COVID-19 Prognosis and Mortality Risk Predictions from Symptoms: A Cloud-Based Smartphone Application

The coronavirus pandemic overwhelmed many countries and their healthcare systems. Shortage of testing kits and Intensive-Care-Unit (ICU) beds for critical patients have become a norm in most developing countries. This has prompted the need to rapidly identify the COVID-19 patients to stop the spread of the virus and also to find critical patients. The latter is imperative for determining the state of critically ill patients as quickly as possible. This will lower the number of deaths from the infection. In this paper, we propose a cloud-based smartphone application for the early prognosis of COVID-19 infected patients and also predict their mortality risk using their symptoms. Moreover, we heuristically identified the most important symptoms necessary for making such predictions. We have successfully reduced the number of features by almost half for the prognosis and by more than a third for forecasting the mortality risk, compared to the contemporary studies. The application makes the real-time analysis using machine learning models, designed and stored in the cloud. Our machine learning model demonstrates an accuracy, precision, recall, and F1 score of 97.72%, 100%, 95.55%, and 97.70%, respectively, in identifying the COVID-19 infected patients and with an accuracy, precision, recall, and F1 score of 90.83%, 88.47%, 92.94%, and 90.65%, respectively, in forecasting the mortality risk from the infection. The real-time cloud-based approach yields faster responses, which is critical in the time of pandemic for mitigating the infection spread and aiding in the efficient management of the limited ICU resources.

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34: Real-time analysis of data using machine learning model significantly improves prediction of unplanned cesarean deliveries

American Journal of Obstetrics and Gynecology ◽

10.1016/j.ajog.2019.11.050 ◽

2020 ◽

Vol 222 (1) ◽

pp. S29

Author(s):

Yishai Sompolinsky ◽

Joshua Guedalia ◽

Amihai Rottenstreich ◽

Michal Novoselsky Persky ◽

Gabriel levin ◽

...

Keyword(s):

Machine Learning ◽

Real Time ◽

Learning Model ◽

Time Analysis ◽

Real Time Analysis ◽

Cesarean Deliveries ◽

Machine Learning Model

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Real-Time Unspecified Major Sub-Events Detection in the Twitter Data Stream That Cause the Change in the Sentiment Score of the Targeted Event

International Journal of Information Technology and Web Engineering ◽

10.4018/ijitwe.2017100101 ◽

2017 ◽

Vol 12 (4) ◽

pp. 1-21 ◽

Cited By ~ 10

Author(s):

Ritesh Srivastava ◽

M.P.S. Bhatia

Keyword(s):

Real Time ◽

Real World ◽

Virtual Communities ◽

Change Points ◽

Time Analysis ◽

The Real ◽

Real Time Analysis ◽

Twitter Data ◽

Sentiment Score ◽

Events Detection

Twitter behaves as a social sensor of the world. The tweets provided by the Twitter Firehose reveal the properties of big data (i.e. volume, variety, and velocity). With millions of users on Twitter, the Twitter's virtual communities are now replicating the real-world communities. Consequently, the discussions of real world events are also very often on Twitter. This work has performed the real-time analysis of the tweets related to a targeted event (e.g. election) to identify those potential sub-events that occurred in the real world, discussed over Twitter and cause the significant change in the aggregated sentiment score of the targeted event with time. Such type of analysis can enrich the real-time decision-making ability of the event bearer. The proposed approach utilizes a three-step process: (1) Real-time sentiment analysis of tweets (2) Application of Bayesian Change Points Detection to determine the sentiment change points (3) Major sub-events detection that have influenced the sentiment of targeted event. This work has experimented on Twitter data of Delhi Election 2015.

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Significance of Parallel Computing on the Performance of Digital Image Correlation Algorithms in MATLAB

Designs ◽

10.3390/designs5010015 ◽

2021 ◽

Vol 5 (1) ◽

pp. 15

Author(s):

Andreas Thoma ◽

Abhijith Moni ◽

Sridhar Ravi

Keyword(s):

Digital Image Correlation ◽

Real Time ◽

Digital Image ◽

Correct Choice ◽

Image Correlation ◽

Particle Swarm Algorithm ◽

Processing Unit ◽

Time Analysis ◽

The Real ◽

Real Time Analysis

Digital Image Correlation (DIC) is a powerful tool used to evaluate displacements and deformations in a non-intrusive manner. By comparing two images, one from the undeformed reference states of the sample and the other from the deformed target state, the relative displacement between the two states is determined. DIC is well-known and often used for post-processing analysis of in-plane displacements and deformation of the specimen. Increasing the analysis speed to enable real-time DIC analysis will be beneficial and expand the scope of this method. Here we tested several combinations of the most common DIC methods in combination with different parallelization approaches in MATLAB and evaluated their performance to determine whether the real-time analysis is possible with these methods. The effects of computing with different hardware settings were also analyzed and discussed. We found that implementation problems can reduce the efficiency of a theoretically superior algorithm, such that it becomes practically slower than a sub-optimal algorithm. The Newton–Raphson algorithm in combination with a modified particle swarm algorithm in parallel image computation was found to be most effective. This is contrary to theory, suggesting that the inverse-compositional Gauss–Newton algorithm is superior. As expected, the brute force search algorithm is the least efficient method. We also found that the correct choice of parallelization tasks is critical in attaining improvements in computing speed. A poorly chosen parallelization approach with high parallel overhead leads to inferior performance. Finally, irrespective of the computing mode, the correct choice of combinations of integer-pixel and sub-pixel search algorithms is critical for efficient analysis. The real-time analysis using DIC will be difficult on computers with standard computing capabilities, even if parallelization is implemented, so the suggested solution would be to use graphics processing unit (GPU) acceleration.

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