ABSTRACT
The healthcare industry and the battle against the COVID‐19 pandemic are two areas where blockchain technology might be useful. In this study, blockchain's significance is examined. Blockchain technology and related procedures will be used in future healthcare systems for collecting sensor data, automated patient monitoring, and safe data storage. Because it can store a large amount of data in a dispersed and secure way and provide access whenever and wherever it is needed, this technology greatly simplifies the process of carrying out activities. The advantages of quantum computing, such as the speed with which patients can be found and monitored, may be fully used with the help of quantum blockchain. Quantum blockchain is an additional resource that may be used to safeguard the veracity, integrity, and availability of stored information. Combining quantum computing with blockchain technology may allow faster and more secure medical information processing. In this research, the authors examine the potential uses of blockchain and quantum technology in the healthcare industry. Quantum technologies, blockchain‐based technologies, and other cutting‐edge ICTs (such as ratification intelligence, machine learning, drones, and so on) were investigated and contrasted in this article. [ FROM AUTHOR] Copyright of Security & Privacy is the property of John Wiley & Sons, Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
ABSTRACT
The government addresses that one of the biggest problems in the country is lacking an effective contact tracing solution. The Philippines' current contact tracing systems have encountered a lot of challenges because of the lack of features that would ensure safety and awareness to users around. The study aims to propose a system framework that can be used as Contact Tracing Solution using data warehousing and edge computing would improve the tracing in small and concentrated areas such as universities and offices. The researchers gather reviews and studies on how to develop a system that would address the current problem in the contact tracing scenario in the Philippines, particularly in the education field. The researcher will be going to apply the descriptive and development design for the conduct of the study and the waterfall methodology will be the software model for the development of the proposed system. Therefore, it is better to develop a contact tracing application that will be used by universities whose main objective is to spread awareness to potentially close contacts of a COVID-19 positive case and further implement the system framework to provide a proactive solution for contact tracing in the academe. © 2022 IEEE.
ABSTRACT
Coronaviruses have been around for years, they are a large family of viruses that can create a variety of anomaly in humans and even in animals, the first symptoms are summed up by a simple cold with fever but it can spread to very serious respiratory problems. This disease has caused a global crisis on all levels;it's a very big challenge that we have lived it since the Second World War. The challenging problem of COVID-19 data science is considered in this paper, where we propose a new data warhouse, that best meets the needs of scientists. The proposed data warhouse as of February 24, 2020, is based on heterogeneous data provided by Our World in Data GitHub and Kaggle database, which are collected daily from Our World in Data COVID-19. Furthermore, this data warehouse is used to feed dashboards in real time that helps the decision-makers to strengthening of the coronavirus screening network, track the spread of the virus before and after vaccination around the world to fight against this dangerous disease.
ABSTRACT
Background: The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has demonstrated the need to share data and biospecimens broadly to optimize clinical outcomes for US military Veterans. Methods: In response, the Veterans Health Administration established VA SHIELD (Science and Health Initiative to Combat Infectious and Emerging Life-threatening Diseases), a comprehensive biorepository of specimens and clinical data from affected Veterans to advance research and public health surveillance and to improve diagnostic and therapeutic capabilities. Results: VA SHIELD now comprises 12 sites collecting de-identified biospecimens from US Veterans affected by SARS-CoV-2. In addition, 2 biorepository sites, a data processing center, and a coordinating center have been established under the direction of the Veterans Affairs Office of Research and Development. Phase 1 of VA SHIELD comprises 34 157 samples. Of these, 83.8% had positive tests for SARS-CoV-2, with the remainder serving as contemporaneous controls. The samples include nasopharyngeal swabs (57.9%), plasma (27.9%), and sera (12.5%). The associated clinical and demographic information available permits the evaluation of biological data in the context of patient demographics, clinical experience and management, vaccinations, and comorbidities. Conclusions: VA SHIELD is representative of US national diversity with a significant potential to impact national healthcare. VA SHIELD will support future projects designed to better understand SARS-CoV-2 and other emergent healthcare crises. To the extent possible, VA SHIELD will facilitate the discovery of diagnostics and therapeutics intended to diminish COVID-19 morbidity and mortality and to reduce the impact of new emerging threats to the health of US Veterans and populations worldwide.
ABSTRACT
AIMS: We analysed the impact of the SARS-CoV-2 pandemic (COVID-19) on the quality of breast cancer care in certified EUSOMA (European Society of Breast Cancer Specialists) breast centres. MATERIALS AND METHODS: The results of the EUSOMA quality indicators were compared, based on pseudonymised individual records, for the periods 1 March 2020 till 30 June 2020 (first COVID-19 peak in most countries in Europe) and 1 March 2019 till 30 June 2019. In addition, a questionnaire was sent to the participating Centres for investigating the impact of the COVID-19 pandemic on the organisation and the quality of breast cancer care. RESULTS: Forty-five centres provided data and 31 (67%) responded to the questionnaire. The total number of new cases dropped by 19% and there was a small significant higher tumour (p = 0.003) and lymph node (p = 0.011) stage at presentation. Comparing quality indicators (12,736 patients) by multivariable analysis showed mostly non-significant differences. Surgery could be performed in a COVID-free zone in 94% of the centres, COVID testing was performed before surgery in 96% of the centres, and surgical case load was reduced in 55% of the centres. Modifications of the indications for neoadjuvant endocrine therapy, chemotherapy, and targeted therapy were necessary in 23%, 23%, and 10% of the centres; changes in indications for adjuvant endocrine, chemo-, targeted, immune, and radiotherapy in 3%, 19%, 3%, 6%, and 10%, respectively. CONCLUSION: Quality of breast cancer care was well maintained in EUSOMA breast centres during the first wave of the COVID-19 pandemic. A small but significantly higher tumour and lymph node stage at presentation was observed.
Subject(s)
Breast Neoplasms , COVID-19 , Humans , Female , Pandemics , SARS-CoV-2 , Breast Neoplasms/diagnostic imaging , Breast Neoplasms/therapy , Breast Neoplasms/pathology , COVID-19 TestingABSTRACT
Large numbers of job postings with complex content can be found on the Internet at present. Therefore, analysis through natural language processing and machine learning techniques plays an important role in the evaluation of job postings. In this study, we propose a novel data structure and a novel algorithm whose aims are effective storage and analysis in data warehouses of big and complex data such as job postings. State-of-the-art approaches in the literature, such as database queries, semantic networking, and clustering algorithms, were tested in this study to compare their results with those of the proposed approach using 100,000 Kariyer.net job postings in Turkish, which can be considered to have an agglutinative language with a grammatical structure differing from that of other languages. The algorithm proposed in this study also utilizes stream logic. Considering the growth potential of job postings, this study aimed to recommend new sub-qualifications to advertisers for new job postings through the analysis of similar postings stored in the system. Finally, complexity and accuracy analyses demonstrate that the proposed approach, using the Cluster Feature approach, can obtain state-of-the-art results on Turkish job posting texts.
ABSTRACT
PREDIMED, Clinical Data Warehouse of Grenoble Alps University Hospital, is currently participating in daily COVID-19 epidemic follow-up via spatial and chronological analysis of geographical maps. This monitoring is aimed for cluster detection and vulnerable population discovery. Our real-time geographical representations allow us to track the epidemic both inside and outside the hospital.
Subject(s)
COVID-19 , COVID-19/epidemiology , Data Warehousing , Geography , Hospitals, University , HumansABSTRACT
Clinical Data Warehouses (CDW) are gold mines and may be useful to manage the COVID-19 outbreak. This article details the use of CDW in order to retrieve patients for vaccination purposes. A list of 34 diseases (or conditions) was published by French Health Authorities to target individuals at a high risk of developing a severe form of COVID. Using a multilevel search engine, 23 queries were built based on structured or unstructured data using natural language processing features. The Diagnosis Related Group coding system was used alone in three queries (13.0%), coupled with unstructured data in four queries (17.4%), and unstructured data were used alone in 16 queries (69.6%). Eleven diseases (conditions) were too broad to be translated into queries. Finally, 6,006 unique re-identified patients were retrieved. This use case demonstrates the usefulness of the Rouen University Hospital CDW in retrieving patients for other purposes than translational research.