2022 IEEE International Conference on Digital Health (ICDH)最新文献

Computer Vision Based Cognition Assessment for Developmental-Behavioral Screening 基于计算机视觉的发育行为筛查认知评估

2022 IEEE International Conference on Digital Health (ICDH) Pub Date : 2022-07-01 DOI: 10.1109/ICDH55609.2022.00031

Chi-yu Chen, Po-Chien Hsu, Tao Chang, Huan Ho, Min-Chun Hu, Chi-Chun Lee, Hui-Ju Chen, M. Ko, Chia-Fan Lee, Pei-Yi Wang

引用次数: 0

Extracting, Visualizing, and Learning from Dynamic Data: Perfusion in Surgical Video for Tissue Characterization 从动态数据中提取、可视化和学习:用于组织表征的外科视频灌注

2022 IEEE International Conference on Digital Health (ICDH) Pub Date : 2022-07-01 DOI: 10.1109/ICDH55609.2022.00009

J. Epperlein, N. Hardy, Pól Mac Aonghusa, R. Cahill

引用次数: 0

Preliminary Data Collection for Collaborative Emergency Department Crowd Management using Wearable Devices 基于可穿戴设备的协同急诊室人群管理的初步数据收集

2022 IEEE International Conference on Digital Health (ICDH) Pub Date : 2022-07-01 DOI: 10.1109/ICDH55609.2022.00013

Victoire Metuge, Maria Valero, Liang Zhao, Valentina Nino, David Claudio

{"title":"Preliminary Data Collection for Collaborative Emergency Department Crowd Management using Wearable Devices","authors":"Victoire Metuge, Maria Valero, Liang Zhao, Valentina Nino, David Claudio","doi":"10.1109/ICDH55609.2022.00013","DOIUrl":"https://doi.org/10.1109/ICDH55609.2022.00013","url":null,"abstract":"Emergency department (ED) visits have risen to more than 60% since 1997, with more than 90% of U.S EDs being over-stretched due to overcrowding which has only been compounded by the recent pandemic. Consequences for ED overcrowding range from less severe effects such as patient inconvenience to more severe outcomes such as patient fatality. Research shows poor crowd management at the ED does not only affect patients but takes a toll on ED staff as well. To attempt to address this issue, our study researches how patient vitals collected and transmitted in real time to ED staff can help manage patients in the ED using a triage system that orders vitals in an urgent priority listing. We gathered data from participants using non-invasive wearable devices (CareTaker4 & Oximeter) to collect vital signs information such as heart rate, respiratory rate, blood pressure, and oxygen levels. We aim to use the data to feed a mathematical model that will create a priority algorithm that can sort patients in an ED according to the urgency of their vital signs and transmit the data in real time to health personnel. This way, the patients can be moved automatically in the list as they deteriorate while waiting. We were able to plot the data to show which patients' health are deteriorating quickly and that would require immediate attention. This will be instrumental by helping ED staff attend to pressing cases faster and help control crowds according to medical urgency instead of a first come first serve basis which is not always effective.","PeriodicalId":120923,"journal":{"name":"2022 IEEE International Conference on Digital Health (ICDH)","volume":"430 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133983639","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

Deep Learning-Based Discrete Calibrated Survival Prediction 基于深度学习的离散校准生存预测

2022 IEEE International Conference on Digital Health (ICDH) Pub Date : 2022-07-01 DOI: 10.1109/ICDH55609.2022.00034

Patrick Fuhlert, Anne Ernst, Esther Dietrich, Fabian Westhaeusser, K. Kloiber, Stefan Bonn

{"title":"Deep Learning-Based Discrete Calibrated Survival Prediction","authors":"Patrick Fuhlert, Anne Ernst, Esther Dietrich, Fabian Westhaeusser, K. Kloiber, Stefan Bonn","doi":"10.1109/ICDH55609.2022.00034","DOIUrl":"https://doi.org/10.1109/ICDH55609.2022.00034","url":null,"abstract":"Deep neural networks for survival prediction outperform classical approaches in discrimination, which is the ordering of patients according to their time-of-event. Conversely, classical approaches like the Cox Proportional Hazards model display much better calibration, the correct temporal prediction of events of the underlying distribution. Especially in the medical domain, where it is critical to predict the survival of a single patient, both discrimination and calibration are important performance metrics. Here we present Discrete Calibrated Survival (DCS), a novel deep neural network for discriminated and calibrated survival prediction that outperforms competing survival models in discrimination on three medical datasets, while achieving best calibration among all discrete time models. The enhanced performance of DCS can be attributed to two novel features, the variable temporal output node spacing and the novel loss term that optimizes the use of uncensored and censored patient data. We believe that DCS is an important step towards clinical application of deep-learning-based survival prediction with state-of-the-art discrimination and good calibration.","PeriodicalId":120923,"journal":{"name":"2022 IEEE International Conference on Digital Health (ICDH)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131412469","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

A New Low-Cost and Accurate Diagnostic mHealth System for Patients with COVID-19 Pneumonia 针对COVID-19肺炎患者的新型低成本、准确诊断移动医疗系统

2022 IEEE International Conference on Digital Health (ICDH) Pub Date : 2022-07-01 DOI: 10.1109/ICDH55609.2022.00027

Tarek El Salti, E. Sykes, Javier Nievas, Chen Tong

引用次数: 0

The Classification of Multiple Interacting Gait Abnormalities Using Insole Sensors and Machine Learning 利用鞋垫传感器和机器学习对多种交互步态异常进行分类

2022 IEEE International Conference on Digital Health (ICDH) Pub Date : 2022-07-01 DOI: 10.1109/ICDH55609.2022.00020

Alexander Turner, David Scott, S. Hayes

{"title":"The Classification of Multiple Interacting Gait Abnormalities Using Insole Sensors and Machine Learning","authors":"Alexander Turner, David Scott, S. Hayes","doi":"10.1109/ICDH55609.2022.00020","DOIUrl":"https://doi.org/10.1109/ICDH55609.2022.00020","url":null,"abstract":"In this work we investigate the effectiveness of a wireless in-shoe pressure sensing system used in combination with a type of machine learning referred to as long term short term memory networks (LSTMs) to classify multiple interacting gait perturbations. Artificially induced gait perturbations consisted of restricted knee extension and altered under foot centre of pressure (COP). The primary aim was to assess the capacity to diagnose gait abnormalities without the need to attend a gait laboratory or visit a clinical healthcare professional, through the use of technology. Ultimately, such a system could be used to autonomously generate therapeutic guidance and provide healthcare professionals with accurate up to date information about a patients gait. The results show that LSTMs are capable of classifying complex interacting gait perturbations using in-shoe pressure data. When testing, 11 of 12 perturbation conditions were correctly classified overall and 58.8% of all data instances were correctly classified (8.3% is random classification). This work illustrates that an automated low cost, non-invasive gait diagnosis system with minimal sensors can be used to identify interacting gait abnormalities in individuals and has further potential to be used in a healthcare setting.","PeriodicalId":120923,"journal":{"name":"2022 IEEE International Conference on Digital Health (ICDH)","volume":"2006 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125840526","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

Digital Health Security & Privacy Symposium 数字健康安全与隐私研讨会

2022 IEEE International Conference on Digital Health (ICDH) Pub Date : 2022-07-01 DOI: 10.1109/icdh55609.2022.00052

引用次数: 0

Digital Health Promotion For Fitness Enthusiasts In Africa 非洲健身爱好者的数字健康推广

2022 IEEE International Conference on Digital Health (ICDH) Pub Date : 2022-07-01 DOI: 10.1109/ICDH55609.2022.00017

Oritsetimevin Arueyinzho, Korede Sanyaolu

引用次数: 2

Message from the Organizing Committee 2022 IEEE International Conference on Digital Health 2022年IEEE数字健康国际会议组委会致辞

2022 IEEE International Conference on Digital Health (ICDH) Pub Date : 2022-07-01 DOI: 10.1109/icdh55609.2022.00051

引用次数: 0

Welcome Message from the General Co-Chair of the IEEE Services 2022 IEEE服务2022大会联合主席致欢迎辞

2022 IEEE International Conference on Digital Health (ICDH) Pub Date : 2022-07-01 DOI: 10.1109/icdh55609.2022.00049

引用次数: 0