Big Data最新文献

{"title":"Secure Biomedical Document Protection Framework to Ensure Privacy Through Blockchain.","authors":"Ramkumar Jayaraman, Mohammed Alshehri, Manoj Kumar, Ahed Abugabah, Surender Singh Samant, Ahmed A Mohamed","doi":"10.1089/big.2022.0170","DOIUrl":"10.1089/big.2022.0170","url":null,"abstract":"<p><p>In the recent health care era, biomedical documents play a crucial role, and they contain much evidence-based documentation associated with many stakeholders data. Protecting those confidential research documents is more difficult and effective, and a significant process in the medical-based research domain. Those bio-documentation related to health care and other relevant community-valued data are suggested by medical professionals and processed. Many traditional security mechanisms such as akteonline and Health Insurance Portability and Accountability Act (HIPAA) are used to protect the biomedical documents as they consider the problem of non-repudiation and data integrity related to the retrieval and storage of documents. Thus, there is a need for a comprehensive framework that improves protection in terms of cost and response time related to biomedical documents. In this research work, blockchain-based biomedical document protection framework (BBDPF) is proposed, which includes blockchain-based biomedical data protection (BBDP) and blockchain-based biomedical data retrieval (BBDR) algorithms. BBDP and BBDR algorithms provide consistency on the data to prevent data modification and interception of confidential data with proper data validation. Both the algorithms have strong cryptographic mechanisms to withstand post-quantum security risks, ensuring the integrity of biomedical document retrieval and non-deny of data retrieval transactions. In the performance analysis, Ethereum blockchain infrastructure is deployed BBDPF and smart contracts using Solidity language. In the performance analysis, request time and searching time are determined based on the number of request to ensure data integrity, non-repudiation, and smart contracts for the proposed hybrid model as it gets increased gradually. A modified prototype is built with a web-based interface to prove the concept and evaluate the proposed framework. The experimental results revealed that the proposed framework renders data integrity, non-repudiation, and support for smart contracts with Query Notary Service, MedRec, MedShare, and Medlock.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"437-451"},"PeriodicalIF":4.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9563040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"OzNet: A New Deep Learning Approach for Automated Classification of COVID-19 Computed Tomography Scans.","authors":"Oznur Ozaltin, Ozgur Yeniay, Abdulhamit Subasi","doi":"10.1089/big.2022.0042","DOIUrl":"10.1089/big.2022.0042","url":null,"abstract":"<p><p>Coronavirus disease 2019 (COVID-19) is spreading rapidly around the world. Therefore, the classification of computed tomography (CT) scans alleviates the workload of experts, whose workload increased considerably during the pandemic. Convolutional neural network (CNN) architectures are successful for the classification of medical images. In this study, we have developed a new deep CNN architecture called OzNet. Moreover, we have compared it with pretrained architectures namely AlexNet, DenseNet201, GoogleNet, NASNetMobile, ResNet-50, SqueezeNet, and VGG-16. In addition, we have compared the classification success of three preprocessing methods with raw CT scans. We have not only classified the raw CT scans, but also have performed the classification with three different preprocessing methods, which are discrete wavelet transform (DWT), intensity adjustment, and gray to color red, green, blue image conversion on the data sets. Furthermore, it is known that the architecture's performance increases with the use of DWT preprocessing method rather than using the raw data set. The results are extremely promising with the CNN algorithms using the COVID-19 CT scans processed with the DWT. The proposed DWT-OzNet has achieved a high classification performance of more than 98.8% for each calculated metric.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"420-436"},"PeriodicalIF":4.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9129822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ODQN-Net: Optimized Deep Q Neural Networks for Disease Prediction Through Tongue Image Analysis Using Remora Optimization Algorithm.","authors":"S V N Sreenivasu, P Santosh Kumar Patra, Vasujadevi Midasala, G S N Murthy, Krishna Chaitanya Janapati, J N V R Swarup Kumar, Pala Mahesh Kumar","doi":"10.1089/big.2023.0014","DOIUrl":"10.1089/big.2023.0014","url":null,"abstract":"<p><p>Tongue analysis plays the major role in disease type prediction and classification according to Indian ayurvedic medicine. Traditionally, there is a manual inspection of tongue image by the expert ayurvedic doctor to identify or predict the disease. However, this is time-consuming and even imprecise. Due to the advancements in recent machine learning models, several researchers addressed the disease prediction from tongue image analysis. However, they have failed to provide enough accuracy. In addition, multiclass disease classification with enhanced accuracy is still a challenging task. Therefore, this article focuses on the development of optimized deep q-neural network (DQNN) for disease identification and classification from tongue images, hereafter referred as ODQN-Net. Initially, the multiscale retinex approach is introduced for enhancing the quality of tongue images, which also acts as a noise removal technique. In addition, a local ternary pattern is used to extract the disease-specific and disease-dependent features based on color analysis. Then, the best features are extracted from the available features set using the natural inspired Remora optimization algorithm with reduced computational time. Finally, the DQNN model is used to classify the type of diseases from these pretrained features. The obtained simulation performance on tongue imaging data set proved that the proposed ODQN-Net resulted in superior performance compared with state-of-the-art approaches with 99.17% of accuracy and 99.75% and 99.84% of F1-score and Mathew's correlation coefficient, respectively.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"452-465"},"PeriodicalIF":4.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10223867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sharing Medical Big Data While Preserving Patient Confidentiality in Innovative Medicines Initiative: A Summary and Case Report from BigData@Heart.","authors":"Megan Schröder, Sam H A Muller, Eleni Vradi, Johanna Mielke, Yvonne M F Lim, Fabrice Couvelard, Menno Mostert, Stefan Koudstaal, Marinus J C Eijkemans, Christoph Gerlinger","doi":"10.1089/big.2022.0178","DOIUrl":"10.1089/big.2022.0178","url":null,"abstract":"<p><p>Sharing individual patient data (IPD) is a simple concept but complex to achieve due to data privacy and data security concerns, underdeveloped guidelines, and legal barriers. Sharing IPD is additionally difficult in big data-driven collaborations such as Bigdata@Heart in the Innovative Medicines Initiative, due to competing interests between diverse consortium members. One project within BigData@Heart, case study 1, needed to pool data from seven heterogeneous data sets: five randomized controlled trials from three different industry partners, and two disease registries. Sharing IPD was not considered feasible due to legal requirements and the sensitive medical nature of these data. In addition, harmonizing the data sets for a federated data analysis was difficult due to capacity constraints and the heterogeneity of the data sets. An alternative option was to share summary statistics through contingency tables. Here it is demonstrated that this method along with anonymization methods to ensure patient anonymity had minimal loss of information. Although sharing IPD should continue to be encouraged and strived for, our approach achieved a good balance between data transparency while protecting patient privacy. It also allowed a successful collaboration between industry and academia.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"399-407"},"PeriodicalIF":4.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10733752/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"61566098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The incidence and prevalence of coeliac disease in the United Kingdom","authors":"Yvonne Nartey, C. Crooks, Joe West, Timothy R. Card, Laila J. Tata","doi":"10.1370/afm.22.s1.5051","DOIUrl":"https://doi.org/10.1370/afm.22.s1.5051","url":null,"abstract":"","PeriodicalId":51314,"journal":{"name":"Big Data","volume":"18 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139303896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning Analysis of Serious Illness Conversations Predicts Patient Reports of Feeling Heard & Understood","authors":"Bob Gramling, Donna Rizzo, Margaret Eppstein, Bradford Demarest","doi":"10.1370/afm.22.s1.5279","DOIUrl":"https://doi.org/10.1370/afm.22.s1.5279","url":null,"abstract":"","PeriodicalId":51314,"journal":{"name":"Big Data","volume":"284 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139291867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Changes in Reasons for Visits to Primary Care as a Result of the COVID-19 Pandemic: by INTRePID","authors":"Karen Tu, M. Lapadula","doi":"10.1370/afm.22.s1.5425","DOIUrl":"https://doi.org/10.1370/afm.22.s1.5425","url":null,"abstract":"","PeriodicalId":51314,"journal":{"name":"Big Data","volume":"11 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139301044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Breast cancer screening during the COVID-19 Pandemic in the United States: Results from real-world health records data","authors":"William Curry, Wen-Jan Tuan, Qiushi Chen, Andrew Chung","doi":"10.1370/afm.22.s1.4885","DOIUrl":"https://doi.org/10.1370/afm.22.s1.4885","url":null,"abstract":"","PeriodicalId":51314,"journal":{"name":"Big Data","volume":"48 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139292120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Method for Utilizing Electronic Health Record Data in Condition-specific Research","authors":"Tarin Clay, Melissa Filippi, Elise Robertson, Cory B. Lutgen, Elisabeth F. Callen","doi":"10.1370/afm.22.s1.4955","DOIUrl":"https://doi.org/10.1370/afm.22.s1.4955","url":null,"abstract":"","PeriodicalId":51314,"journal":{"name":"Big Data","volume":"12 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139294842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Harmonized Healthcare Database across Family Medicine Institutions","authors":"Chance R. Strenth, David Schneider, U. Sambamoorthi, Sravan Mattevada, Kimberly Fulda, Bhaskar Thakur, Anna Espinoza","doi":"10.1370/afm.22.s1.5404","DOIUrl":"https://doi.org/10.1370/afm.22.s1.5404","url":null,"abstract":"","PeriodicalId":51314,"journal":{"name":"Big Data","volume":"14 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139291188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}