scholarly journals Understanding Demographic Risk Factors for Adverse Outcomes in COVID-19 Patients: Explanation of a Deep Learning Model

2021 ◽  
Author(s):  
Yijun Shao ◽  
Ali Ahmed ◽  
Angelike P. Liappis ◽  
Charles Faselis ◽  
Stuart J. Nelson ◽  
...  
Keyword(s):  
Risk Factors ◽  
Deep Learning ◽  
Learning Model ◽  
Adverse Outcomes ◽  
Demographic Risk ◽  
Demographic Risk Factors ◽  
Deep Learning Model

scholarly journals COVID-19 severity: Studying the clinical and demographic risk factors for adverse outcomes

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255999
Author(s):  
Naila Shoaib ◽  
Naila Noureen ◽  
Rimsha Munir ◽  
Farhad Ali Shah ◽  
Noshaba Ishtiaq ◽  
...  
Keyword(s):  
Risk Factors ◽  
Strong Association ◽  
Adverse Outcomes ◽  
Vulnerable Groups ◽  
Nasopharyngeal Swab ◽  
Rt Pcr ◽  
Cross Sectional ◽  
Severity Of The Disease ◽  
Demographic Risk ◽  
Demographic Risk Factors

Background The primary goal of the presented cross-sectional observational study was to determine the clinical and demographic risk factors for adverse coronavirus disease 2019 (COVID-19) outcomes in the Pakistani population. Methods We examined the individuals (n = 6331) that consulted two private diagnostic centers in Lahore, Pakistan, for COVID-19 testing between May 1, 2020, and November 30, 2020. The attending nurse collected clinical and demographic information. A confirmed case of COVID-19 was defined as having a positive result through real-time reverse transcriptase polymerase chain reaction (RT-PCR) assay of nasopharyngeal swab specimens. Results RT-PCR testing was positive in 1094 cases. Out of which, 5.2% had severe, and 20.8% had mild symptoms. We observed a strong association of COVID-19 severity with the number and type of comorbidities. The severity of the disease intensified as the number of comorbidities increased. The most vulnerable groups for the poor outcome are patients with diabetes and hypertension. Increasing age was also associated with PCR positivity and the severity of the disease. Conclusions Most cases of COVID-19 included in this study developed mild symptoms or were asymptomatic. Risk factors for adverse outcomes included older age and the simultaneous presence of comorbidities.

Deep Learning Model to Quantify Left Atrium Volume on Routine Non-Contrast Chest Ct and Predict Adverse Outcomes

2021 ◽  
Author(s):  
Gilberto J. Aquino ◽  
Jordan Chamberlin ◽  
Megan Mercer ◽  
Madison Kocher ◽  
Ismail Kabakus ◽  
...  
Keyword(s):  
Deep Learning ◽  
Left Atrium ◽  
Learning Model ◽  
Chest Ct ◽  
Adverse Outcomes ◽  
Left Atrium Volume ◽  
Deep Learning Model

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

Sign in / Sign up

Export Citation Format

Share Document