2021 IEEE International Conference on Digital Health (ICDH)最新文献_第3页

Secure Cloud EHR with Semantic Access Control, Searchable Encryption and Attribute Revocation 安全云电子病历与语义访问控制，可搜索的加密和属性撤销

2021 IEEE International Conference on Digital Health (ICDH) Pub Date : 2021-09-01 DOI: 10.13016/M2MZKC-IIAV

Redwan Walid, K. Joshi, Seung Geol Choi

{"title":"Secure Cloud EHR with Semantic Access Control, Searchable Encryption and Attribute Revocation","authors":"Redwan Walid, K. Joshi, Seung Geol Choi","doi":"10.13016/M2MZKC-IIAV","DOIUrl":"https://doi.org/10.13016/M2MZKC-IIAV","url":null,"abstract":"To ensure a secure Cloud-based Electronic Health Record (EHR) system, we need to encrypt data and impose field-level access control to prevent malicious usage. Since the attributes of the Users will change with time, the encryption policies adopted may also vary. For large EHR systems, it is often necessary to search through the encrypted data in realtime and perform client-side computations without decrypting all patient records. This paper describes our novel cloud-based EHR system that uses Attribute Based Encryption (ABE) combined with Semantic Web technologies to facilitate differential access to an EHR, thereby ensuring only Users with valid attributes can access a particular field of the EHR. The system also includes searchable encryption using keyword index and search trapdoor, which allows querying EHR fields without decrypting the entire patient record. The attribute revocation feature is efficiently managed in our EHR by delegating the revision of the secret key and ciphertext to the Cloud Service Provider (CSP). Our methodology incorporates advanced security features that eliminate malicious use of EHR data and contributes significantly towards ensuring secure digital health systems on the Cloud.","PeriodicalId":93401,"journal":{"name":"2021 IEEE International Conference on Digital Health (ICDH)","volume":"38 1","pages":"38-47"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89218213","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 Statistical Summary Analysis of Window-Based Extracted Features for EEG Signal Classification 基于窗口特征提取的脑电信号分类统计汇总分析

2021 IEEE International Conference on Digital Health (ICDH) Pub Date : 2021-09-01 DOI: 10.1109/icdh52753.2021.00053

Mohammad Masum, H. Shahriar, Hisham M. Haddad, Wenzhan Song

{"title":"A Statistical Summary Analysis of Window-Based Extracted Features for EEG Signal Classification","authors":"Mohammad Masum, H. Shahriar, Hisham M. Haddad, Wenzhan Song","doi":"10.1109/icdh52753.2021.00053","DOIUrl":"https://doi.org/10.1109/icdh52753.2021.00053","url":null,"abstract":"Epilepsy is a common chronic neurological disorder affecting approximately 50 million people worldwide. The electroencephalogram (EEG) signal, which contains valuable information of electrical activity in the brain, is a standard neuroimaging tool used by clinicians to monitor and diagnose epilepsy. Visually inspecting the EEG signal is an expensive, tedious, and error-prone practice. Moreover, the result can be varied with different neurophysiologists for an identical reading. Thus, automatically classify different epileptic states with a high accuracy rate is an urgent requirement and has long been investigated. In this paper, we propose a novel framework to effectively classify epilepsy leveraging summary statistics analysis of window-based features of EEG signals. The framework first denoised the signals using power spectrum density analysis, replaced outliers with k-NN imputer, and then window level features extracted from statistical, temporal, and spectral domains. Basic summary statistics are then computed from the extracted features to feed into different Machine Learning (ML) classifiers. An optimal set of features are selected leveraging variance thresholding and dropping correlated features before feeding the features for classification. Finally, different ML classifiers such as Support Vector Machine, Decision Tree, Random Forest, and k-Nearest Neighbors classifiers are applied to the extracted features. The proposed framework applying the Random Forest classifier can significantly enhance the EEG signal classification performance compared to other existing state-of-the-art epilepsy classification methods in terms of accuracy, precision, recall, and F-beta score.","PeriodicalId":93401,"journal":{"name":"2021 IEEE International Conference on Digital Health (ICDH)","volume":"21 1","pages":"293-298"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75009693","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

Preictal onset detection through unsupervised clustering for epileptic seizure prediction 通过无监督聚类预测癫痫发作的前兆检测

2021 IEEE International Conference on Digital Health (ICDH) Pub Date : 2021-09-01 DOI: 10.1109/icdh52753.2021.00026

A. Quercia, Thomas Frick, Fabian Emanuel Egli, Nicholas Pullen, I. Dupanloup, Jianbin Tang, Umar Asif, S. Harrer, T. Brunschwiler

{"title":"Preictal onset detection through unsupervised clustering for epileptic seizure prediction","authors":"A. Quercia, Thomas Frick, Fabian Emanuel Egli, Nicholas Pullen, I. Dupanloup, Jianbin Tang, Umar Asif, S. Harrer, T. Brunschwiler","doi":"10.1109/icdh52753.2021.00026","DOIUrl":"https://doi.org/10.1109/icdh52753.2021.00026","url":null,"abstract":"Epilepsy is a common neurological disorder characterized by recurrent epileptic seizures. These seizures have different intensities and might lead to accidents or, in the worst case, to sudden death. Therefore, being able to predict epileptic seizures would allow patients to be prepared, reducing the risk of injury. This paper focuses on epileptic seizure prediction using EEG (Electroencephalogram) signals. In contrast to the standard approach where the preictal state is assumed to have a constant duration in all the seizures of a patient, we propose a new method that labels each seizure individually exploiting clustering. Our labeling approach, which was applicable for 38% of the selected seizures, results in substantial improvements compared to the standard one. In fact, it reduces noise in the labels and improves the performance of the binary classifier used to distinguish the interictal and preictal states. Hence, our results suggest that the preictal duration is seizure-specific, not only patient-specific. Finally, we show that our method is able to predict 17 out of 18 (94%) seizures between 15 and 85 minutes, before seizure onset.","PeriodicalId":93401,"journal":{"name":"2021 IEEE International Conference on Digital Health (ICDH)","volume":"69 1","pages":"142-147"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76295920","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

Deep Learning-based Prediction of Cognitive Function Using Basic Blood Test Data and NIRS-measured Cerebral Hemodynamics 基于深度学习的基于基本血液测试数据和nirs测量的脑血流动力学的认知功能预测

2021 IEEE International Conference on Digital Health (ICDH) Pub Date : 2021-09-01 DOI: 10.1109/icdh52753.2021.00040

K. Oyama, K. Sakatani

{"title":"Deep Learning-based Prediction of Cognitive Function Using Basic Blood Test Data and NIRS-measured Cerebral Hemodynamics","authors":"K. Oyama, K. Sakatani","doi":"10.1109/icdh52753.2021.00040","DOIUrl":"https://doi.org/10.1109/icdh52753.2021.00040","url":null,"abstract":"Recently, we demonstrated that deep learning allows the prediction of cognitive function using basic blood test data. In this study, we evaluated the accuracy of deep learning-based predictions of cognitive function by comparing basic blood test data and cerebral hemodynamics as measured by time-resolved near-infrared spectroscopy (TNIRS) as input data for the model. First, we used a linear regression model, random forest, and a deep neural network as contemporary machine learning regression models. We studied 202 participants to assess cognitive function using the Mini-Mental State Examination and analyzed TNIRS-measured cerebral hemodynamics, including absolute concentrations of hemoglobin, regional oxygen saturation, and optical pathlength in the bilateral prefrontal cortices at rest. The results suggested that prediction using both TNIRS and blood data inputs exhibited lower mean absolute and mean absolute percentage errors. We also confirmed that the blood test data are often useful; however, a sufficient combination, including blood counts, electrolytes, and nutrition, is required for clinical use.","PeriodicalId":93401,"journal":{"name":"2021 IEEE International Conference on Digital Health (ICDH)","volume":"2006 2","pages":"218-219"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91502106","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

Do You Brush Your Teeth Properly? An Off-body Sensor-based Approach for Toothbrushing Monitoring 你刷牙正确吗?基于离体传感器的刷牙监测方法

2021 IEEE International Conference on Digital Health (ICDH) Pub Date : 2021-09-01 DOI: 10.1109/icdh52753.2021.00018

Z. Hussain, D. Waterworth, Murtadha M. N. Aldeer, Wei Emma Zhang, Quan Z. Sheng, Jorge Ortiz

{"title":"Do You Brush Your Teeth Properly? An Off-body Sensor-based Approach for Toothbrushing Monitoring","authors":"Z. Hussain, D. Waterworth, Murtadha M. N. Aldeer, Wei Emma Zhang, Quan Z. Sheng, Jorge Ortiz","doi":"10.1109/icdh52753.2021.00018","DOIUrl":"https://doi.org/10.1109/icdh52753.2021.00018","url":null,"abstract":"Oral hygiene is very important for a healthy life. Proper toothbrushing is one of the most important measures against dental problems. Poor toothbrushing methods can lead to tooth decay and other gum diseases. Unfortunately, many people do not brush their teeth properly and there is very limited technology available to assist them in compliance with the standard toothbrushing procedure. Sensor-based human activity recognition techniques have seen tremendous growth recently and are being used in various applications. In this work, we treat the compliance to the standard toothbrushing method as an activity recognition problem. We divide the toothbrushing activity into 16 sub-activities and use a machine learning model to recognize those activities. We introduce an off-body sensing solution that uses a detachable Inertial Measurement Unit (IMU), attached to the handle of the brush. The sensor captures the movements of the brush while reaching different parts of the teeth. Then a machine learning pipeline is trained to predict the brushing of different parts of the teeth. We evaluated the performance of the proposed approach in real-world scenarios and performed experiments with 10 different users. We collected our own data set and compared our approach with the wearable-based approach. The results show that our approach performs better than wearable-based approaches and can recognize the toothbrushing activities with 97.15% accuracy. We also evaluated our model for different types of brushes (manual and electric) and the results show that the proposed approach can work independently from the brush types.","PeriodicalId":93401,"journal":{"name":"2021 IEEE International Conference on Digital Health (ICDH)","volume":"5 1","pages":"59-69"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89926778","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

Knowledge Graph Building from Real-world Multisource “Dirty” Clinical Electronic Medical Records for Intelligent Consultation Applications 从真实世界的多源“肮脏”临床电子病历中构建知识图谱，用于智能咨询应用

2021 IEEE International Conference on Digital Health (ICDH) Pub Date : 2021-09-01 DOI: 10.1109/icdh52753.2021.00049

Xinlong Liu, Li-Qun Xu

{"title":"Knowledge Graph Building from Real-world Multisource “Dirty” Clinical Electronic Medical Records for Intelligent Consultation Applications","authors":"Xinlong Liu, Li-Qun Xu","doi":"10.1109/icdh52753.2021.00049","DOIUrl":"https://doi.org/10.1109/icdh52753.2021.00049","url":null,"abstract":"Intelligent clinical consultation is a diagnostic support system that inferred the likely diseases from the patient's chief complaints as per the established relationship between symptoms and diseases. The key here is to learn and build automatically the general “symptom-disease” medical knowledge graph (MKG) from real-world clinical data. So, the quality of clinical data (chiefly electronic medical records - EMRs) directly affects the quality of the MKG, which in turn determines the quality of the consultation results. The regional public health information platform gathered a large number of front-pages of EMRs' from hospitals of all tiers across the region. The fact that the health IT systems used by hospitals are often sourced from different vendors, and each may have its own data standards and data quality control criteria, would invariably lead to apparent difference in the quality of EMRs collected. This is even so, considering the gaps in knowledge and skills between clinicians at different qualification levels. By detailed analysis of one such collection we found that the two most prominent problems are the inconsistency in diagnosis results and the mismatch between the diagnosis results and the chief complaints and the current illness history. In order to ensure the quality and effectiveness in building a knowledge graph from these real-world data, this paper proposed a “dirty” data cleaning framework including diagnostic results normalization and semantic similarity matching. The symptom-disease knowledge graph constructed from the cleaned data has been applied and verified in the intelligent consultation system.","PeriodicalId":93401,"journal":{"name":"2021 IEEE International Conference on Digital Health (ICDH)","volume":"9 1","pages":"260-265"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87732163","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 Machine Learning Approach to Predict Length of Stay for Opioid Overdose Admitted Patients 预测阿片类药物过量入院患者住院时间的机器学习方法

2021 IEEE International Conference on Digital Health (ICDH) Pub Date : 2021-09-01 DOI: 10.1109/ICDH52753.2021.00042

Jiawei Wu, Priyanka Annapureddy, Zach Farahany, P. Madiraju

引用次数: 0

Multi-task Learning Based on Multi-type Dataset for Retinal Abnormality Detection 基于多类型数据集的视网膜异常检测多任务学习

2021 IEEE International Conference on Digital Health (ICDH) Pub Date : 2021-09-01 DOI: 10.1109/ICDH52753.2021.00029

Linna Zhao, Jianqiang Li, Zerui Ma, Yu Guan, Xi Xu, Xiaoxi Wang, Li Li

{"title":"Multi-task Learning Based on Multi-type Dataset for Retinal Abnormality Detection","authors":"Linna Zhao, Jianqiang Li, Zerui Ma, Yu Guan, Xi Xu, Xiaoxi Wang, Li Li","doi":"10.1109/ICDH52753.2021.00029","DOIUrl":"https://doi.org/10.1109/ICDH52753.2021.00029","url":null,"abstract":"The number of people suffering from ophthalmic diseases is increasing with the population aging. Many studies have been proposed to automatically identify diseases to reduce the risks of further retinal damage. However, most of existing methods mainly used a single type of dataset to solve the specific medical task, which is not clinically practical in the realworld scenarios. In this paper, we propose a multi-task deep learning network based on multi-types datasets to automatically recognise different ophthalmic diseases. Specifically, we first collect a multi-label dataset from the retinal fundus images and related diagnostic reports. Then, we propose a feature-fusion network to extract image and semantic retinal information from multi-types datasets. Finally, a multi-stream models is designed to integrate different specific features and realize the multiple disease detection. In this way, multi-types datasets based features are fully extracted in a multi-task learning manner. Experiments on our real-world dataset show that our proposed network significantly improve the classification performance of the model for ophthalmic diseases.","PeriodicalId":93401,"journal":{"name":"2021 IEEE International Conference on Digital Health (ICDH)","volume":"24 1","pages":"160-165"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85912610","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

Grading Diabetic Retinopathy Severity Using Modern Convolution Neural Networks (CNN) 使用现代卷积神经网络(CNN)对糖尿病视网膜病变的严重程度进行分级

2021 IEEE International Conference on Digital Health (ICDH) Pub Date : 2021-09-01 DOI: 10.1109/icdh52753.2021.00014

Andrew Lee, Matloob Khushi, Patrick Hao, M. S. Uddin, S. Poon

{"title":"Grading Diabetic Retinopathy Severity Using Modern Convolution Neural Networks (CNN)","authors":"Andrew Lee, Matloob Khushi, Patrick Hao, M. S. Uddin, S. Poon","doi":"10.1109/icdh52753.2021.00014","DOIUrl":"https://doi.org/10.1109/icdh52753.2021.00014","url":null,"abstract":"Diabetic Retinopathy is an ophthalmic complication eventuating with impaired vision or even blindness if left unmanaged. In modern society, ophthalmologists are responsible for diagnosing diabetic retinopathy to prevent such outcomes. However, medical costs and the availability of clinicians are just some of the barriers of entry to these services. Portable and more automated solutions could find immediate effectiveness in remote areas and developing countries lacking necessary medical infrastructure. Over time, various computer vision-based techniques have been proposed to automatically diagnose diabetic retinopathy. However, grading diabetic retinopathy in its different stages is still yet to reach the required clinical precision. In this paper, we developed a solution to this problem by image processing followed by ensembling state of the art Convolution Neural Networks (CNNs). We demonstrate the effectiveness of the developed method on publicly available datasets and show that the method outperforms previous studies in multi-classification metrics, achieving accuracies for 5-classes of up to 88.71 % and quadratic weighted kappa scores of up to 0.9256. These outcomes provide promising validation for the clinical relevance and applicability of modern CNN architectures as automated, portable and accurate solutions for the grading of diabetic retinopathy severity.","PeriodicalId":93401,"journal":{"name":"2021 IEEE International Conference on Digital Health (ICDH)","volume":"176 1","pages":"19-26"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73224617","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

COVID-19 Mortality Prediction Using Machine Learning Techniques 利用机器学习技术预测COVID-19死亡率

2021 IEEE International Conference on Digital Health (ICDH) Pub Date : 2021-09-01 DOI: 10.1109/icdh52753.2021.00035

Lindsay Schirato, Kennedy Makina, Dwayne Flanders, Seyedamin Pouriyeh, H. Shahriar

引用次数: 1