ABSTRACT
Finding Semantic similarity in text is a vital concept in the fields of information mining, text-based profiling. There have been many approaches to improve information retrieval by mining the semantics of the text. With the pandemic situation prevailing all over the world, we come across many useful posts about the COVID infection that is being tweeted by medical practitioners and people in the health care sector. While we come across such tweets, we also have tweets related to the vaccines, medical facilities, change in economic conditions due to pandemic, etc. But there is no methodology to efficiently study the tweet data and retrieve useful information out of them. Also, we need to utilize the geographical information that comes with each tweet. Though there have been many studies conducted on sentiment analysis, statistical analysis related to twitter data, there has not been much research on finding out the geographical distribution of COVID related tweets combined with query-based textual similarity of COVID related tweets. In this paper, we try to study the semantics of geo-Tagged twitter data related to COVID and segregate the tweets based on their geographical location and according to the content of tweets. We use an improved version of Density-Based Spatial Clustering for clustering the tweets according to geo-spatial information. Then, we apply cosine similarity techniques to do the textural clustering and evaluate the performance of proposed model. The proposed model is able to cluster tweets using the spatial coordinates and classify the tweets based on the textual similarity measure. © 2022 IEEE.
ABSTRACT
We investigatd the influence of do-not-resuscitate (DNR) status on mortality of hospital inpatients who died of COVID-19. This is a retrospective, observational cohort study of all patients admitted to two New Jersey hospitals between March 15 and May 15, 2020, who had, or developed, COVID-19 (1270 patients). Of these, 640 patients died (570 [89.1%] with and 70 [10.9%] without a DNR order at the time of admission) and 630 survived (180 [28.6%] with and 450 [71.4%] without a DNR order when admitted). Among the 120 patients without COVID-19 who died during this interval, 110 (91.7%) had a DNR order when admitted. Deceased positive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) patients were significantly more likely to have a DNR order on admission compared with recovered positive SARS-CoV-2 patients (P < 0.05), similar to those who tested negative for SARS-CoV-2. COVID-19 DNR patients had a higher mortality compared with COVID-19 non-DNR patients (log rank P < 0.001). DNR patients had a significantly increased hazard ratio of dying (HR 2.2 [1.5-3.2], P < 0.001) compared with non-DNR patients, a finding that remained significant in the multivariate model. The risk of death from COVID-19 was significantly influenced by the patients' DNR status.