2021 IEEE International Conference on Digital Health (ICDH)最新文献_第2页

Lung Cancer Prediction Using Curriculum Learning Based Deep Neural Networks 基于课程学习的深度神经网络肺癌预测

2021 IEEE International Conference on Digital Health (ICDH) Pub Date : 2021-09-01 DOI: 10.1109/icdh52753.2021.00013

Jackson Zhou, Matloob Khushi, M. Moni, M. S. Uddin, S. Poon

{"title":"Lung Cancer Prediction Using Curriculum Learning Based Deep Neural Networks","authors":"Jackson Zhou, Matloob Khushi, M. Moni, M. S. Uddin, S. Poon","doi":"10.1109/icdh52753.2021.00013","DOIUrl":"https://doi.org/10.1109/icdh52753.2021.00013","url":null,"abstract":"The high incidence and low survival rate of lung cancers contribute to their high death count, and drive the development of lung cancer prediction models using demographic factors. The five year relative survival rate of small cell lung cancer in particular (6%) is four times less than that of non small cell lung cancer (23%), though no predictive models have been developed for it so far. This study aimed to expand on previous lung cancer prediction studies and develop improved models for general and small cell lung cancer prediction. Established machine learning models were considered, in addition to a novel curriculum learning based deep neural network. All models were evaluated using data from the National Cancer Institute's Prostate, Lung, Colorectal and Ovarian Cancer screening trial, with performance measured using the area under the receiver operator characteristic curve (AUROC). Random forest models were found to give the best performances in lung cancer prediction (bootstrap optimism corrected (BOC) $text{AUROC} = {0.927}$), outperforming previous logistic regression models $(text{BOC} text{AUROC} ={0.859})$. Additionally, curriculum learning based neural networks were shown to outperform all other model types for small cell lung cancer prediction in particular (AUROCs of 0.873 and 0.882 across two feature sets). To conclude, high-performance models were developed for general and small cell lung cancer prediction, and could help improve non-invasive lung cancer prediction in a clinical setting.","PeriodicalId":93401,"journal":{"name":"2021 IEEE International Conference on Digital Health (ICDH)","volume":"2007 1","pages":"11-18"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78887850","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

[Copyright notice] (版权)

2021 IEEE International Conference on Digital Health (ICDH) Pub Date : 2021-09-01 DOI: 10.1109/icdh52753.2021.00003

引用次数: 0

Use of Musculoskeletal Modeling to Examine Lower Limb Muscle Contribution to Gait Balance Control: Effects of Overweight 使用肌肉骨骼模型来检查下肢肌肉对步态平衡控制的贡献:超重的影响

2021 IEEE International Conference on Digital Health (ICDH) Pub Date : 2021-09-01 DOI: 10.1109/icdh52753.2021.00056

H. K. Kim, L. Chou

引用次数: 1

A Portable Microfluidic Immuno-biochip Platform for Oral Cancer Biomarker Detection 用于口腔癌生物标志物检测的便携式微流控免疫生物芯片平台

2021 IEEE International Conference on Digital Health (ICDH) Pub Date : 2021-09-01 DOI: 10.1109/icdh52753.2021.00043

H. Chueh, Z. Liao, Ting-Yi Lin, Hsiang-Yu Lei, Hsing-Ying Lin, Chen-Han Huang

引用次数: 1

Risk and Compliance in IoT- Health Data Propagation: A Security-Aware Provenance based Approach 物联网中的风险与合规性——健康数据传播:基于安全意识的来源方法

2021 IEEE International Conference on Digital Health (ICDH) Pub Date : 2021-09-01 DOI: 10.1109/ICDH52753.2021.00015

Fariha Tasmin Jaigirdar, C. Rudolph, Chris Bain

{"title":"Risk and Compliance in IoT- Health Data Propagation: A Security-Aware Provenance based Approach","authors":"Fariha Tasmin Jaigirdar, C. Rudolph, Chris Bain","doi":"10.1109/ICDH52753.2021.00015","DOIUrl":"https://doi.org/10.1109/ICDH52753.2021.00015","url":null,"abstract":"Data generated from various dynamic applications of Internet of Things (IoT) based healthcare technology is effectively used for decision-making, providing reliable and smart healthcare services to the elderly and patients with chronic diseases. Since these precious data are susceptible to various security attacks, continuous monitoring of the system's compliance and identification of security risks in IoT data propagation is essential through potentially several layers of applications. This paper pinpoints how security-aware data provenance graphs can support compliance checking and risk estimation by including sufficient information on security controls and other security-relevant evidence. Real-time analysis of these security evidence to enable a step-wise validation and providing the evidence of this validation to end-users is currently not possible with the available data. This paper analyzes the security concerns in different phases of data propagation in a designed IoT-health scenario and promotes step-wise validation of security evidence. It proposes a system model with a novel protocol that documents and verifies evidence for security controls for data-object relations in data provenance graphs to assist compliance checking of security regulation of healthcare systems. With this regard, this paper discusses the proposed system model design with the requirements for technical safeguards of the Health Insurance Portability and Accountability Act (HIPAA). Based on the verification output at each phase, the proposed protocol reports this chain of verification by creating certain security tokens. Finally, the paper provides a formal security validation and security design analysis to show the applicability of this step-wise validation within the proposed system model.","PeriodicalId":93401,"journal":{"name":"2021 IEEE International Conference on Digital Health (ICDH)","volume":"34 1","pages":"27-37"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77674159","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

Which Usability Assessment for Digital Therapeutics and Patient Support Programs? 哪种可用性评估适用于数字治疗和患者支持计划?

2021 IEEE International Conference on Digital Health (ICDH) Pub Date : 2021-09-01 DOI: 10.1109/icdh52753.2021.00051

Mario Alessandro Bochicchio, L. Vaira, A. Mortara, R. Maria

{"title":"Which Usability Assessment for Digital Therapeutics and Patient Support Programs?","authors":"Mario Alessandro Bochicchio, L. Vaira, A. Mortara, R. Maria","doi":"10.1109/icdh52753.2021.00051","DOIUrl":"https://doi.org/10.1109/icdh52753.2021.00051","url":null,"abstract":"Computer systems can significantly affect the health of their users in both positive and negative ways. In past decades, this finding led the scientific community to create medical devices, primarily based on the effects of the hardware of computer systems. More recently, clinical trials have allowed the validation of new digital therapies, in which the active ingredient is not from biochemistry: it is an algorithm, implemented as a software. Digital Therapeutics (DTx) and Digital Patient Support Programs (DSx) are the names of the two class of digital applications used for this type of interventions. As for other critical software impacting the security and safety of human beings, the quality of DTx and DSx is of paramount relevance. Because of this reason and the unique requirements and constraints associated with the clinical trials adopted to validate each new therapy, specific evaluation techniques are needed to help clinicians and researchers develop new DTxs and support their continuous improvement. To this end, we discuss a flexible method for evaluating DTxs and DSxs. The proposed method incorporates the International Measurement System (IMS) scale and the Mobile Application Rating Scale (MARS) in a framework derived from a simplified version of phase III clinical trials. The method was co-designed by IT experts, health professionals and patients to ensure due rigor while including, from the outset, aspects of privacy, confidentiality, ease of use and sustainability. An example of the application of the proposed approach to a real DSx is discussed in the second part of the paper.","PeriodicalId":93401,"journal":{"name":"2021 IEEE International Conference on Digital Health (ICDH)","volume":"29 1","pages":"276-282"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88155757","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

An Adaptable LSTM Network Predicting COVID-19 Occurrence Using Time Series Data 基于时间序列数据预测COVID-19发生的自适应LSTM网络

2021 IEEE International Conference on Digital Health (ICDH) Pub Date : 2021-09-01 DOI: 10.1109/icdh52753.2021.00031

A. Li, N. Yadav

{"title":"An Adaptable LSTM Network Predicting COVID-19 Occurrence Using Time Series Data","authors":"A. Li, N. Yadav","doi":"10.1109/icdh52753.2021.00031","DOIUrl":"https://doi.org/10.1109/icdh52753.2021.00031","url":null,"abstract":"As the COVID-19 pandemic progresses, it has become critical for policymakers and medical officials to understand how cases are trending. Machine learning models, particularly deep learning LSTM (Long Short-Term Memory) models, may hold immense value to forecast changes in COVID-19 cases. In this paper, a novel LSTM-based architecture is proposed, developed and trained on human logistics data that includes travel patterns, visits to commercial properties, as well as historical cases, demographic, and climate data. This data includes both time series and static data allowing the LSTM to be used in both classification and regression tasks to predict COVID-19 occurrence trends. For classification, the problem is modeled as a multiclass supervised learning classification problem with varying granularity. The proposed LSTM network achieves an 81.0% F1-score outperforming conventional machine learning model benchmarks (such as the random forest model with an F1 score of 58.9%) and is comparable in performance to a time series forest model. Additionally, the LSTM model is adaptable to perform regression and predict a 14-day sliding window based on currently observed data with a mean absolute error of 0.0026. This research serves as a foundation for future work in the forecasting of COVID-19 and other similar disease outbreaks using similar temporal and static data.","PeriodicalId":93401,"journal":{"name":"2021 IEEE International Conference on Digital Health (ICDH)","volume":"29 1","pages":"172-177"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81723815","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

An Investigation of Containment Measure Implementation and Public Responses to the COVID-19 Pandemic in Mainland China 中国大陆新冠肺炎疫情防控措施实施情况及公众反应调查

2021 IEEE International Conference on Digital Health (ICDH) Pub Date : 2021-09-01 DOI: 10.1109/icdh52753.2021.00046

Ji Liu, H. Xiong, Xiakai Wang, Jizhou Huang, Qiaojun Li, Tongtong Huang, Siyu Huang, Haifeng Wang, D. Dou

{"title":"An Investigation of Containment Measure Implementation and Public Responses to the COVID-19 Pandemic in Mainland China","authors":"Ji Liu, H. Xiong, Xiakai Wang, Jizhou Huang, Qiaojun Li, Tongtong Huang, Siyu Huang, Haifeng Wang, D. Dou","doi":"10.1109/icdh52753.2021.00046","DOIUrl":"https://doi.org/10.1109/icdh52753.2021.00046","url":null,"abstract":"While the COVID-19 epidemic expands among multiple countries, diverse measures have been exploited to halt the spread of COVID-19. In Mainland China, the containment measures can be categorized into two types, i.e., intra-city quarantine and isolation, and inter-city travel restriction. Both information acquisition and local economy play an important role while implementing the measures. In order to understand the relationship between the containment measures and pulic responses to the COVID-19 pandemic, we study the correlation among three factors, i.e., the information acquisition of containment measures, the public responses to the COVID-19 pandemic, and local economy of cities in Mainland China. We combine Markov Chain Monte Carlo (MCMC) and SIR-X to estimate the parameters related to the pandemic. Then, we investigate the correlations among multiple representative parameters including mobility, local economy, and information acquisition to understand the implementation of containment measures. We utilize the mobility data from Baidu Maps, the COVID-19 related search frequency data from Baidu Search Engine, and the data of Gross Domestic Product (GDP). From the analysis, we evidence that the the information acquisition is strongly correlated with the local economy. In addition, we find that the cities with stronger local economy have bigger inflows from Wuhan, while the citizens of the cities perform COVID-19- related searches more frequently and take the quarantine measure more strictly.","PeriodicalId":93401,"journal":{"name":"2021 IEEE International Conference on Digital Health (ICDH)","volume":"26 1","pages":"234-243"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86762885","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 Digital Health System for Disease Analytics 用于疾病分析的数字健康系统

2021 IEEE International Conference on Digital Health (ICDH) Pub Date : 2021-09-01 DOI: 10.1109/icdh52753.2021.00019

C. Leung, Daryl L. X. Fung, Thanh Huy Daniel Mai, Joglas Souza, N. D. Tran

{"title":"A Digital Health System for Disease Analytics","authors":"C. Leung, Daryl L. X. Fung, Thanh Huy Daniel Mai, Joglas Souza, N. D. Tran","doi":"10.1109/icdh52753.2021.00019","DOIUrl":"https://doi.org/10.1109/icdh52753.2021.00019","url":null,"abstract":"Data science, data mining and machine learning have been applied in numerous real-life applications and services including disease and healthcare analytics, such as identification and predictive analytics of coronavirus disease 2019 (COVID-19). Many of these existing works usually require large volumes of data train the classification and prediction models. However, these data (e.g., computed tomography (CT) scan images, viral/molecular test results) that can be expensive to produce and/or not easily accessible. For instance, partially due to privacy concerns and other factors, the volume of available disease data can be limited. Hence, in this paper, we present a digital health system for disease analytics. Specifically, the system make good use of autoencoder and few-shot learning to train the prediction model with only a few samples of more accessible and less expensive types of data (e.g., serology/antibody test results from blood samples), which helps to support prediction on classification of potential patients (e.g., potential COVID-19 patients). Moreover, the system also provides users (e.g., healthcare providers) with interpretable explanation of the prediction results, which increases their trust in the system. With this system, users could then focus and provide timely treatment to the true patients, thus preventing them for spreading the disease in the community. The system is helpful, especially for rural areas, when sophisticated equipment (e.g., CT scanners) may be unavailable. Evaluation results on a real-life datasets demonstrate the effectiveness of our digital health system in disease analytics, especially in classifying and explaining crucial information about patients.","PeriodicalId":93401,"journal":{"name":"2021 IEEE International Conference on Digital Health (ICDH)","volume":"53 1","pages":"70-79"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88299536","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

A Novel Telemedicine System to Traditional Tongue Examination for Chinese Medical Applications 一种新型远程医疗系统，用于中医传统舌诊

2021 IEEE International Conference on Digital Health (ICDH) Pub Date : 2021-09-01 DOI: 10.1109/icdh52753.2021.00055

Lisa Li-Chuan Chen, Shen-Kai Wang, Tse-Yu Lin, Ling-Feng Huang, M. Lo, Chien-Chang Chen

引用次数: 1