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

Healthy Aging: A Proactive Model to Prevent Self-neglecting Behavior in Smart Homes 健康老龄化:智能家居中预防自我忽视行为的主动模式

2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom) Pub Date : 2022-10-17 DOI: 10.1109/HealthCom54947.2022.9982760

Rhian Chambers, Muhammad Fahim

{"title":"Healthy Aging: A Proactive Model to Prevent Self-neglecting Behavior in Smart Homes","authors":"Rhian Chambers, Muhammad Fahim","doi":"10.1109/HealthCom54947.2022.9982760","DOIUrl":"https://doi.org/10.1109/HealthCom54947.2022.9982760","url":null,"abstract":"With a continuous and noticeable shift towards an aging population around the world, a drastic shift in elderly care is required. The older people experience significant decline in physical and mental capacity, which limits the ability to care for themselves. It can cause the self-neglecting behavior that include failing to take medications, neglecting personal hygiene, and not eating well. The research community has already made a shift towards the sensors technology and deep learning techniques to monitor the homes for effective interventions. In this paper, our aim is to further develop this research by developing a proactive model to prevent self-neglecting behavior in aging population. We proposed a deep learning approach, which is based on sequence modeling technique – long short-term memory (LSTM). The experiments are performed on publicly available real smart home dataset, where the residence was living alone. The standard performance metrics are calculated to ensures an acceptable performance for the deployment in the real-world setting. Three case studies are discussed to show the effectiveness of the proactive model to prevent the self-neglecting behavior. It is expected that our model may allow elderly individuals to remain independent in their own homes for longer time and reduce the burden on health care systems.","PeriodicalId":202664,"journal":{"name":"2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom)","volume":"5 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":"122558050","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

Data Integrity and Causation Analysis for Wearable Devices in 5G 5G环境下可穿戴设备数据完整性及原因分析

2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom) Pub Date : 2022-10-17 DOI: 10.1109/HealthCom54947.2022.9982756

Ying Wang, Ting Liao

{"title":"Data Integrity and Causation Analysis for Wearable Devices in 5G","authors":"Ying Wang, Ting Liao","doi":"10.1109/HealthCom54947.2022.9982756","DOIUrl":"https://doi.org/10.1109/HealthCom54947.2022.9982756","url":null,"abstract":"The dissemination of information integrity at unprecedented speed and scale is a new phenomenon with the potential for vast harm if used incorrectly, specially applied in healthcare and clinical data. Despite holding much promise, the usefulness for clinical research using data from wearable devices that record user’s health conditions is limited by its integrity pitfall. This study presents and demonstrates a detection framework to effectively identify integrity compromises of wearable data and map the compromises with user scenarios under environmental influence. Through the Bayesian Network Model (BNM), the framework performs causation analyses between use scenario and data impact and integrates auto-encoder based data impact anomaly detection and classification. The auto-encoder based data impact detection eliminate the requirement for pre-training data, and enables a real-time detection with average latency of 4.6s. The BNM based causal inference shows accurate inference of user scenario based on the data impact detection. The proposed framework will allow for back tracing the root causes of the integrity compromises and trigger real-time human intervention to improve system integrity. We demonstrated system performance through a simulated use case.","PeriodicalId":202664,"journal":{"name":"2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom)","volume":"231 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":"122816046","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

GPS-based data driven modeling of ambulance travel times: The case of Žilina region 基于gps的救护车行驶时间数据驱动建模:Žilina地区的案例

2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom) Pub Date : 2022-10-17 DOI: 10.1109/HealthCom54947.2022.9982741

S. Rahmani, L. Buzna

引用次数: 1

Weak-Supervision for Prolonged Hospital Length of Stay Prediction 对延长住院时间预测的弱监管

2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom) Pub Date : 2022-10-17 DOI: 10.1109/HealthCom54947.2022.9982748

Ariana J. Mann, N. Bambos

{"title":"Weak-Supervision for Prolonged Hospital Length of Stay Prediction","authors":"Ariana J. Mann, N. Bambos","doi":"10.1109/HealthCom54947.2022.9982748","DOIUrl":"https://doi.org/10.1109/HealthCom54947.2022.9982748","url":null,"abstract":"Predicting whether a patient will have a prolonged length of stay (LoS) once admitted to a hospital can help ensure medical resources are allocated to where they are needed most. However, prior works on classifying prolonged-LoS patients define a prolonged-LoS as being greater than a single, flat number-of-days cutoff. Using a flat cutoff, means that the classification occurs without reference to a baseline LoS, fails to control for any covariates, and is generally only effective for a specific medical subgroup. Instead, in this work, we introduce an approach where the algorithm designer specifies a LoS percentile that should be used as the cutoff for prolonged-LoS. In a method known as weak-supervision, we use the LoS percentile cutoff to train a model to produce the actual labels for classification machine learning training. Contrary to a number-of-days cutoff, the LoS percentile cutoff coupled with weak-supervision, provides what we claim is a more principled and flexible approach to defining what constitutes a prolonged-LoS.Specifically, we train a quantile regression model to predict the designated LoS percentile value for each patient, which importantly allows us to control for covariates that access to medical care should be equalized across (such as primary medical condition, hospital facility, and admission time of day). The regression output is cast as a noisy binary label for prolonged-LoS, which is then used to train a machine learning model for prolonged-LoS classification. We empirically demonstrate that this weak-supervision based approach provides usable classification performance despite using noisy labels.","PeriodicalId":202664,"journal":{"name":"2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom)","volume":"65 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":"115946691","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

Active Testing for an Emerging Epidemic 积极检测新出现的流行病

2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom) Pub Date : 2022-10-17 DOI: 10.1109/HealthCom54947.2022.9982784

Ariana J. Mann, Ilai Bistritz, N. Bambos

{"title":"Active Testing for an Emerging Epidemic","authors":"Ariana J. Mann, Ilai Bistritz, N. Bambos","doi":"10.1109/HealthCom54947.2022.9982784","DOIUrl":"https://doi.org/10.1109/HealthCom54947.2022.9982784","url":null,"abstract":"Identifying disease carriers is a key barrier to effectively control an epidemic outbreak, especially when many carriers are asymptomatic, have minor symptoms, or have a delayed symptom onset. Current isolation policies largely operate at the two ends of the spectrum: isolate almost everyone (lock-down) or isolate only those with severe symptoms. This leads to high misclassification costs. To address this issue, we develop an active learning approach. Active learning is useful when labeling is expensive and there is a limited budget; an active learning algorithm selects which data points to label in order to build the best training dataset for machine learning. We present the novel Active Testing protocol to combine 1) an online, disease-carrier classification model trained on symptom data paired with 2) an active learning based disease testing policy, that results in lower misclassification costs than either of the two extreme isolation policies. Coupling these two components enables our protocol to pick the best testing kit allocation policy to train the carrier classification model and minimize the total decision-theoretic, isolation misclassification cost. We accomplish this with a novel, cost-aware active learning algorithm, and demonstrate its effectiveness compared to existing algorithms in the class-imbalanced setting of disease-carrier classification.","PeriodicalId":202664,"journal":{"name":"2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom)","volume":"2015 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":"114454076","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 Investigation into Crohn’s Disease Lesions Variability Sensing Using Video Colonoscopy and Machine Learning Techniques 利用视频结肠镜检查和机器学习技术对克罗恩病病变变异性感知的研究

2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom) Pub Date : 2022-10-17 DOI: 10.1109/HealthCom54947.2022.9982753

J. Fiaidhi, Sabah Mohammed, P. Zezos

{"title":"An Investigation into Crohn’s Disease Lesions Variability Sensing Using Video Colonoscopy and Machine Learning Techniques","authors":"J. Fiaidhi, Sabah Mohammed, P. Zezos","doi":"10.1109/HealthCom54947.2022.9982753","DOIUrl":"https://doi.org/10.1109/HealthCom54947.2022.9982753","url":null,"abstract":"Crohn's disease (CD) is a chronic inflammatory disease characterized by transmural inflammation and may affect any part of the gastrointestinal (GI) tract, from the mouth to the perianal area. Crohn's disease most commonly affects the colon and the last part of the small intestine (ileum). Crohn’s disease causes various lesions in the mucosa, which is the inner layer of the gut. CD presents with focal ulcerations, erythema and edema adjacent to areas of normal appearing mucosa resulting in heterogeneous patchy patterns of disease. Knowing the type and the extent of these patterns is important for the clinicians to provide the right treatments. The medical treatment aims at keeping the disease in remission and abating flares, whereas surgical treatment is indicated to address complications that are beyond the efficacy of the medical treatment. The videocolonoscopy is considered the gold standard in examining the colon and the terminal ileum and the video capsule endoscopy (VCE) to examine the entire small bowel. Examination of the video of both viewing procedures can be enhanced by using computer vision and machine learning techniques. In this paper, we have conducted our first investigation to cluster capsule endoscopy video frames from the small bowel into five CD clusters. We call our approach the CD lesion variability sensing as the uses pipeline of variability recognition utilizing thick data image augmentation techniques and deep learning that have the ability to learn such variability from few samples using Siamese neural network (SSN) with triple loss and fuzzy filter that uses structural similarity index (SSIM). The accuracy of our SSN with the triple loss function reached 68% and our added fuzzy filter increased it to reach over 75%. This is only the start of our investigation in this complex field.","PeriodicalId":202664,"journal":{"name":"2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom)","volume":"1 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":"128676366","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

UBIWEAR: An end-to-end, data-driven framework for intelligent physical activity prediction to empower mHealth interventions UBIWEAR:一个端到端的数据驱动框架，用于智能身体活动预测，为移动健康干预提供支持

2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom) Pub Date : 2022-10-17 DOI: 10.1109/HealthCom54947.2022.9982730

Asterios Bampakis, Sofia Yfantidou, A. Vakali

{"title":"UBIWEAR: An end-to-end, data-driven framework for intelligent physical activity prediction to empower mHealth interventions","authors":"Asterios Bampakis, Sofia Yfantidou, A. Vakali","doi":"10.1109/HealthCom54947.2022.9982730","DOIUrl":"https://doi.org/10.1109/HealthCom54947.2022.9982730","url":null,"abstract":"It is indisputable that physical activity is vital for an individual’s health and wellness. However, a global prevalence of physical inactivity has induced significant personal and socioeconomic implications. In recent years, a significant amount of work has showcased the capabilities of self-tracking technology to create positive health behavior change. This work is motivated by the potential of personalized and adaptive goal-setting techniques in encouraging physical activity via self-tracking. To this end, we propose UBIWEAR, an end-to-end framework for intelligent physical activity prediction, with the ultimate goal to empower data-driven goal-setting interventions. To achieve this, we experiment with numerous machine learning and deep learning paradigms as a robust benchmark for physical activity prediction tasks. To train our models, we utilize, \"MyHeart Counts\", an open, large-scale dataset collected in-the-wild from thousands of users. We also propose a prescriptive framework for self-tracking aggregated data preprocessing, to facilitate data wrangling of real-world, noisy data. Our best model achieves a MAE of 1087 steps, 65% lower than the state of the art in terms of absolute error, proving the feasibility of the physical activity prediction task, and paving the way for future research.","PeriodicalId":202664,"journal":{"name":"2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom)","volume":"1 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":"130166634","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

Trustworthy Explanations for Knowledge Discovered from E-Health Records 电子健康档案发现知识的可信解释

2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom) Pub Date : 2022-10-17 DOI: 10.1109/HealthCom54947.2022.9982786

Bikramjit Singh Dhaliwal, Rayan Imran, C. Leung, Evan W. R. Madill

{"title":"Trustworthy Explanations for Knowledge Discovered from E-Health Records","authors":"Bikramjit Singh Dhaliwal, Rayan Imran, C. Leung, Evan W. R. Madill","doi":"10.1109/HealthCom54947.2022.9982786","DOIUrl":"https://doi.org/10.1109/HealthCom54947.2022.9982786","url":null,"abstract":"In the current era of big data, very large amounts of data are generating at a rapid rate from a wide variety of rich data sources. Electronic health (e-health) records are examples of the big data. With the technological advancements, more healthcare practice has gradually been supported by electronic processes and communication. This enables health informatics, in which computer science meets the healthcare sector to address healthcare and medical problems. Embedded in the big data are valuable information and knowledge that can be discovered by data science, data mining and machine learning techniques. Many of these techniques apply \"opaque box\" approaches to make accurate predictions. However, these techniques may not be crystal clear to the users. As the users not necessarily be able to clearly view the entire knowledge discovery (e.g., prediction) process, they may not easily trust the discovered knowledge (e.g., predictions). Hence, in this paper, we present a system for providing trustworthy explanations for knowledge discovered from e-health records. Specifically, our system provides users with global explanations for the important features among the records. It also provides users with local explanations for a particular record. Evaluation results on real-life e-health records show the practicality of our system in providing trustworthy explanations to knowledge discovered (e.g., accurate predictions made).","PeriodicalId":202664,"journal":{"name":"2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom)","volume":"31 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":"123132785","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: 4

Wi-PT: Wireless Sensing based Low-cost Physical Rehabilitation Tracking Wi-PT:基于无线传感的低成本物理康复跟踪

2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom) Pub Date : 2022-10-17 DOI: 10.1109/HealthCom54947.2022.9982743

Steven M. Hernandez, Md Touhiduzzaman, P. Pidcoe, E. Bulut

{"title":"Wi-PT: Wireless Sensing based Low-cost Physical Rehabilitation Tracking","authors":"Steven M. Hernandez, Md Touhiduzzaman, P. Pidcoe, E. Bulut","doi":"10.1109/HealthCom54947.2022.9982743","DOIUrl":"https://doi.org/10.1109/HealthCom54947.2022.9982743","url":null,"abstract":"Physical therapy (PT) exercises are critically important for the rehabilitation of patients with motor deficits. While rehabilitation exercises can be most effective when performed properly under the supervision of a physical therapist, it can be costly in terms of several aspects and may not be a viable option for all patients. At-home systems offer more accessible and less costly solutions to patients while also providing flexibility in scheduling prescribed exercises. However, current systems mostly depend on camera based solutions that have limitations (i.e., deployment cost, requiring patients to be in the sight of camera, potential privacy violations) or wearable solutions that are cumbersome and intrusive. To this end, in this paper, our goal is to leverage the WiFi infrastructure available in most indoor locations (i.e., homes, apartments, nursing homes, etc.) for tracking the exercises prescribed to patients during their rehabilitation. Our solution, Wi-PT, is based on the analysis of Channel State Information (CSI) captured from ambient WiFi signals, and uses deep learning models trained to recognize the prescribed physical therapy exercises. Through our experiments, we show that the proposed solution can successfully recognize different types of physical therapy exercises such as hand and finger movements, limb movements and movements performed with exercise equipment. Moreover, we show that our system can recognize the person performing different activities and can identify when they are at rest or actively performing an exercise.","PeriodicalId":202664,"journal":{"name":"2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom)","volume":"48 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":"125269301","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

PPG-GAN: An Adversarial Network to De-noise PPG Signals during Physical Activity PPG- gan:一种对抗网络在运动过程中去噪PPG信号

2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom) Pub Date : 2022-10-17 DOI: 10.1109/HealthCom54947.2022.9982757

Xiaoyu Zheng, Mahsa Derakhshani, L. Barrett, Vincent M. Dwyer, Sijung Hu

{"title":"PPG-GAN: An Adversarial Network to De-noise PPG Signals during Physical Activity","authors":"Xiaoyu Zheng, Mahsa Derakhshani, L. Barrett, Vincent M. Dwyer, Sijung Hu","doi":"10.1109/HealthCom54947.2022.9982757","DOIUrl":"https://doi.org/10.1109/HealthCom54947.2022.9982757","url":null,"abstract":"Quality photoplethysmographic (PPG) signals are essential for accurate physiological assessment. However, the PPG acquisition process is often accompanied by spurious motion artefacts (MAs), especially during medium-high intensity physical activity. This study proposes a generative adversarial network (PPG-GAN) to create de-noised versions of measure PPG signals. The Adaptive Notch Filtration (ANF) algorithm, which enables the extraction of accurate heart rates (HR) and respiration rates (RR) from PPG signals, is used as the approximate reference signal to train the PPG-GAN. The generated PPG signals from test inputs provide a heart rate (HR) with a mean absolute error of 1.68 bpm for the IEEE-SPC dataset. A comparison with gold-standard HR and RR measurements, for our in-house dataset, show the errors in absolute value of less than 5%. The generated PPG signals, for the test clips, show a very strong correlation with their reference values, R ≈ 0.98. The results suggest that PPG-GAN could be a paradigm for MA-free PPG signal processing specifically for personal healthcare, even during high intensity activity.","PeriodicalId":202664,"journal":{"name":"2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom)","volume":"27 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":"123965892","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