(JAMA. 2020;323:2468–2469)
During a global pandemic, the responsibility of a physician shifts to prioritizing community health rather than maximizing the best interests of individual patients. As such, policies limiting patient visitors have been implemented in virtually all clinical settings in order to curb infectious exposures. Many labor and delivery units have instituted a policy limiting patients to one prescreened and afebrile adult visitor. At the beginning of the pandemic, many hospitals in the New York City Area were more stringent and many prohibited all visitors to delivery and postpartum units. However, the New York Department of Health issued guidelines and the governor of New York issued an executive order that reaffirmed the right of pregnant women to have an accompanying support person present during labor, delivery, and the immediate postpartum period.
Recent advancements in the area of drug discovery using artificial intelligence made it possible to speed up the hunt for new pharmaceuticals. Drugs like arbidol, atazanavir, remdesivir & favipiravir are under testing phase to cure COVID-19. In this paper, we present systematic study of AI based drug discovery techniques suitable for COVID-19 detection.
Over the past decade, there have been tremendous advances in the use of data science to facilitate decision making and extract and thus act upon insights from large data sets in digital business environments. However, despite these advances, there is still a lack of relevant evidence on actions to improve data science management in digital businesses. To fill this gap in the literature. The purpose of this study is to review (i) analytical methods, (ii) usage, and performance metrics based on (iii). Data science used in digital business techniques and strategies. To this end, a comprehensive literature search was carried out on the important scientific contributions made so far in this area of research. The results provide an overview of the most important applications of data science in digital business. Generate insights related to the creation of innovative data mining and knowledge discovery techniques. Important theoretical implications are discussed and a list of topics for further research in this area is provided. This report aims to develop recommendations for enhancing digital business strategies for enterprises, marketing, and non-technical researchers, and identify directions for further research on innovative data mining and discovery applications in science.
Recent advancements in the area of Optical Character Recognition (OCR) using deep learning techniques made it possible to use for real world applications with good accuracy. In this paper we present a system named as OCRXNet. OCRXNetv1, OCRXNetv2 and OCRXNetv3 are proposed and compared on different identity documents. Image processing methods and various text detectors have been used to identify best fitted process for custom ocr of identity documents. We also introduced the end to end pipeline to implement OCR for various use cases.
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