用次数: 0

Towards an ABAC Break-Glass to access EMRs in case of emergency based on Blockchain 在基于区块链的紧急情况下访问电子病历的ABAC破碎玻璃

2021 IEEE International Conference on Digital Health (ICDH) Pub Date : 2021-09-01 DOI: 10.1109/icdh52753.2021.00041

M. Saberi, Mehdi Adda, H. Mcheick

{"title":"Towards an ABAC Break-Glass to access EMRs in case of emergency based on Blockchain","authors":"M. Saberi, Mehdi Adda, H. Mcheick","doi":"10.1109/icdh52753.2021.00041","DOIUrl":"https://doi.org/10.1109/icdh52753.2021.00041","url":null,"abstract":"Blockchain technology is a fast-evolving sector that has proposed value in different domains. A distributed health care system for managing electronic medical records has various significant advantages in comparison to centralized health care systems. In a distributed system without a central authority, many threats such as data leakage by human mistake or a single point of failure are no longer feasible. A small number of recent research have proposed health care systems that have used IPFS and Blockchain as part of their security and storage components. Both have a transparent process and clear logic as a distributed system. In emergency care, access to medical records is an indisputable need to make efficient decisions fast. Current regulatory and bureaucratic processes make it near impossible to serve the data in a timely manner. This research designs a model of a break-glass mechanism for EMR management systems to provide access to healthcare professionals just in case of emergency. This conceptual model provides access to patients' records regarding patient privacy and data security, which they set previously by themselves.","PeriodicalId":93401,"journal":{"name":"2021 IEEE International Conference on Digital Health (ICDH)","volume":"1 1","pages":"220-222"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83021513","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

Physical Exercise Recommendation and Success Prediction Using Interconnected Recurrent Neural Networks 基于互联递归神经网络的体育锻炼推荐与成功预测

2021 IEEE International Conference on Digital Health (ICDH) Pub Date : 2020-10-01 DOI: 10.2196/preprints.27538

A. Mahyari, P. Pirolli

引用次数: 9